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Construction and Verification of a Fibroblast-Related Prognostic Signature Model for Colon Cancer

ABSTRACT: Traditionally, cancer-associated fibroblasts (CAFs), an essential component of tumor microenvironment, were exert a crucial part in colon cancer progression. In this study, single-cell RNA-sequencing (scRNA-seq) data from 23 and bulk RNA-seq data from 452 colon cancer patients were extracted from the GEO database and TCGA-COAD and GEO databases, respectively. From single-cell analysis, 825 differentially expressed genes (DEGs) in CAFs were identified between each pair of six newly defined CAFs, named enCAF, adCAF, vaCAF, meCAF, erCAF, and cyCAF. Cell communication analysis with the iTALK package showed communication relationship between CAFs, including cell autocrine, cytokine, and growth factor subtypes, such as receptor-ligand pairs of TNFSF14-LTBR, IL6-F3, and IL6-IL6ST. Herein, we demonstrated the presence and prognostic value of adCAF and erCAF in colon cancer based on CIBERSORTx, combining single-cell marker genes and transcriptomics data. The prognostic significance of the enCAF and erCAF has been indirectly proved by both the correlation analysis with macrophages and CAFs, and the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiment based on 20 paired tumor samples. A prognostic model was constructed with 10 DEGs using the LASSO Cox regression method. The model was validated using two testing datasets, indicate a significant survival accuracy (p < 0.0025). Correlation analyses between clinical information, such as age, gender, tumor stage and tumor features (tumor purity and immune score), and risk scores revealed our CAF-related model’s robustness and excellent performance. Cell infiltration analysis by xCell revealed that the interaction between CAFs and multiple non-specific immune cells such as macrophages and the dendritic cell was a vital factor affecting immune score and prognosis. Finally, we analyzed how common anti-cancer drugs, including camptothecin, docetaxel and bortezomib, and immunotherapy, such as anti-PD-1 treatment, could be different in low-risk and high-risk patients inferred from our CAF-related model. In conclusion, the study utilized refined colon cancer fibroblast subsets and established the prognostic effects from the interaction with nonspecific immune cell.

SUBMITTER: Zhao Z

PROVIDER: S-EPMC9329609 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThough immunotherapy has become one of the standard therapies for colon cancer, the overall effective rate of immunotherapy is very low. Constructing an immune-related genes prognostic index (IRGPI) model may help to predict the response to immunotherapy and clinical outcomes.MethodsDifferentially expressed immune-related genes (DEIRGs) between normal tissues and colon cancer tissues were identified and used to construct the co-expression network. Genes in the module with the most significant differences were further analyzed. Independent prognostic immune-related genes (IRGs) were identified by univariate and multivariate cox regression analysis. Independent prognostic IRGs were used to construct the IRGPI model using the multivariate cox proportional hazards regression model, and the IRGPI model was validated by independent dataset. ROC curves were plotted and AUCs were calculated to estimate the predictive power of the IRGPI model to prognosis. Gene set enrichment analysis (GSEA) was performed to screen the enriched KEGG pathways in the high-risk and low-risk phenotype. Correlations between IRGPI and clinical characteristic, immune checkpoint expression, TMB, immune cell infiltration, immune function, immune dysfunction, immune exclusion, immune subtype were analyzed.ResultsTotally 680 DEIRGs were identified. Three independent IRGs,NR5A2, PPARGC1A and LGALS4, were independently related to survival. NR5A2, PPARGC1A and LGALS4 were used to establish the IRGPI model. Survival analysis showed that patients with high-risk showed worse survival than patients in the low-risk group. The AUC of the IRGPI model for 1-year, 3-year and 5-year were 0.584, 0.608 and 0.697, respectively. Univariate analysis and multivariate cox regression analysis indicated that IRGPI were independent prognostic factors for survival. Stratified survival analysis showed that patients with IRGPI low-risk and low TMB had the best survival, which suggested that combination of TMB and IRGPI can better predict clinical outcome. Immune cell infiltration, immune function, immune checkpoint expression and immune exclusion were different between IRGPI high-risk and low-risk patients.ConclusionAn immune-related genes prognostic index (IRGPI) was constructed and validated in the current study and the IRGPI maybe a potential biomarker for evaluating response to immunotherapy and clinical outcome for colon cancer patients.

Project description:BackgroundCuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.MethodsThe COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.ResultsThe high-risk group was associated with shorter overall survival (OS) time in COAD patients (p<0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p<0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR.ConclusionA ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.

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Construction and Verification of a Fibroblast-Related Prognostic Signature Model for Colon Cancer

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