Dataset Information

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

ABSTRACT:

Background

Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the in silico development of a CAF-related prognostic signature for CRC.

Methods

We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microenvironment Cell Population-counter, were used to calculate CAF abundance, while the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression algorithm was used to calculate the stromal score. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator algorithm were used to identify CAF-related genes and prognostic signatures.

Results

We identified a three-gene, prognostic, CAF-related signature and defined risk groups based on the Riskscores. Multidimensional validations were applied to evaluate the robustness of the signature and its correlation with clinical parameters. We utilized Tumor Immune Dysfunction and Exclusion (TIDE) and oncoPredict algorithms to predict therapy responses and found that patients in low-risk groups are more sensitive to immunotherapy and chemotherapy drugs such as 5-fluorouracil and oxaliplatin. Finally, we used the Cancer Cell Line Encyclopedia and Human Protein Atlas databases to evaluate the mRNA and protein levels encoded by the signature genes.

Conclusions

This novel CAF-related three-gene signature is expected to become a potential prognostic biomarker in CRC and predict chemotherapy and immunotherapy responses. It may be of considerable value for studying the tumor microenvironment in CRC.

SUBMITTER: Lv Y

PROVIDER: S-EPMC10583018 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

Lv Yiming Y Hu Jinhui J Zheng Wenqian W Shan Lina L Bai Bingjun B Zhu Hongbo H Dai Sheng S

Translational cancer research 20230922 9

<h4>Background</h4>Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the <i>in silico</i> development of a CAF-related prognostic signature for CRC.<h4>Methods</h4>We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microe ...[more]

PMID: 37859738

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Project description:BackgroundCurrent paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.MethodsThe GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.ResultsSix CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.ConclusionThe prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

Project description:Background Colorectal cancer (CRC) is one of the most common digestive system tumors worldwide. Hypoxia and immunity are closely related in CRC; however, the role of hypoxia-immune–related lncRNAs in CRC prognosis is unknown. Methods Data used in the current study were sourced from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases. CRC patients were divided into low- and high-hypoxia groups using the single-sample gene set enrichment analysis (ssGSEA) algorithm and into low- and high-immune groups using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Differentially expressed lncRNAs (DElncRNAs) between low- and high-hypoxia groups, low- and high-immune groups, and tumor and control samples were identified using the limma package. Hypoxia-immune–related lncRNAs were obtained by intersecting these DElncRNAs. A hypoxia-immune–related lncRNA risk signature was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. The tumor microenvironments in the low- and high-risk groups were evaluated using ssGSEA, ESTIMATE, and the expression of immune checkpoints. The therapeutic response in the two groups was assessed using TIDE, IPS, and IC50. A ceRNA network based on signature lncRNAs was constructed. Finally, we used RT-qPCR to verify the expression of hypoxia-immune–related lncRNA signatures in normal and cancer tissues. Results Using differential expression analysis, and univariate Cox and LASSO regression analyses, ZNF667-AS1, LINC01354, LINC00996, DANCR, CECR7, and LINC01116 were selected to construct a hypoxia-immune–related lncRNA signature. The performance of the risk signature in predicting CRC prognosis was validated in internal and external datasets, as evidenced by receiver operating characteristic curves. In addition, we observed significant differences in the tumor microenvironment and immunotherapy response between low- and high-risk groups and constructed a CECR7–miRNA–mRNA regulatory network in CRC. Furthermore, RT-qPCR results confirmed that the expression patterns of the six lncRNA signatures were consistent with those in TCGA-CRC cohort. Conclusion Our study identified six hypoxia-immune–related lncRNAs for predicting CRC survival and sensitivity to immunotherapy. These findings may enrich our understanding of CRC and help improve CRC treatment. However, large-scale long-term follow-up studies are required for verification.

Project description:Pancreatic cancer is a lethal malignancy with a 5-year survival rate of about 10% in the United States, and it is becoming an increasingly prominent cause of cancer death. Among pancreatic cancer patients, pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all cases and has a very poor prognosis with an average survival of only 1 year in about 18% of all tumor stages. In the past years, there has been an increasing interest in cancer-associated fibroblasts (CAFs) and their roles in PDAC. Recent data reveals that CAFs in PDAC are heterogeneous and various CAF subtypes have been demonstrated to promote tumor development while others hinder cancer proliferation. Furthermore, CAFs and other stromal populations can be potentially used as novel prognostic markers in cancer. In the present study, in order to evaluate the prognostic value of CAFs in PDAC, CAF infiltration rate was evaluated in 4 PDAC datasets of TCGA, GEO, and ArrayExpress databases and differentially expressed genes (DEGs) between CAF-high and CAF-low patients were identified. Subsequently, a CAF-based gene expression signature was developed and studied for its association with overall survival (OS). Additionally, functional enrichment analysis, somatic alteration analysis, and prognostic risk model construction was conducted on the identified DEGs. Finally, oncoPredict algorithm was implemented to assess drug sensitivity prediction between high- and low-risk cohorts. Our results revealed that CAF risk-high patients have a worse survival rate and increased CAF infiltration is a poor prognostic indicator in pancreatic cancer. Functional enrichment analysis also revealed that "extracellular matrix organization" and "vasculature development" were the top enriched pathways among the identified DEGs. We also developed a panel of 12 genes, which in additional to its prognostic value, could predict higher chemotherapy resistance rate. This CAF-based panel can be potentially utilized alone or in conjunction with other clinical parameters to make early predictions and prognosticate responsiveness to treatment in PDAC patients. Indeed, it is necessary to conduct extensive prospective investigations to confirm the clinical utility of these findings.

Dataset Information

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

Background

Methods

Results

Conclusions

Publications

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

Similar Datasets

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