
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39259234
1322
10.1007/s12672-024-01322-4
Analysis
Deciphering the impact of aggregated autophagy-related genes TUBA1B and HSP90AA1 on colorectal cancer evolution: a single-cell sequencing study of the tumor microenvironment
Xu Qianping 1
Liu Chao 23
Wang Hailin 4
Li Shujuan 23
Yan Hanshen 23
Liu Ziyang 23
Chen Kexin 23
Xu Yaoqin 23
Yang Runqin 23
Zhou Jingfang 23
Yang Xiaolin yangwj04@126.com

3
Liu Jie 123574514@qq.com

5
Wang Lexin 1418801292@qq.com

23
1 grid.412901.f 0000 0004 1770 1022 Department of Gastrointestinal and Hernial Surgery, Meishan Hospital of West China Hospital of Sichuan University, Meishan City People’s Hospital, Meishan, 620010 China
2 https://ror.org/02h8a1848 grid.412194.b 0000 0004 1761 9803 General Hospital of Ningxia Medical University, Yinchuan, 750000 Ningxia China
3 https://ror.org/02h8a1848 grid.412194.b 0000 0004 1761 9803 Ningxia Medical University, Yinchuan, 750000 Ningxia China
4 https://ror.org/05k3sdc46 grid.449525.b 0000 0004 1798 4472 Department of Hepatobiliary Surgery, Affliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province China
5 https://ror.org/05qz7n275 grid.507934.c Department of General Surgery, Dazhou Central Hospital, Dazhou, 635000 China
11 9 2024
11 9 2024
12 2024
15 43125 7 2024
6 9 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/.
Background

Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with the tumor microenvironment (TME) playing a crucial role in its progression. Aggregated autophagy (AA) has been recognized as a factor that exacerbates CRC progression. This study aims to study the relationship between aggregated autophagy and CRC using single-cell sequencing techniques. Our goal is to explain the heterogeneity of the TME and to explore the potential for targeted personalized therapies.

Objective

To study the role of AA in CRC, we employed single-cell sequencing to discern distinct subpopulations within the TME. These subpopulations were characterized by their autophagy levels and further analyzed to identify specific biological processes and marker genes.

Results

Our study revealed significant correlations between immune factors and both clinical and biological characteristics of the tumor microenvironment (TME), particularly in cells expressing TUBA1B and HSP90AA1. These immune factors were associated with T cell depletion, a reduction in protective factors, diminished efficacy of immune checkpoint blockade (ICB), and enhanced migration of cancer-associated fibroblasts (CAFs), resulting in pronounced inflammation. In vitro experiments showd that silencing TUBA1B and HSP90AA1 using siRNA (Si-TUBA1B and Si-HSP90AA1) significantly reduced the expression of IL-6, IL-7, CXCL1, and CXCL2 and inhibition of tumor cell growth in Caco-2 and Colo-205 cell lines. This reduction led to a substantial alleviation of chronic inflammation and highlighted the heterogeneous nature of the TME.

Conclusion

This study marks an initial foray into understanding how AA-associated processes may potentiate the TME and weaken immune function. Our findings provide insights into the complex dynamics of the TME and highlight potential targets for therapeutic intervention, suggesting a key role for AA in the advancement of colorectal cancer.

Keywords

Single-cell
Colorectal cancer (CRC)
Tumor microenvironment (TME)
Aggregated Autophagy (AA)
TUBA1B and HSP90AA1
Cancer-associated fibroblast (CAF)
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Colorectal cancer (CRC) ranks third in worldwide cancer incidence, within the second highest mortality rate. It is challenging to diagnose CRC early, and the clinical manifestations of CRC often progress to the middle and late stages [1–3]. In the 2020 update from the WHO's International Agency for Research on Cancer (IARC), high mortality rate and poor treatment prognosis of colorectal cancer (CRC) have been acknowledged as notable features of CRC in China [4–6]. The traditional treatment for CRC primarily involves surgical intervention, which often exhibits significant individual variability [7, 8]. For patients with advanced surgery, the prognosis is still not ideal [9]. Therefore, in-depth research on CRC is needed to find a perfect treatment method and provide an experimental basis for further research on the pathogenesis of CRC [10].

The TME refers to the complex and dynamic ecosystem surrounding a tumor, comprising various cell types, extracellular matrix components, signaling molecules, and blood vessels. It plays a crucial role in cancer progression. TME not only influencing cell growth but also the behavior of immune cells and other stromal components.In CRC, the TME is characterized by the presence of a diverse array of cells, including cancer-associated fibroblasts (CAFs), immune cells (such as macrophages, T cells, and B cells), endothelial cells, and mesenchymal stem cells. These cellular components interact with each other and with the tumor cells through a network of signaling pathways, creating a microenvironment that can either promote or inhibit tumor growth.

The TME is often infiltrated by various immune cells, including tumor-associated macrophages (TAMs), dendritic cells, and lymphocytes. The balance between pro-tumor and anti-tumor immune responses is critical, as immune cells can either help eliminate tumor cells or promote tumor growth through the release of cytokines, growth factors, and other mediators. In addition Cancer-Associated Fibroblasts (CAFs) were play a pivotal role in remodeling the extracellular matrix and facilitating tumor growth. CAFs secrete various factors that can enhance tumor cell proliferation, migration, and invasion, while also modulating the immune response within the TME. They can contributes to the heterogeneity observed within CRC tumors. Different regions within the tumor may exhibit distinct cellular compositions and microenvironmental conditions, leading to varying responses to therapies.

Emerging evidence underscores the critical role of changes in the CRC TME and immune modulators. Single-cell transcriptomics has revealed complex communications among various cell subtypes within the TME [11, 12]. Recent findings by AlMusawi show how tumor-associated fibroblasts within the TME can enhance cell adhesion, elevate E-calmodulin expression levels, and promote tumor cell migration and proliferation, thereby accelerating tumorigenic development [13]. Maria Tsoumakidou et al. have highlighted that high expression of CAFs serves as a robust prognostic marker for patient survival [14]. Furthermore, Kasprzak et al. have identified interactions among TME cells that lead to the production of various pathogenic factors, culminating in the passive release of inflammatory cytokines, such as tumor necrosis factor-alpha (TNFα), interleukins (IL)-1, IL-6, and chemotactic factors like IL-8 [15]. These interactions are linked to cellular inflammatory damage [16]. This body of evidence points to the necessity of further exploring the pathogenesis associated with different TME subtypes in CRC.

The advent of single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for unveiling the intricacies of tumor heterogeneity [20–23]. With its pivotal role in revealing the diversity within tumors, literature reports have highlighted scRNA-seq's significant contributions to our understanding of the TME expression profile [18, 19]. Therefore, we have employed single-cell sequencing not only to explain the relationship between AA-associated genes and CRC but also to light on the interplay between AA and the TME.

AA-associated cell death, characterized by intracellular phagocytic degradation, has emerged as a vital mechanism for maintaining the stability of the human internal environment [17, 18]. This study studys the relationship between AA and the tumor TME in CRC. These samples encompass a diverse array of cell types, including fibroblasts, macrophages, T cells, and B cells. By applying non-negative matrix factorization (NMF) clustering to the AA-related data, we identified distinct patterns of autophagy across the heterogeneous cellular landscape of the CRC TME [19]. These patterns are characterized by their unique immune signatures, metabolic pathways, transcriptional profiles, and prognostic implications [20–23]. Our comprehensive single-cell analysis suggests that the intensification of aggregated autophagy-related processes within the TME may contribute to a decline in immune function, potentially accelerating the progression of CRC.

Experimental procedures and materials

GEO and TCGA data collection and processing

The Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo) serves as a comprehensive repository for a wide variety of datasets. In our investigation of colorectal cancer, we identified GSM4994385 as representative of the normal group, while GSM5688708 and GSM5688711 were selected to represent the tumor group. Additionally, we included the cohort study sample GSE39582, GSE37182 and GSE41258. To further enhance our analysis, we incorporated RNA sequencing data from TCGA-COAD. This combined dataset provides a robust foundation for our colorectal cancer research.

Visualization of different TME types in CRC

We employed the Seurat package to construct Seurat objects from the gene expression matrix derived from single-cell RNA sequencing (ScRNA-seq) data. Using the Seurat package, we normalized the data for each individual cell. Subsequently, we applied the ScaleData and PCA functions to identify the principal components within the Seurat object. Following dimensionality reduction through t-distributed stochastic neighbor embedding (t-SNE), we delineated and graphically represented the primary cell categories or variants within the tumor microenvironment (TME) by using the Idents and DimPlot functions. This method enabled us to systematically analyze and visualize the intricate cellular landscape of the TME.

Pseudo-time analysis of AA-related genes in TME cells

To delve into the relationship between the apparent temporal progression of cells and immune factors within the TME, we harnessed the Monocle R package to scrutinize single-cell RNA data across all cell types in CRC. We implemented the DDRTree algorithm for dimensionality reduction, which facilitated the tracking of cellular trajectories. Subsequently, we employed a function designed for plotting pseudo-time heatmaps to graphically depict changes in immune factors as they unfolded along the pseudo-temporal course of diverse TME cell types in CRC. This visualization technique illuminated the dynamic expression patterns of immune factors, revealing intricate temporal dynamics within the TME as captured in the heatmap.

Analysis of intercellular communication between NMF immune factors

The CellChat R package, which includes a comprehensive database of ligand-receptor pairings for both human and mouse models, enables an in-depth analysis of intercellular communication networks through the use of annotated single-cell RNA sequencing data spanning various cell clusters. Initially, we harnessed the capabilities of the Cell Chat database to dissect and understand significant signaling inputs and outputs within all cell groupings delineated by the NMF within the TME. Following this analysis, we utilized the CellChat function to depict the strength and vulnerability of the communication networks emanating from a specific cell grouping and radiating towards various other cell aggregates within all NMF clusters. To conclude, the CellChat function was instrumental in graphically representing the focal cell cluster and delineating its interactions with other pertinent cell groupings, thereby offering a spherical representation of targeted intercellular dialogues.

Si-RNA transfection

Gene knockdown and overexpression were conducted using the HiPerFect Transfection kit (Qiagen, Germany). Colo-205 and Caco-2 cells were transfected with siRNA targeting TUBA1B and HSP90AA1, following the manufacturer’s protocol. As negative controls, cells were transfected with non-targeting siRNA, or underwent sham transfection. After 24 h of culture post-transfection, the efficiency of gene knockdown was assessed using quantitative reverse transcription PCR (qRT-PCR).

Expression of TUBA1B and HSP90AA1 by qRT-PCR in Colo-205 and Caco-2 cells

Total RNA was extracted from Colo-205 and Caco-2 cells using an RNA extraction kit from Aibotek Biotechnology Company, Wuhan. The TUBA1B and HSP90AA1 mRNA expression levels were quantified using specific primers after extraction. RNA was reverse transcribed into cDNA using the RNA reverse transcription kit from TaKaRa, Japan. Quantitative PCR was then used to measure mRNA expression levels via the 2-ΔΔCt method, providing precise gene activity measurements.

Cell viability assay

Collect Colo-205 and Caco-2 cells and distribute them into a 96-well plate, with 2000 cells per well. Set up 5 replicate wells for each condition. After seeding the plate, measure the optical density (OD) values at 1, 2, 3, 4, and 5 days using a microplate reader. Then, add 10 μl of CCK8 solution to each well and measure the OD values within 2–4 h using the microplate reader.

Statistical analysis

We utilized multiple statistical techniques, such as the t-test for student instances, Kruskal–Wallis examination, and Chi-square examination, to value variances in consecutive outcome or grouping variables inside specific subsets of cells. To evaluate the biologic characteristics of every immune factor-associated subtype within TME cell types in CRC. The visualization of scaled data for target variables in NMF groupings derived from TME CRC cellular compositions was achieved using complex heat maps or pre-map packages. Statistical analyses were conducted using R 4.0 software, with analytica importance characterized by a p-value below 0.05.

Results

Characterization of cell–cell interactions and AA-related gene expression in the CRC TME

In this study, we utilized data from the GEO and TCGA databases, concentrating on three single-cell RNA sequencing datasets of CRC (GSM4994385, GSM5688708, and GSM5688711) to enhance our understanding of the tissue landscape associated with this disease, as previously illustrated in Fig. 1A. Our dataset comprised 33,694 cells from the TME, meticulously categorized into primary cell types, including T cells, B cells, myeloid cells, plasma cells, epithelial cells, and fibroblasts (Fig. 1B).Fig. 1 Tumor microenvironment of CRC. A This study's overall design and data were obtained from the GEO dataset (GSM4994385, GSM5688708, GSM5688711); B cell type annotation of 33,694 cells was performed using Seurat's distribution-based random neighbor embedding (t-SNE) plots; C The expression of aggregated autophagy genes TUBA1B and HSP90AA1 in normal and tumor group; D cell chat analysis show variety correlation of TME cell types in T Cell, B Cell, Myeloids Cell, Plasma Cell, Epithelial Cell, and Fibroblasts Cell; E Heat map show that gene variety

Through a detailed analysis of cell–cell communication, we elucidated the intricate interactions among these TME cell types, revealing their complex interrelationships. Notably, our analysis uncovered significant associations between AA and various TME cell types, encompassing T cells, B cells, myeloid cells, plasma cells, epithelial cells, and fibroblasts (Fig. 1C). A heatmap further demonstrated the dynamic expression patterns of all AA-related genes throughout different stages of tumor progression, highlighting notable genes such as TUBA1A, TUBA1B, PARK7, TUBA4B, DYNC1I2, and HSP90AA1 (Fig. 1E). In addition, the AA-related genes HSP90AA1 and TUBA1B also exhibit widespread expression across different cell subpopulations (Fig. 1D).Our comprehensive findings suggest that elevated expression levels of TUBA1B, and HSP90AA1 may potentially accelerate the progression of CRC. These insights not only enhance our understanding of the molecular mechanisms underpinning CRC but also pave the way for targeted therapeutic interventions.

Role of AA-related genes TUBA1B and HSP90AA1 in CAFs activation and CRC progression

The AA-related gene has been implicated in the proliferation of CAFs, which play a crucial role in promoting vascular inflammation and tissue stiffness, thereby significantly influencing the progression of CRC. To further investigate this relationship, we isolated highly abundant fibroblasts from tumor tissues. Pseudotime analysis revealed that the AA genes TUBA1B and HSP90AA1 exhibited low expression levels at earlier stages, while DYNC1LI2 and HSP90AA1 were expressed at later stages (Fig. 2A). Utilizing non-negative matrix factorization (NMF) analysis, we identified four distinct clusters of CAFs within the tumor microenvironment (TME) based on AA gene expression: TUBA1B + CAF-C1 (n = 40), HSP90AA1 + CAF-C2 (n = 125), DYNC1I2 + CAF-C3 (n = 94), and non-aggregated CAF-C0 (Fig. 2B). Cell-Chat analysis revealed variable molecular signaling interactions among these CAF clusters, as well as interactions with epithelial cells and other fibroblasts (Fig. 2C). Notably, both TUBA1B and HSP90AA1 not only received signals but also initiated signaling pathways, suggesting a potent regulatory role in the TME (Fig. 2D). Furthermore, the expression of TUBA1B and HSP90AA1 was found to inhibit the function of macrophage migration inhibitory factor (MIF) and enhance the expression of the adhesion protein periostin within tumors (Fig. 2E).Fig. 2 Aggregate autophagy gene set factors alter the characteristics of fibroblasts; A Pseudo-time analysis of the role of AA-related gene sets in fibroblasts (n = 746); B, C Four CAF clusters, DYNC1I2 + CAF-C6 (n = 94), HSP90AA1 + CAF-C2 (n = 125), TUBA1B + CAF-C1 (n = 40), and the percentage of HSP90AA1 + CAF-C2 was higher in tumors than in normal mucosa (p < 0.05); D heat map using GAS pathway analysis ( p < 0.05); F,G Transcription factor analysis between four clusters; H correlation of 6 TFS with inflammation demonstrated; The heatmap illustrates variations in the mean expression levels of essential genes involved in common signaling pathways across four distinct clusters. These pathways include essential components such as collagen, extracellular matrix (ECM), matrix metalloproteinases (MMPs), changing growth factor beta (TGF-β), neo-angiogenesis, contraction, RAS signaling, and pro-inflammatory mediators

Our investigation into gene regulatory networks highlighted significant differences in the expression of 28 transcription factors (TFs) across the four CAF clusters. In particular, the TUBA1B and HSP90AA1 cluster exhibited elevated levels of TFs such as KLF2, REL, STAT3, STAT1, and CREB3 (Fig. 2F). The identification of Pan-CAF signatures, as reported in previous studies, reinforced our findings, revealing a strong correlation between the TUBA1B and HSP90AA1 cluster and inflammatory cancer-associated fibroblasts (iCAFs-2). These iCAFs-2 subtypes are characterized by their secretion of various growth factors, including pro-inflammatory chemokines and cytokines such as CXCL2, CXCL1, CCL2, IL-6, and IL-7 (Fig. 2G, H). Given the critical role of CAFs in the tumor microenvironment, the observed association between TUBA1B and HSP90AA1 expression and CAF activation underscores the significance of TUBA1B in CRC progression. Specifically, TUBA1B and HSP90AA1 may drive CAFs to enhance the TME through the release of inflammatory factors, thereby significantly influencing tumor development and progression.

Impact of AA-related gene expression on T cell dynamics in CRC progression

Recent evidence highlights the critical presence of T cells as the most abundant and characteristic components of the TME in CRC. Intriguingly, the progression of CRC through AA is marked by alterations in T cell dynamics. We isolated high-abundance CD8 + T cells (n = 4753) and B cells (n = 2327) from CRC samples to investigate these changes. Pseudotime analysis indicated that genes TUBA1A, TUBA1B, and DYNC1H1 showed elevated expression in the late stages of CRC (Fig. 3A). Using NMF analysis, we delineated distinct clusters of CD8 + T cells based on AA gene expression, including TUBA1A + CD8 + T cells-C1 (n = 11), PARK7-CD8 + T cells-C2 (n = 7), TUBA1B + CD8 + T cells-C3 (n = 8), TUBB4B + CD8 + T cells-C4 (n = 208), and others (Fig. 3B). Cell-chat analysis revealed varying levels of ligand-receptor interactions among these clusters, particularly between epithelial cells and T cells (Fig. 3C). The TME may drive the plasticity of tumor subtypes, and we analyzed the communication network between cells through cellChat. The results showed that epithelial cells and T cells acted as communication hubs through VISFATIN signals (Fig. 3D).Fig. 3 NMF clusters of AA regulatory factors in T cells, B cells and epithelial cells; A Pseudo-time analysis play the role of AA-related gene sets in T cells, B cells (CD8 + T Cell n = 4753,B cell n = 2327); B, C Role of ligand-receptor relationships in AA-related CD8 + T cells clusters; E, F Transcription factor analysis in AA-related CD8 + T cells clusters; G CD8 + T cells killing and exhaustion rate correlation is demonstrated; Heat map displaying the the expression of ICB in AA-related CD8 + T cells clusters

During CRC progression, interactions between AA TME cells and transcription factors such as JUND, JUNB, FOS, JUN, FOSB, RORA, BATF, IRF1, RBPJ, and RPDM1 were observed. These factors were up-regulated in TUBA1A + CD8 + T cells-C1 and PARK7-CD8 + T cells-C2 clusters, while in TUBA1B + B cells-C3, decreased transcription factor activity was linked to immune evasion (Fig. 3E). Additionally, TUBA1B + CD8 + T cells-C3 showed reduced association with T cell immune factors (Fig. 3G). Heatmap analysis further demonstrated significant variations in the expression of key immune checkpoint molecules across different clusters. Notably, TIGIT showed high expression in tumor-infiltrating lymphocytes (TILs), which interacts with receptors like CD155, D112, and CD113, enhancing T cell activation but dampening cytotoxicity. Conversely, TNFRSF15 (GITR) showed low expression in TUBA1A + CD8 + T cells-C1 and PARK7-CD8 + T cells-C2, promoting tumor development through co-stimulatory effects (Fig. 3F). Comparative analysis of AA-mediated interactions among CD8 + T cells revealed that the presence of TUBA1A + CD8 + T cells-C1 and PARK7-CD8 + T cells-C2 in the TME contributes to T cell exhaustion and immune escape, thereby accelerating tumor metastasis (Fig. 3G). In summary, the interplay of T cell exhaustion, and immune escape mechanisms within the TME significantly accelerates tumor development and metastasis in CRC.

Role of AA-related gene expression in macrophage profiling

During tumor progression, the expression of genes such as TUBA1C, HSP90AA1, TUBB4B, and DYNLL1 shows a marked increase in the later stages, while TUBA1B is expressed at earlier phases (Fig. 4A). A comprehensive analysis of AA-related gene sets across various macrophage types within the TME revealed six distinct macrophage clusters: TUBA1B + Mac-C1 (n = 72), DYNLL1 + Mac-C2 (n = 35), TUBA1C + Mac-C3 (n = 300), TUBB4B + Mac-C4 (n = 40), HSP90AA1-Mac-C5 (n = 63), and Non-Aggre-Mac-C6. These clusters displayed varying degrees of ligand-receptor interactions with epithelial cells (Fig. 4B, C).Fig. 4 Identification of clustered AA-related gene in macrophages; A pseudo-time analysis of AA-related gene sets in macroautophagy (n = 1088); B,C ligand-receptor relationship roles among the AA-related gene in MAC clusters; D Metabolism anslysis in six MAC clusters

Further examination of the metabolic activities of these macrophage clusters demonstrated that the TUBA1B-Mac-C1 cluster exhibited elevated expression of critical metabolic pathways, including the pentose phosphate pathway, citrate cycle (TCA cycle), and pyruvate metabolism (Fig. 4D). This indicates that the aggregation of autophagy-associated macrophages significantly enhances TCA cycle energy metabolism, thereby facilitating tumor development. Such metabolic reprogramming not only meets the heightened energy demands of the tumor but also contributes to its aggressive growth and in vasiveness.

Expression profiling of TUBA1B and HSP90AA1 were involvement in metabolic pathways in CRC

Next, we analyzed the expression of TUBA1B and HSP90AA1 from GSE37182 and GSE41258. Our results indicated that the expression of AA-related genes, specifically TUBA1B and HSP90AA1, is significantly increased in COAD (Fig. 5A). We also observed that both genes exhibited favorable AUC values (Fig. 5B). Coincidentally, KEGG analysis revealed that these genes are associated with cellular development and metabolic processes, such as the TCA cycle and cell cycle regulation (Fig. 5C).Fig. 5 High expression of TUBA1B and HSP90AA1 accelerates tumor progression. A Validation of TUBA1B and HSP90AA1 expression in GSE37182 and GSE41258. B Diagnostic efficacy analysis, with an AUC greater than 0.75 indicating high diagnostic efficacy. C KEGG enrichment analysis of differentially expressed genes in GSE37182

Influence of TUBA1B and HSP90AA1 on the exhaustion of CD8 T and CD4 cells in CRC

Subsequently, we conducted a further analysis of TUBA1B and HSP90AA1. Our findings revealed that the expression of TUBA1B is associated with the promotion of exhaustion in CD8 T and CD4 cells, with blue indicating a negative correlation and red indicating a positive correlation (Fig. 6A). Conversely, higher expression levels of HSP90AA1 were found to accelerate the functional exhaustion of CD8 T and CD4 cells, as well as impair the activity of neighboring cells, including macrophages and mast cells, showing a positive correlation in their expression (Fig. 6B).Fig. 6 TUBA1B and HSP90AA1 induce immune dysfunction, accelerating immune evasion. A, B Immune correlation analysis of TUBA1B and HSP90AA1, with red indicating positive correlation and blue indicating negative correlation

Inhibition of TUBA1B and HSP90AA1 reduces tumor cell viability and alters inflammatory factors in CRC

Previous analyses have identified that the expression of TUBA1B and HSP90AA1 is a key factor accelerating the progression of CRC. To further study this, we employed siRNA to inhibit the expression of TUBA1B and TUBA1A. The CCK-8 assay confirmed that silencing TUBA1B and HSP90AA1 significantly reduced the viability of tumor cells (Fig. 7A). Additionally, we observed a specific decrease in the expression of TUBA1B and HSP90AA1 in CRC following transfection with siTUBA1B and siHSP90AA1, which was accompanied by a reduction in the expression of IL6, IL7, CXCL1, and CXCL2 (Fig. 7B, C). This suggests that chronic inflammatory factors are critical components involved in the complex TME. Inhibiting the expression of TUBA1B and HSP90AA1 may alleviate the complexities of the TME while concurrently suppressing tumor cell proliferation.Fig. 7 Low expression of TUBA1B and HSP90AA1 inhibits tumor growth. A CCK8 assay validated the effect of transfecting si-TUBA1B and si-HSP90AA1 on tumor cell viability in Caco-2 Colo-205; B Expression levels of TUBA1B and HSP90AA1 in Caco-2 Colo-205; C Expression levels of inflammatory factors IL6, IL7, CXCL1, and CXCL2 in Caco-2 Colo-205. **P < 0.01, ***P < 0.001, ****P < 0.0001

AA-related gene clusters in CRC prognosis and immunotherapy response

To enhance the accuracy of prognostic predictions for CRC and to assess patient outcomes following immunotherapy. Our analysis revealed a downregulation in the expression of several gene clusters, including DYNC1I2 + CAF-C3, TUBB4B + CD8 + T cells-C4, TUBA1B + Mac-C1, and TUBA1C + Mac-C3, alongside an up-regulation of clusters such as TUBA1B + CD8 + T cells-C3, PARK7 + CD8 + T cells-C2, TUBB4B + Mac-C4, and HSP90AA1 + Mac-C5, suggesting significant variability within CRC (Fig. 8A).Fig. 8 Illustrates the general prognosis and response to immunotherapy of AA cell types; A Predict the expression of different groups by GSVA score; B Single factor regression analysis that twelve aggregation autophagy cluster by TCGA and GEO; C Analysis of immunotherapy response (analysis of twelve cell clusters with response rate TCGA-COAD); D Global landscape of the tumor microenvironment.*P < 0.05,**P < 0.01,***P < 0.001,****P < 0.0001

Further analyses using Cox regression and bulk transcriptome data indicated that the expression of gene clusters DYNC1I2 + CAF-C3, HSP90AA1 + Mac-C5, TUBA1B + CD8 + T cells-C3, TUBA1B + Mac-C1, TUBA1C + Mac-C3, and TUBB4B + CD8 + T cells-C4 plays a crucial role in resisting immune escape and inhibiting tumor growth and metastasis (Fig. 8B). Additionally, our evaluation of immunotherapy responses, utilizing data from TCGA-COAD and GEO-GSE39582, demonstrated that AA-related genes, such as DYNC1I2 + CAF-C3, HSP90AA1 + CAF-C2, TUBA1A + CD8 + T cells-C1, TUBA1B + CAF-C1, TUBA1B + Mac-C1, and TUBA1C + Mac-C3, are implicated in promoting tumor cell proliferation and migration. Conversely, clusters such as TUBA1B + CD8 + T cells-C3, TUBB4B + CD8 + T cells-C4, and PARK7 + CD8 + T cells-C2 exhibit protective functions, contributing to increased cell killing and exhaustion, thereby facilitating effective anti-tumor responses (Fig. 8C, D). These findings elucidate the differential roles of autophagy-related gene clusters and enhance our understanding of their impact on CRC progression and response to immunotherapy.

Discussion

In our study, we identified a specific set of AA-related genes activated by changes in immune levels and interactions among diverse cell types within both the AA group and the TME, utilizing 10X Genomics single-cell analytics to explore their role in the progression of CRC [24–28]. Our integrated analysis identified differentially expressed genes associated with AA in CRC, revealing nuanced regulatory patterns across various cell types within the TME [29–33]. This regulation is characterized by intricate interactions, particularly with epithelial cells, and extends to T cells, B cells, macrophages, and fibroblasts. Complementing these findings, we developed a novel classification system for CAFs, a key component of the stroma, categorizing them into distinct groups, including pan-myCAFs, pan-dCAFs, pan-iCAFs, pan-iCAFs-2, and pan-pCAFs, based on specific molecular signatures [34].

Furthering our investigation, we identified pivotal genes such as TUBA1B and HSP90AA1 that drive inflammatory infiltration through interactions with pro-inflammatory agents, including CXCL2, CXCL1, CCL2, IL6, and IL7. These interactions contribute to the progression of CRC by dampening immune function [35]. Additionally, Gao LF et al. has illuminated the fibroblast-mediated effects of tumor bud-derived C–C chemokine ligand 5 (CCL5) on the TME, while the studies by Peng Z et al. have highlighted a diminished presence of natural killer (NK) cells and monocytes within iCAF-enriched clusters [36–39].

Recent studies have increasingly underscored the significant role of AA-related mechanisms in tumor regulation and the reprogramming of immune cells in CRC [40, 41]. In our research, we observed that AA significantly influences the formation of macrophage clusters, facilitating extensive interactions with tumor cells. Among the five identified macrophage clusters (TUBA1B + Mac-C1, DYNLL1 + Mac-C2, TUBA1C + Mac-C3, TUBB4B + Mac-C4, and HSP90AA1-Mac-C5), the TUBA1B + Mac-C1 cluster exhibited a strong correlation with tumor necrosis factor (TNF) signaling pathways. This cluster was also significantly associated with metabolic pathways, including the synthesis and degradation of ketone bodies, the citric acid cycle (TCA cycle), and glycolysis and gluconeogenesis. We propose that enhanced macrophage metabolism could accelerate cellular proliferation, driven by increased energy flux resulting from these metabolic processes [42–46].There is a growing recognition of the roles of CD8 + T cell cytotoxicity and exhaustion in advanced CRC [47, 48]. Interestingly, our research revealed that AA-related pathways significantly mediate interactions involving CD8 + T cells. We found that HSP90AA1 expression is associated with increased T cell exhaustion and a reduction in levels of macrophage migration inhibitory factor [47, 48]. Furthermore, the AA-related gene TUBA1B may elevate the expression of inflammatory cytokines such as IL-6, IL-7, CXCL1, and CXCL2, thereby promoting the activation of CAFs within the persistent CRC TME.

Collectively, our findings suggest that the AA-related genes HSP90AA1 and TUBA1B may contribute to both the cytotoxicity and exhaustion of CD8 + T cells. These genes appear to antagonize CD8 + T cell function and exhibit complex immunomodulatory patterns in the context of chronic infection and carcinogenesis, particularly when involving the patient's own T cells and fibroblasts. Targeting these molecular pathways or their corresponding receptors may offer promising strategies to mitigate the adverse effects of the TME on T cell functionality and to address failures in chronic infections and cancers [49–51].Considering the complicated communication of AA in TME cells, we comprehensively summed up the scores of these subclusters in the public bulk RNA-seq cohort in relation to prognosis and immune response [52]. Patients with a predominant TME cell AA exhibit a significant variance in CRC prognosis. Moreover, those undergoing ICB therapy, particularly CAFs and T cells, demonstrate notably diverse immune responses. This underscores the pivotal role of TME AA in CRC patients. This also provides an important breakthrough point for exploring the mechanisms of CRC development and lays the foundation for further clinical studies.

As an initial study, our analysis faced a primary limitation due to the insufficient number of samples in scRNA-seq and the extensive experimentation required to validate the accuracy of our findings. This constraint may have introduced bias into the clustering approach employed in our research. Nevertheless, scRNA-seq analysis continues to be instrumental in providing new insights into the characteristics of AA-related regulators within diverse monocyte populations in the TME. This effort aims to address the heterogeneity of CRC and represents a significant step toward clinical application.

Conclusions

Through single-cell sequencing analysis, we successfully identified and characterized unique cellular subtypes expressing TUBA1B and HSP90AA1 within TME cells. Our findings indicate that these specific manifestations of the autophagy-associated genes TUBA1B and HSP90AA1 enhance intercellular communication within the TME. This enhanced communication is critical for regulating both tumor proliferation and anti-tumor immune responses. These insights deepen our understanding of the intricate dynamics governing tumor growth and the interactions between the tumor and immune system in the TME.

Author contributions

QX, CL, CL, HW, SL, HY, ZL, KC, YX, RY, JZ, XY, JL and LW: Writing-Original Draft Preparation, LW: Writing-Review & Editing, Conceptualization, Formal Analysis, Supervision. All authors have read and agreed to the published version of the manuscript.

Funding

The study was approved by Dazhou Science and Technology Bureau project (21ZDYF0025, 21ZDYF0023), Sichuan Provincial Administration of Traditional Chinese Medicine project (2023MS141), and Sichuan Medical Association Project (S21048).

Data availability

scRNA-seq data from patients were retrieved from the GEO database. RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and GEO database.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

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

Qianping Xu, Chao Liu, Hailin Wang and Shujuan Li have contributed equally to this work.
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