
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251710
71317
10.1038/s41598-024-71317-2
Article
Development of a prognostic model for NSCLC based on differential genes in tumour stem cells
Ma Yuqi 1
Li Jiawei 1
Xiong Chunping 2
Sun Xiaoluo 1
Shen Tao st@cdutcm.edu.cn

1
1 https://ror.org/00pcrz470 grid.411304.3 0000 0001 0376 205X School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
2 https://ror.org/00pcrz470 grid.411304.3 0000 0001 0376 205X Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
9 9 2024
9 9 2024
2024
14 2093813 1 2024
27 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/.
Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

Keywords

NSCLC
Single cell
Machine learning
Prognosis
Subject terms

Cancer models
Tumour biomarkers
Cancer stem cells
http://dx.doi.org/10.13039/501100016350 Sichuan Provincial Administration of Traditional Chinese Medicine No.2023MS399 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Lung cancer stands as the primary cause of worldwide cancer-related fatalities, accounting for over 1.8 million annual deaths attributed to lung cancer1. Approximately 80–85% of all lung cancers are attributed to NSCLC. Among individuals initially diagnosed with locally advanced or metastatic disease, nearly 70% will receive an NSCLC diagnosis2.

The main strategy for managing NSCLC involves chemotherapy. Platinum-based medications are regarded as the first-line standard, as endorsed by ASCO and NCCN. Employing platinum-based drugs, either in isolation or in conjunction with other chemotherapeutic substances, generally results in an average overall survival of 8–10 months for patients3. The amalgamation of tyrosine kinase inhibitors and chemotherapy, such as platinum-based chemotherapy combined with osimertinib, in individuals with epidermal growth factor receptor-positive NSCLC, results in extended remission periods4. However, there are some limitations to these treatments: immunotherapy is not available in all countries, and only about 15% of NSCLC patients have EGFR gene mutations5.

Cisplatin (DDP) is a primary chemotherapeutic agent in the treatment of lung cancer, serving as a cornerstone in therapy. While it proves effective, the development of resistance presents a significant challenge, promoting tumor progression and causing side effects6,7. Acquired chemoresistance is a major hurdle facing clinicians and contributes to treatment failure8. Therefore, managing cisplatin resistance is a critical factor in improving the prognosis of patients with NSCLC.

The swift progress of next-generation sequencing technologies has spurred a growing body of research employing RNA-seq to explore gene expression in NSCLC9,10. ScRNA-seq is an innovative sequencing technology capable of providing detailed characterisation information for individual tumour cells11–13. Developments in the field of chemotherapy resistance have been made in recent years by tumour stem cell researchers14,15. These techniques have significant implications for the construction of prognostic models for NSCLC16–18.

In this study, we identified the differential genes in SOX2 tumour cells using the NSCLC scRNA-seq dataset from GEO and the Kuruven Functional Epigenetics Laboratory database. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. We also validated the SLC2A1 gene through in vitro experiments, demonstrating its distinct expression patterns in A549 cells and A549/DDP cells. Subsequent CCK8, Transwell, Western blot and apoptosis experiments showed that silencing the SLC2A1 gene not only reduced the invasion, migration and proliferative capacity of A549 cells, but also attenuated their resistance to various concentrations of cisplatin. SLC2A1 is emerging as a promising prognostic marker and potential therapeutic target, providing valuable insights to guide clinical treatment decisions. The flow chart of this study is shown in Fig. 1.Fig. 1 Flowchart.

Methods

Data source and preprocessing

We acquired scRNA-seq data from 110 NSCLC patients from GEO database and the Kuruven Functional Epigenetics Laboratory. The datasets used included GSE148071, KU_loom, GSE131907, GSE136246 and GSE127465. Details of scRNA-seq data are provided in Attachment 1. After filtering, the mitochondrial content was constrained to less than 20%, resulting in a final count of 218379 cells. Bulk RNA-seq data, mutation data and clinicopathological features of LUAD were downloaded from the TCGA database and used as training sets.

ScRNA‑seq

First, we checked the scRNA-seq data using the “Seurat” R package19 to ensure data quality. Subsequently, we normalised the merged data by log-normalisation and found the top 2000 highly variable genes (identifying variable features based on the variance stabilisation transform (“vst”)) using the FindVariableFeatures function. Meanwhile, all genes were scaled using the ScaleData function, and RunPCA function was used to reduce the dimension of PCA for the first 2000 highly variable genes screened above. Based on the Elbow plots (Attachment 2), we will identify the PC cutoff point, responsible for less than 5% of the variance yet accumulating 90% of the total variance, as the inflection point of the curve. We selected the parameter “PC” to 17 and clustered the cells through the “FindNeighbors” and “FindClusters” functions (resolution = 0.3) to find the cell clusters. Then, using the UMAP approach for visualization, we further reduced the dimensionality by choosing the first 17 principal components. UMAP is a method of data dimensionality reduction, which assumes that the available data samples are uniformly distributed in the topological space (Manifold), and these limited data samples can be approximated (Approximation) and mapped (Projection) to a low-dimensional space. Finally, we screened 28 marker gene subgroups using the FindAllMarkers function with logFC = 0.25 (differential multiples) and Minpct = 0.25 (the expression ratio of the least differential genes).

Prognostic model construction and validation

The DEGs extracted in SOX2_cancer formed the basis of our model construction. To improve the accuracy of the model, we implemented a novel strategy. First, we discovered that 23 mRNAs had a significant association with the prognosis of NSCLC patients by univariate Cox regression analysis (Attachment 3). Then, we designated TCGA-LUAD as our training set and GSE13213 as the validation set. We used ten machine learning methods—ridge regression, least absolute shrinkage and selection operator (Lasso) regression, stepwise Cox regression, CoxBoost, random survival forest (RSF), elastic network (Enet), plsRcox, supervised principal component (SuperPC), survival support vector machine (SVM) and gradient boosting machine (GBM) (The details of each method can be found in Attachment 4a). These methods were combined into 101 unique combinations for variable selection and model formulation. Finally, we evaluated the effectiveness of our models on the training and validation datasets using a consistency index (C-index) and then calculated the average scores. The C-index runs from 0 to 1, with values closer to 1 indicating stronger prediction performance. The optimal choice was the ‘ StepCox[both] + RSF’ model. For results on RSF and StepCox (both) see Attachment 4b,c.

Using the ‘survminer’ package in R, we performed Kaplan-Meier (KM) curve analyses on the TCGA-LUAD and the two GEO datasets to explore the differences in overall survival (OS) between high- and low-risk cohorts of LUAD patients. Using the ‘timeROC’ package, we generated one-, three- and five-year receiver operating characteristic (ROC) curves for risk scores from TCGA-LUAD and the two GEO datasets, and then calculated the area under the ROC curve (AUC). In addition, we designed a nomogram combining age, gender, stage, and risk to maximise predictive accuracy.

Cell lines culture

A549 cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS, Thermo). To establish drug-resistant cell lines, A549 cells underwent exposure to escalating concentrations of cisplatin in complete cell culture medium. Initially, A549 cells were seeded in cell culture dishes. Subsequently, cisplatin was administered to A549 cells at initial concentrations of 0, 5, 15, and 20 µg/ml for 48 h. At each iteration, the drug concentration was increased by 1.5 times the previous concentration. This sequential process persisted until the cells demonstrated steady proliferation without significant cell death. The entire procedure was reiterated to instigate resistance to 5, 15, and 20 μg/ml cisplatin.

RT-qPCR

Total RNA extraction from tissues or cell lines was performed using TRIzol (15596026, Invitrogen life technologies) following the manufacturer’s instructions (R0016, Beyotime). All primers utilized were provided by Beyotime (Shanghai, China), and detailed primer sequences can be found in Attachment 5.

Western blot assay

Tissue blocks were placed in a homogeniser (G2002-100ML, Servicebio) and rinsed 2–3 times with pre-cooled PBS to remove blood. For membrane transfer, a constant current of 300 mA was applied for 30 min and the membrane transfer device was placed in ice water for cooling during the process. The transferred membrane was immersed in a tank containing TBST (G2150-1L, Servicebio), quickly rinsed and then covered with protein-free blocking solution. The primary antibody was diluted and prepared according to the antibody instructions. The blocking solution was poured off and the prepared primary antibody (G2025-100ML, Servicebio) was added. TBST was then added for rapid elution on a decolourising shaker for 5 min each and washed three times. The secondary antibody (G2009-100ML, Servicebio) was diluted 1:5000 with TBST, added to the incubator.

Apoptosis assay

When the cell adherent growth density reached 80–90%, the cell culture medium was removed and 2 mL of PBS was added for two washes. After 5 min of digestion, most of the digestion solution was discarded and the remaining digestion solution was continued for a further 1–2 min. Under the microscope, cells were observed to be completely detached from the cell wall and, as far as possible, dispersed into single cells. The counted cells were then diluted with 1640 complete medium and inoculated into 6-well plates at a density of 6 × 104 cells/1 ml, with 4 ml in each well. After 24 h of culture, the original complete medium was discarded. The experiment was divided into three groups: A549 control, NC-siSLC2A1 and siSLC2A1. Cells were collected after a further 24 h of culture. The cells were then connected to a flow cytometer (Beckman, A00-1-1102) for detection.

Transfection

The A549 and A549/DDP cells were infected with SLC2A1 knockout lentivirus (L30951, Beyotime, China) according to the manufacturer’s protocols, resulting in knockout of the target gene.

Transwell assay

The Transwell assay encompassed both cell migration and invasion assessments. To assess the invasive and migratory capacity of the cells, the upper part of the plate was coated with Matrigel solution (BD Biosciences, USA). After incubation, quantification was performed under a light microscope.

CCK8 assay

When the cells reached 80–90% confluence, the cells were washed twice with 2 mL of PBS (C0221A, Beyotime). After discarding the PBS, 2 mL of digestion solution containing 0.25% trypsin with 0.02% EDTA (H0518, HAKATA) was added for digestion. The remaining digestion solution was digested for a further 1–2 min. Cell separation was confirmed under the microscope, ensuring complete detachment from the cell wall and obtaining single cells. Cells were counted using a cell counting plate. After counting, the cells were diluted with 1640 complete medium (G4531-500ML, Servicebio). After 24 h of culture, the original complete medium was discarded. The experiment was performed in three groups: A549 control group, NC-siSLC2A1 and si-SLC2A1. An enzyme-linked immunosorbent assay was then used to measure the optical density (OD) at 450 nm.

Statistical analysis

Experimental data were analysed using R software and GraphPad Prism software. Results from three independent experiments are presented as mean ± standard deviation (SD). Student’s t-test was used for comparisons between groups, with significance levels indicated as *P < 0.05, **P < 0.01, ***P < 0.001.

Result

ScRNA data analysis

To explore the characterisation of distinct cell populations in NSCLC, we collected scRNA-seq profiles from a total of 218,379 cells. A comprehensive collection of 218,379 cells from 110 tumour tissues was obtained and classified into 23 distinct cell clusters, as shown in Fig. 2a. The cell distribution remained relatively consistent across each sample, indicating the absence of significant batch effects and making the samples suitable for subsequent analysis. Nine cell clusters, namely T cells, B cells, tumour cells, plasma cells, myeloid cells, mast cells and epithelial cells, were identified using marker genes (Fig. 2b). The first level of annotation of the different clusters was performed using the DISCO website20 (https://www.immunesinglecell.org/atlas/lung) as well as reference and cell marker databases. According to the definition of tumour cells by Karolina Hanna Prazanowska et al.21, We performed secondary annotation of tumour cells and categorised 24,798 tumour cells into seven types: Alveolar, SOX2 tumour cells, LAMC2 tumour cells, CDKN2A tumour cells, CXCL1 tumour cells, proliferating tumour cells and pathological tumour cells (Fig. 2e).Fig. 2 (a-c) Clustering annotation and cell type identification by UMAP. (d-e) Secondary clustering and annotation of tumour cells. (f) stemness score of tumour cells.

Identification of relevant differential genes in NSCLC

After quality control of the single cell data, we re-clustered the tumour cells for further analysis. Annotation information for tumour cells is in Attachment 6. Based on the definition of lung tumour stem cells proposed by Gemma Leon et al.22, We found that the SOX2 tumour cells had stronger tumour stemness (Fig. 2f). Finally, we extracted 162 differential genes from SOX2_cancer (Attachment 7).

Validation of the prognostic model

We performed univariate Cox analysis on the DEGs expression profile of SOX2_cancer and identified 23 prognostic genes (Attachment 8). Subsequently, we developed 101 combinatorial machine learning models using the TCGA-LUAD cohort as a training set and GSE29016 as a validation set, and evaluated their performance using the C-index. The results showed that the combination of ‘StepCox[both] + RSF’ had the highest average C-index (0.796) (Fig. 3a). The relative importance of the model genes is shown in Attachment 9.Fig. 3 C-index of each model on all validation datasets. (a) A total of 101 predictive models were built by the tenfold cross-validation framework and the C-index of each model was further calculated on all validation datasets. (b) Kaplan-Meier and ROC curves for TCGA-LUAD. (c-d) Kaplan-Meier and ROC curves for GSE29016 and GSE13213. (e) Nomograms constructed on the basis of clinical characteristics including age, graders and stages.

For the training set, K-M curve analysis showed that patients in the high-risk group had a worse prognosis (P < 0.0001). The predictive ability of the model was assessed using ROC curve analysis, and the AUC values and 95% confidence intervals (CIs) for predicting 1-, 3-, and 5-year OS were 0.92, 0.98, and 0.98, respectively (Fig. 3b). For the two validation sets (GSE29016, GSE13213), the AUC and 95% CI for predicting 1-, 3-, and 5 year OS were 0.77, 0.64, 0.62, and 0.60, 0.63, 0.68, respectively (Fig. 3c,d). To improve the clinical utility of the model, a nomogram was constructed based on our risk scores for prognostic characteristics and other clinicopathological indicators of patients to provide a more comprehensive prediction of patient OS (Fig. 3e).

Mutation landscape

We created two waterfall plots to scrutinize detailed gene mutation profiles between high- and low-risk populations. TP53, CSMD3 and MUC16 were identified as the most frequently mutated genes in both groups (Fig. 4a,b). In addition, the risk of mutations was higher in the high-risk group (Fig. 4c). These mutation-prone genes suggest an association with immune escape.Fig. 4 (a-b) Waterfall plot of gene mutations. (c) Tumour Mutation Burden (TMB) risk.

Immune infiltration analysis

We used heat maps to evaluate the degree of immune cell infiltration in each patient in order to obtain a more comprehensive understanding of the features of tumour-infiltrating immune cells in the high- and low-risk groups. Our analysis revealed that immune cell infiltration was generally elevated in the high-risk group compared to the low-risk group. Specifically, in the high-risk group, there was a notable increase in NK cell and T cell infiltration (Fig. 5a,b). In addition, we found that B cells, NK cells and CD8+Tcells were more prominently expressed in LUAD (Fig. 5c). This finding suggested that there may be an immune escape in the tumour microenvironment that reduced the cytotoxicity of NK cells.Fig. 5 (a-b) Heat map of immune cell infiltration. (c) Distribution and correlation of tumour infiltrating immune cells in LUAD and LUSC.

GSEA

We explored potential mechanisms underlying the poor prognosis of NSCLC patients in the high-risk group. The comparison revealed the high-risk group was significantly enriched in ribosome production and DNA replication. This suggested that tumour cells in the high-risk group have more active ribosome function and robust DNA damage repair mechanisms to cope with cell division and the stress of chemotherapy in vitro. This analysis provides insight into the molecular pathways associated with NSCLC prognosis (Fig. 6).Fig. 6 GSEA.

Cell–cell communication

The ‘Cellchat’ package was used to investigate cell communication. We present the number, intensity and signals emitted by each of the seven critical cell interactions within tumour cells (Fig. 7b,c). The results showed that cellular communication occurred more frequently in SOX2 tumour cells, Alveolar, LAMC2 tumour cells and pathological tumour cells. In addition, Fig. 7a showed that MK, SPP1 and GEF serve as the primary signalling afferent modes in SOX2 tumour cells.Fig. 7 Seven key cellular interactions within tumour cells. (a) Efferent and afferent signalling patterns of tumour cells. (b) Signals from various types of tumour cells. (c) The number and strength of 7 types of cell interactions.

Drug susceptibility analysis

To guide the development of clinical treatment strategies, we screened 11 mainstream agents based on the CCLE and DGSC databases. The box plot showed that AEW541, AZD6244, L.685458, nilotinib, panobinostat, PD.0332991, PLX4720, RAF265, sorafenib and TKI258 had higher scores in the low-risk group (Fig. 8a). Conversely, cisplatin scores were higher in the high-risk group (Fig. 8b), suggesting that cisplatin may be more appropriate for patients with lower risk scores. These findings not only provide valuable insights into the selection of appropriate chemotherapeutic agents based on the risk scores of patients, but also help to guide clinical treatment decisions.Fig. 8 Drug sensitivity analysis.

Validation of the expression and function of SLC2A1

SLC2A1 exhibited the most pronounced disparity between normal and tumor cells of all cancers (Attachment 10), and SLC2A1 was positively associated with poor prognosis (Attachment 11). However, there has been little research on the biological role of SLC2A1 in NSCLC. Subsequently, we performed a sequence of examinations to elucidate the role of SLC2A1 in NSCLC. In addition, we observed increased SLC2A1 expression in A549/DDP cells compared to A549 cells (Fig. 9).Fig. 9 (a-b) qPCR and WB showing SLC2A1 expression in A549/DDP cell line compared to A549 cell line.

Transwell assay revealed that A549 cells exhibited reduced migration and invasion following SLC2A1 knockdown compared to baseline (Fig. 10b), suggesting that SLC2A1 knockdown attenuated the migratory and invasive capacities of the A549 cell line. CCK-8 proliferation showed a decrease in the proliferative capacity of A549 cells after SLC2A1 knockdown (Fig. 10a). In addition, the apoptosis assay showed a significant increase in the apoptosis rate of A549 cells following SLC2A1 knockdown (Fig. 11a). These differences were statistically significant.Fig. 10 (a) Proliferation of CCK-8. (b) Transwell assay. (c) Expression of SLC2A1 in Transwell assay.

Fig. 11 (a) Apoptosis assay. (b) Cytotoxicity of CCK-8.

Resensitising A549 cells to cisplatin

Cisplatin was divided into four concentration gradients (0, 5, 15, 20 µg/ml). A549/DDP cells were divided into 12 groups, with each concentration corresponding to the A549/DDP control group, NC-siSLC2A1 group and siSLC2A1 group. The CCK8 assay showed that knockdown of SLC2A1 resulted in decreased survival of A549/DDP cells, suggesting that knockdown of SLC2A1 could effectively raise cisplatin sensitivity in A549/DDP cells (Fig. 11b).

Discussion

NSCLC stands as one of the most prevalent and lethal cancers globally. Cisplatin, used as a standard adjuvant chemotherapy for NSCLC patients, faces a significant hurdle in its clinical application due to the formidable challenge of chemoresistance23. scRNA-seq has become an important tool for classifying tumour cells24,25. Specifically expressed gene markers were used to identify cell subpopulations across a large number of samples26. Therefore, a thorough analysis of both bulk RNA-seq and scRNA-seq data was performed to establish a prognostic model.

First, NSCLC scRNA-seq datas from the GEO database and the Kuruven Functional Epigenetics Laboratory were used to delineate tumour cells and their subpopulations. We collected scRNA-seq data from a total of 218,379 cells. We then performed secondary annotation of tumour cells and classified 24,798 tumour cells into seven types: Alveolar, SOX2 tumour cells, LAMC2 tumour cells, CDKN2A tumour cells, CXCL1 tumour cells, proliferating tumour cells and pathological tumour cells. Consistent with the definition of lung tumour stem cells proposed by Gemma Leon et al., our results indicate that SOX2 tumour cells exhibit strong stemness22. Cellular communication analyses revealed that MK, SPP1 and GEF as afferent signalling pathways had the greatest impact on stemness in SOX2 tumour cells. Experimental evidence suggests that these signalling pathways are associated with drug resistance in A549 cells27–29. KEGG analysis showed that the high-risk and low-risk groups differed significantly in several pathways. In particular, genes involved in ribosome production and DNA replication were significantly enriched in the high-risk group. This suggests that tumour cells in the high-risk group have more active ribosome function and robust DNA damage repair mechanisms to cope with cell division and the stress of chemotherapy, in line with Chunlan Tang et al.30.

Second, we assessed TMB and immune infiltration from various angles by categorizing all samples based on the computed risk scores. Notably, the mutation rates of TP53, CSMD3, and MUC16 showed an upward trend with increasing risk scores. Previous research has established that mutations in these genes are linked to immune escape31–34. Furthermore, the elevated level of NK cell infiltration in the high-risk group suggests a potential scenario of immune escape.

Third, using the CCLE and DGSC databases, 11 mainstream agents were identified based on the developed prognostic models. Notably, higher cisplatin scores were observed in the high-risk group, suggesting that cisplatin may be more appropriate for patients with lower risk scores.

Finally, SLC2A1 displayed the most significant difference between normal and tumor cells of all cancers in the prognostic model Additionally, SLC2A1 showed a positive correlation with a poor prognosis. Nevertheless, there has been a scarcity of study about the biological function of SLC2A1 in NSCLC. Afterwards, we conducted a series of studies to clarify the function of SLC2A1 in NSCLC. The results of vitro experiments demonstrated that suppressing the expression of SLC2A1 had a significant effect on the activity, invasion, and migratory ability of NSCLC cells, and reduced the resistance to cisplatin. It provides more evidence that SLC2A1 is involved in the development of NSCLC. Previous research has shown that SLC2A1 has a role in several different types of cancer. The expression of SLC2A1 was increased significantly in colorectal adenocarcinoma, and its levels were shown to be associated with unfavorable tumor histology, advanced stage, liver metastasis, and reduced survival rates35. Coptis chinensis Franch and Zingiber officinale Roscoe decreased the proliferation, migration, invasion of SGC7901/MFC gastric cells, and in turn, repressed carcinogenesis by controlling glucose metabolism via regulation of LDHA and SLC2A1 genes36. In our study, SLC2A1 was also found to be a potential target for NSCLC.

Conclusions

In conclusion, our findings underscored the precision and reliability of the NSCLC prognostic model. Furthermore, the compelling evidence from our in vitro experiments supported the involvement of SLC2A1 in cisplatin resistance in the A549/DDP cells. These results offer valuable insights into the potential development of novel therapeutic strategies for NSCLC.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information 3.

Supplementary Information 4.

Supplementary Information 5.

Supplementary Information 6.

Supplementary Information 7.

Supplementary Information 8.

Supplementary Information 9.

Supplementary Information 10.

Supplementary Information 11.

Supplementary Information 12.

Abbreviations

NSCLC Non-small cell lung cancer NSCLC

scRNA-seq Single-cell RNA sequencing

GEO Gene expression omnibus

TCGA The cancer genome atlas

OS Overall survival

ROC Receiver operating characteristic

DEGs Differentially expressed genes

RFS Random forest

AUC Area under the curve

GSEA Gene set enrichment analysis

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71317-2.

Acknowledgements

We thank the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) Database and the Ku Leuven Laboratory for Functional Epigenetics for sharing a large amount of data.

Author contributions

TS and YQM conceived and designed the study. YQM and XLS drafted the manuscript and analysed the data. YQM and JWL formatted the images and the article. CPX and JWL complete the cell experiment. YQM and TS reviewed the data. All authors read and approved the final manuscript.

Funding

This work was supported by the Sichuan Provincial Administration of Traditional Chinese Medicine (No.2023MS399).

Data availability

We thank the TCGA and GEO databases for providing data. Single-cell data can be obtained by searching the official website according to the data number(s).TCGA: https://portal.gdc.cancer.gov/, GEO: https://www.ncbi.nlm.nih.gov/geo/. The dataset named KU_loom is available at (https://gbiomed.kuleuven.be/scRNAseq-NSCLC). The raw data of the in vitro experiments and the code of the R software can be obtained by contacting the corresponding author.

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.

These authors contributed equally: Yuqi Ma, Jiawei Li, Chunping Xiong and Xiaoluo Sun.
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