
==== Front
Cancer Immunol Immunother
Cancer Immunol Immunother
Cancer Immunology, Immunotherapy : CII
0340-7004
1432-0851
Springer Berlin Heidelberg Berlin/Heidelberg

39235522
3807
10.1007/s00262-024-03807-1
Research
Immunogenomic features of radiologically distinctive nodules in multiple primary lung cancer
Chen Mei-Cheng 1
Yang Hao-Shuai 2
Dong Zhi 1
Li Lu-Jie 1
Li Xiang-Min 3
Luo Hong-He 4
Li Qiong liqiong@sysucc.org.cn

5
Zhu Ying zhuy45@mail.sysu.edu.cn

1
1 grid.412615.5 0000 0004 1803 6239 Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080 Province Guangdong People’s Republic of China
2 https://ror.org/037cjxp13 grid.415954.8 0000 0004 1771 3349 Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, 100029 China
3 grid.12981.33 0000 0001 2360 039X Department of Radiology, Hui Ya Hospital of The First Affiliated Hospital, Sun Yat-Sen University, Huizhou, 516080 Guangdong People’s Republic of China
4 grid.12981.33 0000 0001 2360 039X Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong People’s Republic of China
5 grid.12981.33 0000 0001 2360 039X Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center/Cancer Hospital, Guangzhou, 510080 Province Guangdong People’s Republic of China
5 9 2024
5 9 2024
11 2024
73 11 2178 7 2024
10 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objectives

To provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC).

Methods

The Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC.

Results

In MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR).

Conclusions

Our findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-024-03807-1.

Keywords

Ground-glass opacity
Multiple primary lung cancer
Immunogenomic
Radiology
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82001882 82102109 Dong Zhi Zhu Ying issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Lung cancer is the deadliest malignancy in the world [1]. Multiple primary lung cancer (MPLC) refers to patients who have two or more primary lung cancers simultaneously or consecutively [2–4]. With the popularization of low-dose CT screening and the increasing of population aging, the incidence of MPLC is gradually increasing [5–7]. According to the consolidation/tumor ratio (CTR) [8] from the lung window under thin-section CT, pulmonary nodules of MPLC can be artificially divided into solid nodules (SN), mixed-GGO nodules (mGGN), and pure-GGO nodules (pGGN) [9, 10].

Studies have shown that different radiological classifications seem to correspond to different degree of invasive growth of MPLC [11, 12]. There were evidences that MPLC patients with GGO lesions presented a more favorable prognosis after surgery, with 97.2% 5-year overall survival after surgically resection being shown [13]. The results suggested that pGGN may be more biologically indolent compared to mGGN or SN in MPLC. Nevertheless, the biology underlying these radiological features and clinical outcomes of MPLC needs further exploration.

Recent studies have investigated the molecular features of pulmonary nodules with radiologically distinct GGO components or without GGO components in patients with SPLC [14, 15]. Since the clinical prognosis and management had incredible difference in MPLC compared to SPLC [16], further research is needed to understand more clearly the characteristics of MPLC for appropriate treatment design. Previous studies have shown that in MPLC, solid-predominant nodules seemed to have a higher response rate than GGO-predominant nodules to immune therapy [17, 18]. However, the molecular and immune characteristics in MPLC with different GGO components were still poorly understood [19]. This study aimed to explore the differences between pGGN and mGGN, to give a comprehensive description about molecular and immune features in nodules with radiologically distinct GGO components in early-stage MPLC. Additionally, the study aimed to provide a molecular foundation for the immunotherapy of MPLC by comparing the difference in the immune microenvironment between MPLC and SPLC.

Method

The patients cohort and its associated radiological assessment

After providing written informed consent, this study enrolled the patients > 18 years of age who had undergone radical resection of at least two primary lung cancer lesions and were identified as MPLC as guided by the Martini-Melamed criteria(2) from January 2020 to December 2022 in the First Affiliated Hospital of Sun Yat-sen University (SYSUFAH). The criteria for selection were as follows: (i) every neoplastic lesion represented a primary lung cancer; (ii) the histopathological analysis indicated that there existed a minimum of two pulmonary malignancies, all of adenocarcinomatous nature; (iii) the carcinomatous lesions were distributed across distinct pulmonary lobes, or with disparate histological subtypes within each lobe, devoid of any lymphatic metastasis, signifying a multicentric origin of the tumors within the same individual; and (iv) comprehensive pathological specimens were available.

Employing previously established methodologies, we subsequently conducted radiological assessments using enhanced chest computed tomography scans images. The maximal tumor diameter was quantified on the lung window (the window width was 1500 HU and window level was − 800 HU, respectively). Based on the consolidation/tumor ratio (CTR, representing the ratio of the maximum axial diameter of the solid component to the maximum diameter of the entire tumor as discerned on CT scans), radiological assessments yielded three subtypes, namely, pGGN, mGGN, and SN.

Subsequently, this study encompassed a comprehensive collection of pGGN-mGGN paired specimens derived from a total of 10 individuals (comprising 21 primary lung cancer lesions, including 11 pGGN and 10 mGGN) for Bulk RNA sequencing (Table S1). This study was reviewed and approved by the Ethics Committee of the SYSUFAH in strict accordance with the ethical principles of the Declaration of Helsinki.

Gene expression omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database was one of the largest public databases, containing gene data from various platforms. We downloaded the transcription data, the clinical information and the CT images of patients with SPLCs from GEO database (GSE103584). After a 1:4 matching of SPLCs and MPLCs based on age and sex of patients, 40 cases of SPLC were included in the analysis. The single-cell RNA sequencing data of MPLC was from GSE200972, which includes 11 tumors and eight adjacent tissues from four patients with MPLC. After excluding squamous cell carcinoma tissue, six multiple primary tumor tissue samples and four normal tissue samples from two patients were included in the study. The related clinical data was obtained by contacting the authors. Furthermore, we explored single-cell data from a patient with MPLC (included a SN, a pGGN, and a mGGN) who received pembrolizumab treatment, sourced from GSE146100.

RNA extraction and sequencing

Total RNA was meticulously extracted from fresh tissue specimens, followed by a thorough RNA analysis using the Nanodrop2000 spectrophotometer by Thermo Fisher Scientific. The Agilent 2100 Bioanalyzer System, crafted by Agilent Technologies, was employed to further evaluate RNA integrity. Subsequent to these preparatory steps, sample labeling and array hybridization were conducted in accordance with the exacting guidelines delineated in the Agilent monochrome microarray gene expression analysis protocol (Agilent Technologies). Thereafter, 100 μL of the hybridization solution was dispensed onto spacer slides, meticulously assembled into gene expression microarray slides, and uploaded into comprehensive whole human genome expression microarrays (China National Microbiology Data Center ID, NMDC10018429).

Differential expression and pathway enrichment analysis

We performed differential expression analysis between pGGN and mGGN in MPLCs and between MPLCs and SPLCs, respectively. Differentially expressed genes (DEGs) were discerned through stringent criteria: a false discovery rate threshold below 0.05 and a |log2 FC (fold-change)| exceeding 1.5. Subsequent enrichment analysis including gene ontology (GO) enrichment analyses were performed to unravel the biological underpinnings associated with these DEGs. These analyses were carried out using R packages ‘edgeR’, ‘pheatmap’, ‘clusterProfler’, ‘org.Hs.eg.db’, ‘enrichplot’, and ‘ggplot2’, and were deemed significant at a P-value threshold below 0.05.

Weighted correlation network analysis (WGCNA) and protein-protein interaction (PPI) network construction

The process of WGCNA was conducted using the R package “WGCNA” to construct gene co-expression networks within the MPLC dataset. With a soft threshold of eight, different gene modules were obtained (configuring parameters as deepSplit = 2, minModuleSize = 25), and the correlation between these modules and clinical features were calculated. Import the core gene module into STRING (https://string-db.org/) and construct the PPI network. Using cytoscape (v3.8.2) software and the cytohubba plugin to discover the hub genes.

Tumor immune microenvironment investigation

CIBERSORT (cell type identification by estimating relative subsets of RNA transcripts) and MCPcounter (microenvironment cell populations-counter) algorithm were harnessed to estimate the levels of infiltration by distinct immune cell types in a sample. We meticulously computed, compared, and visually shown the infiltration levels of these 22 immune cell types between patients with mGGN and pGGN. Moreover, the ESTIMATE algorithm was deployed to compute matrix, immune, and ESTIMATE scores for each sample. These scores aptly reflect the content of stromal cells in the tumor, the content of immune cells within the tumor, and the purity of tumor cells. These analyses were conducted using the run code thoughtfully provided by the developer, and the R packages “IOBR” [20], ‘limma’ and ‘ggpubr’.

Immune cell annotation of MPLC single-cell RNA sequencing data

After cleaning and quality control of the raw data, further analysis was performed using the R package “Seurat”. The FindClusters function was used to identify functional characteristics of various cell clusters, with a resolution of 0.8 and using PC = 20 for cell clustering based on the expression profiles of each cell cluster. Subsequently, the FindMarkers function was employed to identify marker genes for each cluster. Immune cells were selected for further research based on the expression of these marker genes. Additionally, the cell types of each immune cell subpopulation were annotated according to human lung cell and immune cell marker genes established in previous studies.

Cell communication and transcription factor analysis

Cell communication analysis was performed using the R package “CellChat”, with the following specific methods. A CellChat object was created based on the Seurat object data mentioned earlier. After defining cell groups, the human receptor database CellChatDB.human was imported. Following data preprocessing, high-expressing receptors within each subpopulation are identified, and overexpressed receptor interactions were recognized, allowing for the inference of cell–cell interactions. Transcription factor analysis was carried out using the pySCENIC software, based on single-cell gene expression matrices and the expression of transcription factors (TFs). Co-expression modules between TFs and candidate target genes were constructed. The RcisTarget software was used to identify modules with significantly enriched regulatory factor motifs in target genes and create regulons containing TFs and directly targeted genes. Activity scores for each regulon in each cell were evaluated using the AUCell software.

Statistical analysis

All statistical analyses were conducted using R software (version 4.1.2, sourced from https://www.r-project.org). Depending on the normality distribution of the metric data, values were expressed as either mean ± standard deviation or median with interquartile range (IQR) for continuous variables, and as frequencies (percentages) for categorical data. Wilcoxon rank-sum test was chosen to compare differences between the two groups. All tests were two-sided, with a significance level of P < 0.05.

Result

Delineation of differentially expressed genes and enrichment analysis

After preprocessing and normalization, the dataset with 18,345 genes was further processed. Differential expression analysis between pGGN and mGGN in MPLC (p < 0.05, |log2 FC|> 1.5) yielded a set of 188 Differentially Expressed Genes (DEGs), which included 81 downregulated genes and 107 upregulated genes (Fig. 1A), with elucidating the top five upregulated and downregulated DEGs. A heatmap displaying the upregulated and downregulated genes was depicted (Fig. 1B). Base on the identification of these DEGs, the exploration of the distinct molecular mechanisms that differentiate these two groups was performed via the application of Hallmark gene set and GO pathway enrichment analyses. Enrichment analyses of Hallmark gene set revealed that the upregulated DEGs were abundantly enriched in the epithelial-mesenchymal transition, hypoxia and TNFA signaling via NFKB pathways (Fig. 1C). GO analysis unveiled significant enrichments in biological processes associated with extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization (Fig. 1D, E).Fig. 1 Integrated analyses of gene expression and pathway enrichment in MPLC. A volcano map of differentially expressed genes (DEGs) between pGGN and mGGN in MPLC. The plot displays the log2 fold change on the x-axis and −log10 adjusted p-value on the y-axis. Genes with a false discovery rate threshold below 0.05 and a |log2 FC| exceeding 1.5 were highlighted in red (upregulated) and cyan (downregulated), B heatmap of DEGs between pGGN and mGGN samples. Each row represents a gene, and each column represents a sample. The color gradient indicates the expression levels, with red representing high expression and blue representing low expression, C dot plot depicting the results of HALLMARK gene set enrichment analysis of the DEGs (the upregulated and downregulated DEGs were analyzed separately), D and E bar plot and dot plot of GO analyses of the DEGs

Clinically significant module identification via WGCNA and identification of hub genes

To screen immune-related gene modules, variant immune genes (2484 from the immunology database and analysis portal, https://www.immport.org/) were selected for WGCNA studies on the expression profile to fabricate gene co-expression networks in MPLC. Pearson’s correlations were conducted for all gene pairs, with WGCNA employed to establish a weighted co-expression network (Fig. 2A). Subsequently, we determined eight as the optimal soft threshold for adjacency calculations when the scale-free topology fit index reached 0.9. In this investigation, the co-expression network exhibited conformity with the characteristics of a scale-free network. (Fig. 2B). Subsequently, the expression matrix underwent a transformation into an adjacency matrix, followed by the transformation of this adjacency matrix into a topology matrix. Employing the topological overlap matrix (TOM) as our foundation, we applied the average-linkage clustering methodology to gene clustering, establishing a requirement of at least 25 genes within each module, adhering to the criteria of the hybrid dynamic clipped tree standard. A module analysis was executed, amalgamating modules with closer proximities into a novel module, resulting in the acquisition of a total of 21 modules (Fig. 2C). Further scrutiny encompassed the examination of correlations between each module and the value of CTR and the mGGN group in MPLC (Fig. 2D). The outcomes revealed that the cyan module exhibited the most pronounced association with the CTR and the mGGN group.Fig. 2 Comprehensive analysis of network modules and functional associations based on expression profiles of immune-related genes in MPLC. A clustering dendrogram of samples, B determination of the soft-thresholding power (β = 8) to ensure that the constructed network is scale-free, C clustering dendrogram of genes showing the grouping of immune-related genes into distinct modules, D. module-trait associations: heatmap displaying the correlation between identified modules and clinical traits (CTR and mGGN groups). Each cell contains the correlation coefficient and the corresponding p-value. E.PPI network of genes in cyan module. F.PPI network of hub genes (confidence > 0.4 and FDR < 0.05, using the MCC method), G, H GO functional enrichment analysis in the top 10 hub genes of cyan module

The protein–protein interaction (PPI) network was assembled utilizing 62 genes in cyan modules through the STRING database (Fig. 2E). Applying a stringent criteria (confidence > 0.4 and FDR < 0.05), we employed the CytoHubba plugin within Cytoscape to assign and rank each gene using topological network algorithms, thereby unveiling the top ten hub genes. The MCC method yielded the following ten key genes: LCK, ZAP70, CD247, CXCR3, IL2RB, CD3D, CD3G, IL2RG, GRAP2, and FASLG. (Fig. 2F) illustrates the protein–protein interaction (PPI) connections among these pivotal genes. Gene ontology (GO) analysis revealed that these genes were significant cytokines that predominantly function in the T cell selection (Fig. 2G, H). This indicated that T cells may play a significant role in the development of MPLC.

Estimation of tumor immune microenvironment in MPLC

We compared the differences in tumor-infiltrating immune cells, fibroblasts, and epithelial cell abundance between the mGGN and pGGN groups in MPLCs (assessed using the MCPcounter algorithm). Compared to pGGN, mGGN demonstrated significantly elevated degree of infiltration of T cell, CD8+ T cell and B lineage (p < 0.05) (Fig. 3A). The CIBERSORT algorithm computed the proportions of 22 different immune cell types for each sample. Among these, M2 macrophages, CD8+ T cells, and resting memory CD4+ T cells were identified as the predominant immune cell populations within MPLC (Fig. 3B). Furthermore, the immunescore was markedly higher among mGGN compared to pGGN (p < 0.05) (Fig. 3C). These results indicate a more robust immune cell presence in mGGN compared to pGGN. We proceeded to investigate the correlation between the T cell, CD8+ T cell and B lineage cell proportions calculated by MCPcounter and the radiological parameter CTR. Our observations revealed a positive correlation between them, with R2 values of 0.0381, 0.127 and 0.119, respectively, emphasizing the positive association between the increase in solid components and the elevation in immune cells infiltration. (Fig. 3D). Besides, CD8+ T cells may play a more significant role during the variation of CTR, warranting further research and attention.Fig. 3 Comparative analysis of immune infiltration between the mGGN and pGGN in MPLC. A comparison of differences in tumor-infiltrating immune cells, fibroblasts, and epithelial cell abundance between the mGGN and pGGN groups in MPLCs (assessed using the MCPcounter algorithm), B calculation of the proportions of 22 immune cell types in MPLCs using the CIBERSORT algorithm, C comparison of differences in stromal score, immunescore, and tumor purity between the mGGN and pGGN groups (evaluated with the ESTIMATE algorithm). D. exploration of the correlation between the proportions of T cell, CD8+ T cell and B lineage cell (calculated by the MCPcounter algorithm) and CTR. Note: * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, and **** indicates p < 0.0001

Comparison of immune infiltration between MPLC and SPLC for both mGGN and pGGN

To further explore the differences in the immune microenvironment between MPLC and SPLC with different solid components, we conducted a 1:4 matching of SPLC and MPLC based on age and sex of patients. This process resulted in the selection of 40 cases of SPLC. There were no statistically significant differences in sex or age between the two groups (p < 0.05, Table S2). Subsequently, we assessed the CTR in selected SPLCs from the CT data in the database and categorized them into two groups: the SPLC-pGGN group, comprising 18 cases, and the SPLC-mGGN group, consisting of 22 cases.

Compared to SPLC-pGGN, MPLC-pGGN demonstrated significantly elevated degree of infiltration of tumor-infiltrating immune cells, fibroblasts, and epithelial cells (p < 0.05) (Fig. 4A, B, C). Compared to SPLC-mGGN, MPLC-mGGN demonstrated significantly elevated degree of infiltration of naive B cells, CD8+ T cells, and M2 macrophages (p < 0.05) (Fig. 4D, E, F). Besides, the infiltration of tumor-infiltrating immune cells, fibroblasts, epithelial cells and immunescore were higher in MPLC-mGGN than in SPLC-mGGN (p < 0.05). Our observations revealed heightened immune infiltration in MPLCs compared to SPLCs for both mGGN and pGGN. Combined with these results, CD8+ T cells, which showed the strongest association with CTR, exhibited significant differences in the comparisons between pGGN and mGGN in MPLC, as well as in the comparisons between MPLC and SPLC for both pGGN and mGGN. Therefore, we further analyzed the specific role of CD8+ T cells in MPLC through single-cell transcriptome analysis.Fig. 4 Comparison of immune infiltration between MPLC and SPLC for both mGGN and pGGN. A, B, C comparison of differences in tumor immune infiltration between the pGGN nodules in MPLC and SPLC (assessed using the CIBERSORT algorithm, the MCPcounter algorithm and the ESTIMATE algorithm). D, E, F comparison of differences in tumor immune infiltration between the mGGN nodules in MPLC and SPLC (assessed using the CIBERSORT algorithm, the MCPcounter algorithm and the ESTIMATE algorithm). Note * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, and **** indicates p < 0.0001.

Clustering and annotation of single-cell transcriptomes

After downloading and cleaning the single-cell data, 86,090 cells were obtained. Cell types were identified based on the cell expression profiles. Immune cells were extracted for further analysis, and 40,442 immune cells were retained. Subsequently, a subclustering analysis was performed based on the expression of characteristic genes of immune cells, identifying a total of 13 immune cell subtypes. The dimensionality reduction and clustering results were shown in Fig. 5A, and the expression of marker genes was shown in Fig. 5B.Fig. 5 Single-cell RNA sequencing analysis reveals immune cell dynamics in MPLC. Analysis of scRNA‐seq data of MPLC samples. A uniform manifold approximation and projection (UMAP) plot of 40,442 cells colored by 13 immune cell clusters. Each color represents a different immune cell type, providing a visual representation of the cellular diversity and clustering within the tumor microenvironment, B the expression of marker genes in different cell types, C ligand–receptor interactions of immune cells in MPLC. (D) Intercellular interaction analysis focusing on CD8+ T cells and B cells in MPLC, E transcription factor analysis of CD8+ T cell in MPLC, F volcano plot showing DEGs between CD8+ T cells from pGGN and mGGN in MPLC, highlighting significant upregulated and downregulated genes, G bar plot illustrating the results of GO analyses, including biological processes, cellular components, and molecular functions, H (Up) UMAP plot visualizing the distribution of CD8+ T cells in pGGN and mGGN (from GSE146100). (Down) Bar chart showing the frequency of CD8+ T cells in pGGN and mGGN, (from GSE146100)

Cell communication and transcription factor analysis

Cell-to-cell information exchange was based on complex reactions between ligands and their receptors, as well as the activation of specific cellular signaling pathways. In order to further explore the role of CD8+ T cell in MPLC, we conducted cell communication analysis. The results of cell communication analysis shown that 13 cell types obtained through single-cell sequencing analysis have close interactions with each other. In the inference of signaling pathways, we found receptor relationships related to CD74, such as CD74-MIF, CD74-COPA, CD74-APP, play an important role in the cell–cell communications, and HLA-C and HLA-E are crucial in the communication between CD8+ T cell and other cell types (Fig. 5C). CD8+ T cell and B cells both showed remarkable interactions with dendritic cells and monocytes in MPLD (Fig. 5D). Transcription factor analysis revealed that CD8+ T cell had a unique regulatory network, with MAFG, RUNX3, TBX21, EOMES, NFATC2, and others potentially controlling the behavior of CD8+ T cells in MPLC (Fig. 5E). However, the functions of the genes controlled by each transcription factor still require further investigation.

To further investigate the roles of CD8+ T cells in pGGN and mGGN in MPLC, we performed a differential expression analysis (p < 0.05, |log2 FC|> 0.3) to compare CD8+ T cells between mGGN and pGGN (Fig. 5F), followed by enrichment analysis (Fig. 5G). The enrichment results revealed that genes upregulated in CD8+ T cells from mGGN, compared to those from pGGN, play significant roles in immune system processes, positive regulation of transcription, and signal transduction. This suggests that CD8+ T cells in mGGN may be more actively involved in immune responses and cellular signaling pathways, contributing to their enhanced functionality and activity. Additionally, we conducted single-cell analysis on a case of MPLC patient treated with immunotherapy (from GSE146100). The results showed a lower frequency of CD8+ T cells in pGGN compared to mGGN, consistent with our earlier observations (Fig. 5H). Collectively, these findings indicate that CD8+ T cells are more abundant and active in mGGN in MPLC, which may explain the biological inertness of pGGN.

Discussion

In this study, we investigated the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The bulk transcriptome analysis demonstrated that mGGN demonstrated a significantly elevated infiltration of CD8+ T cell. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. This study, for the first time, explored the immune microenvironment distinctions between mGGN and pGGN in MPLC, as well as between MPLC and SPLC for both mGGN and pGGN, providing a foundation for understanding the developmental mechanisms of MPLC, and offering valuable insights for guiding clinical treatment decisions for MPLC.

Our study unveiled significant gene expression differences between pGGN and mGGN in MPLCs. The enrichment results highlighted that these DEGs were predominantly enriched in epithelial-mesenchymal transition and hypoxia pathways, which were towards malignant biological behavior, such as promoting cellular plasticity and induce proliferation, metastasis, and drugs and radiotherapy resistance of cancer cells [21, 22]. Harmonious with our findings, the increasing of solid constituents was reported to be related to a more aggressive pulmonary malignancies [2]. GO analysis unveiled significant enrichments in biological processes associated with extracellular matrix organization. It was reported that extracellular matrix component signaling in cancers not only governs cellular adhesion, cytoskeletal arrangement, and movement but also imparts cues for cancer cell survival and proliferation [23]. This suggests that disparities in the developmental processes between pGGN and mGGN may be driven by extracellular matrix-related pathways. Moreover, the molecular pathways linked to these DEGs may offer insight into the progressive transformation of nodules from precancerous states to invasive MPLC.

The neoadjuvant immunotherapy had played important role in MPLCs, and genomic alterations and the immune microenvironment had great impact on MPLCs treatment response [17, 24, and 25]. Consequently, we employed WGCNA to elucidate immune gene modules associated with CTR and mGGN groups, offering deeper insights into the molecular alterations transpiring during the onset of mGGN. This investigation successfully identified a singular gene module significantly correlated with both CTR and mGGN groups, which was subsequently utilized in the construction of a protein–protein interaction (PPI) network. GO enrichment analysis underscored the centrality of the hub genes in T cell selection, signifying their pivotal role in regulating the immune infiltration within the tumor microenvironment (TME). Therefore, we conducted a comprehensive analysis of the tumor immune microenvironment between pGGN and mGGN in MPLCs. Notably, MPLCs exhibited a greater abundance of M2 macrophages, which have been reported in previous studies to possess immunosuppressive functions and contribute to tumor progression [26, 27]. Furthermore, mGGN featured a higher number of immune cells, including T cell, CD8+ T cell and B lineage, and had higher immunescore compared to pGGN in MPLCs. Previous research has identified high levels of CD8+ tumor-infiltrating lymphocytes as a biomarker indicating improved immunotherapy efficacy in lung cancer, which was crucial for restoring immune surveillance and antitumor efficacy [28, 29]. Additionally, specific subsets of B cells within the tumor microenvironment have been reported to promote anticancer immune responses and inhibit tumor progression by presenting cognate tumor-derived antigens to T cells [30]. Our conclusion aligns with the findings of Chen et al. [15] in SPLCs. They found that in contrast to lung malignancies manifesting as SN, pGGN exhibit features of reduced metabolic activity and a less dynamic immune microenvironment. Moreover, our study aligns closely with the findings reported in a single-arm, phase II trial that treated MPLC patients with sintilimab, a PD-1 inhibitors [18], which highlights that nodules with GGO component had a low response rate to sintilimab (The objective response rate: 5.6%, 2/36). Additionally, the study found that higher ratio of CD8+ T cells was associated with the response to sintilimab. Zhang et al. reported a case, where a solid nodule of MPLC exhibited significant shrinkage following immunotherapy, whereas two subsolid nodules showed no response [17]. High levels of infiltrating CD8+ and CD68+ cells were observed in responding nodule. These findings may elucidate the mechanisms underlying the less aggressive clinical trajectory observed in nodules with GGO component. However, due to small sample size, these studies did not further explore relationship between the proportion of solid components and treatment response rates. In our study, we proceeded to find a positive correlation between the CD8+ T cells abundance and CTR, which once more substantiates the aforementioned conclusion. As the proportion of solid components increases, the infiltration of CD8+ T cells also increases, which may lead to a higher response rate to immunotherapy. The deployment of immunotherapy in such clinical circumstances necessitates judicious consideration, prompting exploration of alternative therapeutic approaches for mGGN in MPLC. Compared to SPLC for both mGGN and pGGN, MPLC exhibit a heightened immune infiltration, with this difference being particularly pronounced in mGGN lesions. This suggests that there is potential for exploring a broader range of immunotherapy options for MPLC. Our research can serve as a valuable supplement to these clinical studies, potentially aiding in the future identification and enrollment of patients who may benefit from such clinical trials. Future investigations must focus on elucidating the crucial role played by immune evasion mechanisms and immune microenvironmental factors throughout the development and progression of pGGN in MPLC.

In addition, we conducted cell communication analysis to investigate the potential intercellular dialogues among immune cells by analyzing ligand-receptor pairs. Our investigation revealed that HLA-related ligand/receptor genes exhibiting a strong correlation with the communication between CD8+ T cells and other immune cell types in MPLC, including FAM3C, an oncogenic cargo protein that serving as conduits for potential cell–cell growth signal communication [31, 32]. This protein emerges as a formidable instigator of tumor metastasis, orchestrating epithelial-mesenchymal transition (EMT) through TGF-β signaling pathways [33]. Interestingly, upregulation of FAM3C has been reported to play an important role in the proliferation, adhesion, migration of cancer cells in nonsmall cell lung cancer [34]. We further discerned several transcription factors that correlated with the biological behavior of CD8+ T cells in MPLC. Our results suggest that the transcription factors including MAFG, RUNX3, TBX21, EOMES, NFATC2 may play pivotal roles in the development of CD8+ T cells along different trajectories of different immunological functions in MPLC. MAFG and TBX21 have been reported to promote tumor initiation and proliferation in lung cancer [35, 36]. In contrast, previous studies revealed that elevating RUNX3 could reduce lung tumor cell migration and recruit CD8+ T cells [37]. While, EOMES was commonly believed to be linked to T cell exhaustion and development, previous study demonstrated that the increase in EOMES+CD8+ T cells may be attributed to tissue-resident memory T cell conversion and metabolic reprogramming in lung cancer [38]. NFATC2, a calcium pathway transcription factor, was reported to regulate lung tumor-initiating cells phenotypes, including tumorspheres, cell motility, and tumorigenesis [39]. Collectively, to explore the crosstalk between cancer cells and CD8+ T cells in MPLC, a more thorough examination was needed in the future. Our study also found that CD8+ T cells in mGGN have higher abundance and activity compared to those in pGGN in MPLC, further supporting our conclusion that mGGN exhibits a more robust immune microenvironment. Consequently, the lower abundance and reduced activity of CD8+ T cells in pGGN in MPLC might lead to a lower response to immunotherapy, highlighting the importance of considering the immune landscape in therapeutic strategies for MPLC.

Several limitations necessitate acknowledgment. Firstly, as the cases we collected are from recent years and only involve early-stage MPLC, prognosis data for this cohort is currently unavailable, thus preventing us from investigating the association between MPLC prognosis and our findings. Secondly, the sample size of MPLC patients is relatively small. To ensure the study’s design is robust, we only included cases, where the same patient had both pGGN and mGGN nodules, and all nodules were surgically resected. Future studies with larger sample sizes are needed to validate our conclusions. Moreover, further mechanistic exploration is required to validate our findings, including the specific functions and mechanisms of the transcription factors identified in this study.

Conclusion

In conclusion, our findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN, which plausibly contributed to understand the developmental mechanisms of MPLC and explain the indolent clinical trajectory observed in pGGN. Moreover, we found that compared to SPLC for both mGGN and pGGN, MPLC exhibit a heightened immune infiltration, and the difference was more pronounced in mGGN. Our findings may provide evidences for the design of immunotherapeutic strategies of MPLC.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 15 KB)

Supplementary file2 (DOCX 15 KB)

Abbreviations

DEGs Differentially expressed genes

EMT Epithelial-mesenchymal transition

GGO Ground-glass opacity

mGGN Mixed ground-glass opacity nodules

MPLC Multiple primary lung cancer

PGGN Pure ground-glass opacity nodules

SPLC Single primary lung cancer

TME Tumor microenvironment

Author contributions

The project administration, conceptualization, methodology and supervision were performed by [Hong-He Luo, Qiong Li, and Ying Zhu]. All authors contributed to the study conception and design. Material preparation, data collection, investigation and analysis were performed by [Mei-Cheng Chen, Hao-Shuai Yang, Zhi Dong, Lu-Jie Li, and Xiang-Min Li]. Writing—original draft preparation and Writing—review and editing were performed by [Mei-Cheng Chen, Hao-Shuai Yang and Zhi Dong] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

The study was supported by the National Natural Science Foundation of China (82102109, 82001882).

Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

The authors declare no competing interests.

Ethics approval

This study was approved by the institutional ethics committee of the First Affiliated Hospital of Sun Yat-sen University [No.2019–232]. Informed consent was waived. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Consent to publish

Consent for publication has been received from all participants.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mei-Cheng Chen, Hao-Shuai Yang and Zhi Dong have contributed equally to this work.
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References

1. Siegel RL Miller KD Fuchs HE Jemal A Cancer statistics CA: a Cancer J Clin 2022 72 1 7 33
Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics. CA: a Cancer J Clin 72(1):7–33
2. Martini N Melamed MR Multiple primary lung cancers J Thorac Cardiovasc Surg 1975 70 4 606 612 10.1016/S0022-5223(19)40289-4 170482
Martini N, Melamed MR (1975) Multiple primary lung cancers. J Thorac Cardiovasc Surg 70(4):606–612170482 10.1016/S0022-5223(19)40289-4
3. Rami‐Porta R Asamura H Travis WD Rusch VW Lung cancer–major changes in the American joint committee on cancer eighth edition cancer staging manual CA: A Cancer J Clin 2017 67 2 138 155 10.3322/caac.21390
Rami‐Porta R, Asamura H, Travis WD, Rusch VW (2017) Lung cancer–major changes in the American joint committee on cancer eighth edition cancer staging manual. CA: A Cancer J Clin 67(2):138–155. 10.3322/caac.2139010.3322/caac.21390
4. Detterbeck FC Franklin WA Nicholson AG Girard N Arenberg DA Travis WD The IASLC Lung Cancer Staging Project: Background Data and proposed criteria to distinguish separate primary lung cancers from metastatic foci in patients with two lung tumors in the forthcoming eighth edition of the TNM classification for lung cancer J Thoracic Oncology 2016 11 5 651 665 10.1016/j.jtho.2016.01.025
Detterbeck FC, Franklin WA, Nicholson AG, Girard N, Arenberg DA, Travis WD (2016) The IASLC Lung Cancer Staging Project: Background Data and proposed criteria to distinguish separate primary lung cancers from metastatic foci in patients with two lung tumors in the forthcoming eighth edition of the TNM classification for lung cancer. J Thoracic Oncology 11(5):651–665. 10.1016/j.jtho.2016.01.02510.1016/j.jtho.2016.01.025
5. Chen K Chen W Cai J Yang F Lou F Wang X Favorable prognosis and high discrepancy of genetic features in surgical patients with multiple primary lung cancers J Thorac Cardiovasc Surg 2018 155 1 371 379 10.1016/j.jtcvs.2017.08.141 29092754
Chen K, Chen W, Cai J, Yang F, Lou F, Wang X et al (2018) Favorable prognosis and high discrepancy of genetic features in surgical patients with multiple primary lung cancers. J Thorac Cardiovasc Surg 155(1):371–37929092754 10.1016/j.jtcvs.2017.08.141
6. Vazquez M Carter D Brambilla E Gazdar A Noguchi M Travis WD Solitary and multiple resected adenocarcinomas after CT screening for lung cancer: histopathologic features and their prognostic implications Lung Cancer 2009 64 2 148 154 10.1016/j.lungcan.2008.08.009 18951650
Vazquez M, Carter D, Brambilla E, Gazdar A, Noguchi M, Travis WD et al (2009) Solitary and multiple resected adenocarcinomas after CT screening for lung cancer: histopathologic features and their prognostic implications. Lung Cancer 64(2):148–15418951650 10.1016/j.lungcan.2008.08.009
7. Choi HK Mazzone PJ Lung cancer screening Med Clin North Am 2022 106 6 1041 1053 10.1016/j.mcna.2022.07.007 36280331
Choi HK, Mazzone PJ (2022) Lung cancer screening. Med Clin North Am 106(6):1041–105336280331 10.1016/j.mcna.2022.07.007
8. Hattori A Matsunaga T Hayashi T Takamochi K Oh S Suzuki K Prognostic impact of the findings on thin-section computed tomography in patients with subcentimeter non-small cell lung cancer J Thorac Oncol 2017 12 6 954 962 10.1016/j.jtho.2017.02.015 28257958
Hattori A, Matsunaga T, Hayashi T, Takamochi K, Oh S, Suzuki K (2017) Prognostic impact of the findings on thin-section computed tomography in patients with subcentimeter non-small cell lung cancer. J Thorac Oncol 12(6):954–96228257958 10.1016/j.jtho.2017.02.015
9. Shimada Y Saji H Otani K Maehara S Maeda J Yoshida K Survival of a surgical series of lung cancer patients with synchronous multiple ground-glass opacities, and the management of their residual lesions Lung Cancer 2015 88 2 174 180 10.1016/j.lungcan.2015.02.016 25758554
Shimada Y, Saji H, Otani K, Maehara S, Maeda J, Yoshida K et al (2015) Survival of a surgical series of lung cancer patients with synchronous multiple ground-glass opacities, and the management of their residual lesions. Lung Cancer 88(2):174–18025758554 10.1016/j.lungcan.2015.02.016
10. Zhang Y Fu F Chen H Management of ground-glass opacities in the lung cancer spectrum Ann Thorac Surg 2020 110 6 1796 1804 10.1016/j.athoracsur.2020.04.094 32525031
Zhang Y, Fu F, Chen H (2020) Management of ground-glass opacities in the lung cancer spectrum. Ann Thorac Surg 110(6):1796–180432525031 10.1016/j.athoracsur.2020.04.094
11. Asamura H Hishida T Suzuki K Koike T Nakamura K Kusumoto M Radiographically determined noninvasive adenocarcinoma of the lung: survival outcomes of Japan clinical oncology group 0201 J Thorac Cardiovasc Surg 2013 146 1 24 30 10.1016/j.jtcvs.2012.12.047 23398645
Asamura H, Hishida T, Suzuki K, Koike T, Nakamura K, Kusumoto M et al (2013) Radiographically determined noninvasive adenocarcinoma of the lung: survival outcomes of Japan clinical oncology group 0201. J Thorac Cardiovasc Surg 146(1):24–3023398645 10.1016/j.jtcvs.2012.12.047
12. Sun F Huang Y Yang X Zhan C Xi J Lin Z Solid component ratio influences prognosis of GGO-featured IA stage invasive lung adenocarcinoma Cancer Imaging: official Pub Int Cancer Imaging Soc 2020 20 1 87 10.1186/s40644-020-00363-6
Sun F, Huang Y, Yang X, Zhan C, Xi J, Lin Z et al (2020) Solid component ratio influences prognosis of GGO-featured IA stage invasive lung adenocarcinoma. Cancer Imaging: official Pub Int Cancer Imaging Soc 20(1):8710.1186/s40644-020-00363-6
13. Hattori A Takamochi K Oh S Suzuki K Prognostic classification of multiple primary lung cancers based on a ground-glass opacity component Ann Thorac Surg 2020 109 2 420 427 10.1016/j.athoracsur.2019.09.008 31593656
Hattori A, Takamochi K, Oh S, Suzuki K (2020) Prognostic classification of multiple primary lung cancers based on a ground-glass opacity component. Ann Thorac Surg 109(2):420–42731593656 10.1016/j.athoracsur.2019.09.008
14. Kobayashi Y Mitsudomi T Sakao Y Yatabe Y Genetic features of pulmonary adenocarcinoma presenting with ground-glass nodules: the differences between nodules with and without growth Ann Oncol 2015 26 1 156 161 10.1093/annonc/mdu505 25361983
Kobayashi Y, Mitsudomi T, Sakao Y, Yatabe Y (2015) Genetic features of pulmonary adenocarcinoma presenting with ground-glass nodules: the differences between nodules with and without growth. Ann Oncol 26(1):156–16125361983 10.1093/annonc/mdu505
15. Chen K Bai J Reuben A Zhao H Kang G Zhang C Multiomics analysis reveals distinct immunogenomic features of lung cancer with ground-glass opacity Am J Respir Crit Care Med 2021 204 10 1180 1192 10.1164/rccm.202101-0119OC 34473939
Chen K, Bai J, Reuben A, Zhao H, Kang G, Zhang C et al (2021) Multiomics analysis reveals distinct immunogenomic features of lung cancer with ground-glass opacity. Am J Respir Crit Care Med 204(10):1180–119234473939 10.1164/rccm.202101-0119OC
16. Goodwin D Rathi V Conron M Wright GM Genomic and clinical significance of multiple primary lung cancers as determined by next-generation sequencing J Thorac Oncol 2021 16 7 1166 1175 10.1016/j.jtho.2021.03.018 33845213
Goodwin D, Rathi V, Conron M, Wright GM (2021) Genomic and clinical significance of multiple primary lung cancers as determined by next-generation sequencing. J Thorac Oncol 16(7):1166–117533845213 10.1016/j.jtho.2021.03.018
17. Zhang C Yin K Liu SY Yan LX Su J Wu YL Multiomics analysis reveals a distinct response mechanism in multiple primary lung adenocarcinoma after neoadjuvant immunotherapy J Immunother Cancer 2021 9 4 e002312 10.1136/jitc-2020-002312 33820821
Zhang C, Yin K, Liu SY, Yan LX, Su J, Wu YL et al (2021) Multiomics analysis reveals a distinct response mechanism in multiple primary lung adenocarcinoma after neoadjuvant immunotherapy. J Immunother Cancer 9(4):e00231233820821 10.1136/jitc-2020-002312
18. Cheng B Li C Li J Gong L Liang P Chen Y The activity and immune dynamics of PD-1 inhibition on high-risk pulmonary ground glass opacity lesions: insights from a single-arm, phase II trial Signal Transduct Targeted Ther 2024 9 1 93 10.1038/s41392-024-01799-z
Cheng B, Li C, Li J, Gong L, Liang P, Chen Y et al (2024) The activity and immune dynamics of PD-1 inhibition on high-risk pulmonary ground glass opacity lesions: insights from a single-arm, phase II trial. Signal Transduct Targeted Ther 9(1):9310.1038/s41392-024-01799-z
19. Li Y Li X Li H Zhao Y Liu Z Sun K Genomic characterisation of pulmonary subsolid nodules: mutational landscape and radiological features European Respir J 2020 55 2 1901409 10.1183/13993003.01409-2019 31699841
Li Y, Li X, Li H, Zhao Y, Liu Z, Sun K et al (2020) Genomic characterisation of pulmonary subsolid nodules: mutational landscape and radiological features. European Respir J 55(2):190140931699841 10.1183/13993003.01409-2019
20. Zeng D Ye Z Shen R Yu G Wu J Xiong Y IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures Front Immunol 2021 12 687975 10.3389/fimmu.2021.687975 34276676
Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y et al (2021) IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front Immunol 12:68797534276676 10.3389/fimmu.2021.687975
21. Shi Y Fan S Wu M Zuo Z Li X Jiang L YTHDF1 links hypoxia adaptation and non-small cell lung cancer progression Nat Commun 2019 10 1 4892 10.1038/s41467-019-12801-6 31653849
Shi Y, Fan S, Wu M, Zuo Z, Li X, Jiang L et al (2019) YTHDF1 links hypoxia adaptation and non-small cell lung cancer progression. Nat Commun 10(1):489231653849 10.1038/s41467-019-12801-6
22. Mittal V Epithelial mesenchymal transition in tumor metastasis Annu Rev Pathol 2018 13 395 412 10.1146/annurev-pathol-020117-043854 29414248
Mittal V (2018) Epithelial mesenchymal transition in tumor metastasis. Annu Rev Pathol 13:395–41229414248 10.1146/annurev-pathol-020117-043854
23. Multhaupt HA Leitinger B Gullberg D Couchman JR Extracellular matrix component signaling in cancer Adv Drug Deliv Rev 2016 97 28 40 10.1016/j.addr.2015.10.013 26519775
Multhaupt HA, Leitinger B, Gullberg D, Couchman JR (2016) Extracellular matrix component signaling in cancer. Adv Drug Deliv Rev 97:28–4026519775 10.1016/j.addr.2015.10.013
24. Wu S Li D Chen J Chen W Ren F Tailing effect of PD-1 antibody results in the eradication of unresectable multiple primary lung cancer presenting as ground-glass opacities: a case report Ann Palliat Med 2021 10 1 778 784 10.21037/apm-20-2132 33545799
Wu S, Li D, Chen J, Chen W, Ren F (2021) Tailing effect of PD-1 antibody results in the eradication of unresectable multiple primary lung cancer presenting as ground-glass opacities: a case report. Ann Palliat Med 10(1):778–78433545799 10.21037/apm-20-2132
25. Wang M Herbst RS Boshoff C Toward personalized treatment approaches for non-small-cell lung cancer Nat Med 2021 27 8 1345 1356 10.1038/s41591-021-01450-2 34385702
Wang M, Herbst RS, Boshoff C (2021) Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 27(8):1345–135634385702 10.1038/s41591-021-01450-2
26. Locati M Curtale G Mantovani A Diversity, mechanisms, and significance of macrophage plasticity Annu Rev Pathol 2020 15 123 147 10.1146/annurev-pathmechdis-012418-012718 31530089
Locati M, Curtale G, Mantovani A (2020) Diversity, mechanisms, and significance of macrophage plasticity. Annu Rev Pathol 15:123–14731530089 10.1146/annurev-pathmechdis-012418-012718
27. Mantovani A Sozzani S Locati M Allavena P Sica A Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes Trends Immunol 2002 23 11 549 555 10.1016/S1471-4906(02)02302-5 12401408
Mantovani A, Sozzani S, Locati M, Allavena P, Sica A (2002) Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 23(11):549–55512401408 10.1016/S1471-4906(02)02302-5
28. Duchemann B Naigeon M Auclin E Ferrara R Cassard L Jouniaux J-M CD8+PD-1+ to CD4+PD-1+ ratio (PERLS) is associated with prognosis of patients with advanced NSCLC treated with PD-(L)1 blockers J Immunother Cancer 2022 10 2 e004012 10.1136/jitc-2021-004012 35131864
Duchemann B, Naigeon M, Auclin E, Ferrara R, Cassard L, Jouniaux J-M et al (2022) CD8+PD-1+ to CD4+PD-1+ ratio (PERLS) is associated with prognosis of patients with advanced NSCLC treated with PD-(L)1 blockers. J Immunother Cancer 10(2):e00401235131864 10.1136/jitc-2021-004012
29. Li F Li C Cai X Xie Z Zhou L Cheng B The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: a systematic review and meta-analysis Clin Med 2021 41 101134
Li F, Li C, Cai X, Xie Z, Zhou L, Cheng B et al (2021) The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: a systematic review and meta-analysis. Clin Med 41:101134
30. Sharonov GV Serebrovskaya EO Yuzhakova DV Britanova OV Chudakov DM B cells, plasma cells and antibody repertoires in the tumour microenvironment Nat Rev Immunol 2020 20 5 294 307 10.1038/s41577-019-0257-x 31988391
Sharonov GV, Serebrovskaya EO, Yuzhakova DV, Britanova OV, Chudakov DM (2020) B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat Rev Immunol 20(5):294–30731988391 10.1038/s41577-019-0257-x
31. Yang W Feng B Meng Y Wang J Geng B Cui Q FAM3C-YY1 axis is essential for TGFbeta-promoted proliferation and migration of human breast cancer MDA-MB-231 cells via the activation of HSF1 J Cell Mol Med 2019 23 5 3464 3475 10.1111/jcmm.14243 30887707
Yang W, Feng B, Meng Y, Wang J, Geng B, Cui Q et al (2019) FAM3C-YY1 axis is essential for TGFbeta-promoted proliferation and migration of human breast cancer MDA-MB-231 cells via the activation of HSF1. J Cell Mol Med 23(5):3464–347530887707 10.1111/jcmm.14243
32. Shi M Duan G Nie S Shen S Zou X Elevated FAM3C promotes cell epithelial-mesenchymal transition and cell migration in gastric cancer Onco Targets Ther 2018 11 8491 8505 10.2147/OTT.S178455 30584315
Shi M, Duan G, Nie S, Shen S, Zou X (2018) Elevated FAM3C promotes cell epithelial-mesenchymal transition and cell migration in gastric cancer. Onco Targets Ther 11:8491–850530584315 10.2147/OTT.S178455
33. Jansson AM Csiszar A Maier J Nystrom AC Ax E Johansson P The interleukin-like epithelial-mesenchymal transition inducer ILEI exhibits a non-interleukin-like fold and is active as a domain-swapped dimer J Biol Chem 2017 292 37 15501 15511 10.1074/jbc.M117.782904 28751379
Jansson AM, Csiszar A, Maier J, Nystrom AC, Ax E, Johansson P et al (2017) The interleukin-like epithelial-mesenchymal transition inducer ILEI exhibits a non-interleukin-like fold and is active as a domain-swapped dimer. J Biol Chem 292(37):15501–1551128751379 10.1074/jbc.M117.782904
34. Thuya WL Kong LR Syn NL Ding LW Cheow ESH Wong RTX FAM3C in circulating tumor-derived extracellular vesicles promotes non-small cell lung cancer growth in secondary sites Theranostics 2023 13 2 621 638 10.7150/thno.72297 36632230
Thuya WL, Kong LR, Syn NL, Ding LW, Cheow ESH, Wong RTX et al (2023) FAM3C in circulating tumor-derived extracellular vesicles promotes non-small cell lung cancer growth in secondary sites. Theranostics 13(2):621–63836632230 10.7150/thno.72297
35. Fan W Cao D Yang B Wang J Li X Kitka D Hepatic prohibitin 1 and methionine adenosyltransferase alpha1 defend against primary and secondary liver cancer metastasis J Hepatol 2024 80 3 443 453 10.1016/j.jhep.2023.11.022 38086446
Fan W, Cao D, Yang B, Wang J, Li X, Kitka D et al (2024) Hepatic prohibitin 1 and methionine adenosyltransferase alpha1 defend against primary and secondary liver cancer metastasis. J Hepatol 80(3):443–45338086446 10.1016/j.jhep.2023.11.022
36. Jia YC Wang JY Liu YY Li B Guo H Zang AM LncRNA MAFG-AS1 facilitates the migration and invasion of NSCLC cell via sponging miR-339-5p from MMP15 Cell Biol Int 2019 43 4 384 393 10.1002/cbin.11092 30599080
Jia YC, Wang JY, Liu YY, Li B, Guo H, Zang AM (2019) LncRNA MAFG-AS1 facilitates the migration and invasion of NSCLC cell via sponging miR-339-5p from MMP15. Cell Biol Int 43(4):384–39330599080 10.1002/cbin.11092
37. Li X Zhong M Wang J Wang L Lin Z Cao Z miR-301a promotes lung tumorigenesis by suppressing Runx3 Mol Cancer 2019 18 1 99 10.1186/s12943-019-1024-0 31122259
Li X, Zhong M, Wang J, Wang L, Lin Z, Cao Z et al (2019) miR-301a promotes lung tumorigenesis by suppressing Runx3. Mol Cancer 18(1):9931122259 10.1186/s12943-019-1024-0
38. Wang G Sun J Zhang J Zhu Q Lu J Gao S Single-cell transcriptional profiling uncovers the association between EOMES(+)CD8(+) T cells and acquired EGFR-TKI resistance Drug Resistance Updates: Rev Comment Antimicrob Anticancer Chemother 2023 66 100910 10.1016/j.drup.2022.100910
Wang G, Sun J, Zhang J, Zhu Q, Lu J, Gao S et al (2023) Single-cell transcriptional profiling uncovers the association between EOMES(+)CD8(+) T cells and acquired EGFR-TKI resistance. Drug Resistance Updates: Rev Comment Antimicrob Anticancer Chemother 66:10091010.1016/j.drup.2022.100910
39. Xiao ZJ Liu J Wang SQ Zhu Y Gao XY Tin VP NFATc2 enhances tumor-initiating phenotypes through the NFATc2/SOX2/ALDH axis in lung adenocarcinoma Elife 2017 6 e26733 10.7554/eLife.26733 28737489
Xiao ZJ, Liu J, Wang SQ, Zhu Y, Gao XY, Tin VP et al (2017) NFATc2 enhances tumor-initiating phenotypes through the NFATc2/SOX2/ALDH axis in lung adenocarcinoma. Elife 6:e2673328737489 10.7554/eLife.26733
