Weighted Gene Co-expression Network Analysis Identifies CALD1 as a Biomarker Related to M2 Macrophages Infiltration in Stage III and IV Mismatch Repair-Proficient Colorectal Carcinoma

Immunotherapy has achieved efficacy for advanced colorectal cancer (CRC) patients with a mismatch-repair-deficient (dMMR) subtype. However, little immunotherapy efficacy was observed in patients with the mismatch repair-proficient (pMMR) subtype, and hence, identifying new immune therapeutic targets is imperative for those patients. In this study, transcriptome data of stage III/IV CRC patients were retrieved from the Gene Expression Omnibus database. The CIBERSORT algorithm was used to quantify immune cellular compositions, and the results revealed that M2 macrophage fractions were higher in pMMR patients as compared with those with the dMMR subtype; moreover, pMMR patients with higher M2 macrophage fractions experienced shorter overall survival (OS). Subsequently, weighted gene co-expression network analysis and protein–protein interaction network analysis identified six hub genes related to M2 macrophage infiltrations in pMMR CRC patients: CALD1, COL6A1, COL1A2, TIMP3, DCN, and SPARC. Univariate and multivariate Cox regression analyses then determined CALD1 as the independent prognostic biomarker for OS. CALD1 was upregulated specifically the in CMS4 CRC subtype, and single-sample Gene Set Enrichment Analysis (ssGSEA) revealed that CALD1 was significantly correlated with angiogenesis and TGF-β signaling gene sets enrichment scores in stage III/IV pMMR CRC samples. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm and correlation analysis revealed that CALD1 was significantly associated with multiple immune and stromal components in a tumor microenvironment. In addition, GSEA demonstrated that high expression of CALD1 was significantly correlated with antigen processing and presentation, chemokine signaling, leukocyte transendothelial migration, vascular smooth muscle contraction, cytokine–cytokine receptor interaction, cell adhesion molecules, focal adhesion, MAPK, and TGF-beta signaling pathways. Furthermore, the proliferation, invasion, and migration abilities of cancer cells were suppressed after reducing CALD1 expression in CRC cell lines. Taken together, multiple bioinformatics analyses and cell-level assays demonstrated that CALD1 could serve as a prognostic biomarker and a prospective therapeutic target for stage III/IV pMMR CRCs.

Download Full-text

INHBB Is a Novel Prognostic Biomarker Associated with Cancer-Promoting Pathways in Colorectal Cancer

BioMed Research International ◽

10.1155/2020/6909672 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jinpeng Yuan ◽

Aosi Xie ◽

Qiangjian Cao ◽

Xinxin Li ◽

Juntian Chen

Keyword(s):

Colorectal Cancer ◽

Signaling Pathways ◽

Signaling Pathway ◽

Transforming Growth Factor ◽

Cox Regression ◽

Prognostic Biomarker ◽

Disease Free Survival ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Receptor Interaction

Background. Inhibin subunit beta B (INHBB) is a protein-coding gene that participated in the synthesis of the transforming growth factor-β (TGF-β) family members. The study is aimed at exploring the clinical significance of INHBB in patients with colorectal cancer (CRC) by bioinformatics analysis. Methods. Real-time PCR and analyses of Oncomine, Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases were utilized to evaluate the INHBB gene transcription level of colorectal cancer (CRC) tissue. We evaluated the INHBB methylation level and the relationship between expression and methylation levels of CpG islands in CRC tissue. The corresponding clinical data were obtained to further explore the association of INHBB with clinical and survival features. In addition, Gene Set Enrichment Analysis (GSEA) was performed to explore the gene ontology and signaling pathways of INHBB involved. Results. INHBB expression was elevated in CRC tissue. Although the promoter of INHBB was hypermethylated in CRC, methylation did not ultimately correlate with the expression of INHBB. Overexpression of INHBB was significantly and positively associated with invasion depth, distant metastasis, and TNM stage. Cox regression analyses and Kaplan-Meier survival analysis indicated that high expression of INHBB was correlated with worse overall survival (OS) and disease-free survival (DFS). GSEA showed that INHBB was closely correlated with 5 cancer-promoting signaling pathways including the Hedgehog signaling pathway, ECM receptor interaction, TGF-β signaling pathway, focal adhesion, and pathway in cancer. INHBB expression significantly promoted macrophage infiltration and inhibited memory T cell, mast cell, and dendritic cell infiltration. INHBB expression was positively correlated with stromal and immune scores of CRC samples. Conclusion. INHBB might be a potential prognostic biomarker and a novel therapeutic target for CRC.

Download Full-text

APLN: A potential novel biomarker for cervical cancer

Science Progress ◽

10.1177/00368504211011341 ◽

2021 ◽

Vol 104 (2) ◽

pp. 003685042110113

Author(s):

Yusha Chen ◽

Xiaoqian Lin ◽

Jinwen Zheng ◽

Jiancui Chen ◽

Huifeng Xue ◽

...

Keyword(s):

Cervical Cancer ◽

Cox Regression ◽

Clinical Stage ◽

Mapk Signaling ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Expression ◽

Receptor Interaction ◽

Primary Therapy ◽

Advanced Clinical Stage

Apelin (APLN) is recently demonstrated a direct association with many malignant diseases. However, its effects on cervical cancer remain unclear. This study therefore aims to evaluate the association between APLN expression and cervical cancer using publicly available data from The Cancer Genome Atlas (TCGA). The Pearson χ2 test and Fish exact test, as well as logistic regression, were used to evaluate the relationship between clinicopathological factors in cervical cancer and the expression of APLN. Additionally, the Cox regression and Kaplan-Meier methods were conducted to analyze the Overall Survival (OS) of cervical cancer patients in TCGA. Finally, gene set enrichment analysis (GSEA) was performed to establish its biological functions. High expression of APLN in cervical cancer was significantly associated with a more advanced clinical stage (OR = 1.91 (1.21–3.05) for Stage II, Stage III, and Stage IV vs Stage I, p = 0.006). Additionally, it was associated with poor outcome after primary therapy (OR = 2.14 (1.03–4.59) for Progressive Disease (PD), Stable Disease (SD), and Partial Response (PR) vs Complete Remission (CR), p = 0.045) and high histologic grade (OR = 1.67 (1.03–2.72) for G3 and G4 vs G1 and G2, p = 0.037). Moreover, multivariate analysis showed that high expression of APLN was associated with a shorter OS. GSEA demonstrated that six KEGG pathways, including PPAR signaling, ECM-receptor interaction, focal adhesion, MAPK signaling, TGF-beta signaling, and Gap junction pathways were differentially enriched in the high expression APLN phenotype. The recent study suggests that APLN plays an important role in the progression of cervical cancer and might be a promising prognostic biomarker of the disease.

Download Full-text

Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

Download Full-text

Bioinformatics and Gene Network Analyses of the Swine Mammary Gland Transcriptome during Late Gestation

Bioinformatics and Biology Insights ◽

10.4137/bbi.s12205 ◽

2013 ◽

Vol 7 ◽

pp. BBI.S12205 ◽

Cited By ~ 9

Author(s):

Wangsheng Zhao ◽

Khuram Shahzad ◽

Mingfeng Jiang ◽

Daniel E. Graugnard ◽

Sandra L. Rodriguez-Zas ◽

...

Keyword(s):

Network Analysis ◽

Receptor Binding ◽

Gene Network ◽

Fatty Acid Biosynthesis ◽

Mammary Development ◽

Late Gestation ◽

Interleukin 4 ◽

Receptor Interaction ◽

Bioinformatics Analyses ◽

Network Analyses

We used the newly-developed Dynamic Impact Approach (DIA) and gene network analysis to study the sow mammary transcriptome at 80, 100, and 110 days of pregnancy. A swine oligoarray with 13,290 inserts was used for transcriptome profiling. An ANOVA with false discovery rate (FDR < 0.15) correction resulted in 1,409 genes with a significant time effect across time comparisons. The DIA uncovered that Fatty acid biosynthesis, Interleukin-4 receptor binding, Galactose metabolism, and mTOR signaling were among the most-impacted pathways. IL-4 receptor binding, ABC transporters, cytokine-cytokine receptor interaction, and Jak-STAT signaling were markedly activated at 110 days compared with 80 and 100 days. Epigenetic and transcription factor regulatory mechanisms appear important in coordinating the final stages of mammary development during pregnancy. Network analysis revealed a crucial role for TP53, ARNT2, E2F4, and PPARG. The bioinformatics analyses revealed a number of pathways and functions that perform an irreplaceable role during late gestation to farrowing.

Download Full-text

Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

Journal of Healthcare Engineering ◽

10.1155/2022/3912697 ◽

2022 ◽

Vol 2022 ◽

pp. 1-8

Author(s):

Benzhuo Zhang ◽

Wei Huang ◽

Mingquan Yi ◽

Chunxu Xing

Keyword(s):

Network Analysis ◽

Cerebral Infarction ◽

Antigen Processing ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Neutrophil Activation ◽

Receptor Interaction ◽

Ppi Network ◽

Geo Database

Atherosclerotic cerebral infarction (ACI) seriously threatens the health of the senile patients, and the strategies are urgent for the diagnosis and treatment of ACI. This study investigated the mRNA profiling of the patients with ischemic stroke and atherosclerosis via excavating the datasets in the GEO database and attempted to reveal the biomarkers and molecular mechanism of ACI. In this study, GES16561 and GES100927 were obtained from Gene Expression Omnibus (GEO) database, and the related differentially expressed genes (DEGs) were analyzed with R language. Furthermore, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Besides, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 133 downregulated DEGs and 234 upregulated DEGs were found in GES16561, 25 downregulated DEGs and 104 upregulated DEGs were found in GSE100927, and 6 common genes were found in GES16561 and GES100927. GO enrichment analysis showed that the functional models of the common genes were involved in neutrophil activation, neutrophil degranulation, neutrophil activation, and immune response. KEGG enrichment analysis showed that the DEGs in both GSE100927 and GSE16561 were connected with the pathways including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Phagosome, Antigen processing and presentation, and Staphylococcus aureus infection. The PPI network analysis showed that 9 common DEGs were found in GSE100927 and GSE16561, and a cluster with 6 nodes and 12 edges was also identified by PPI network analysis. In conclusion, this study suggested that FCGR3A and MAPK pathways were connected with ACI.

Download Full-text

Identification of a Hypoxia - Related lncRNA Signature For The Prognosis of Colorectal Cancer

10.21203/rs.3.rs-757504/v1 ◽

2021 ◽

Author(s):

HUA HUANG ◽

Shanshan Xu ◽

Youran Li ◽

Yunfei Gu ◽

Lijiang Ji

Keyword(s):

Colorectal Cancer ◽

Roc Analysis ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Receptor Interaction ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Gene Set

Abstract Background: Colorectal cancer (CRC), the commonly seen malignancy, ranks the 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long coding RNAs (lncRNAs) have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods: The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which were later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results: Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression, and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion: To conclude , we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic target.

Download Full-text

The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-021-89747-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lunxu Li ◽

Shilin Xia ◽

Xueying Shi ◽

Xu Chen ◽

Dong Shang

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Risk Score ◽

Cox Regression ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Immune Related Genes

AbstractHepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

Download Full-text

Elevated limb-bud and heart development (LBH) expression indicates poor prognosis and promotes gastric cancer cell proliferation and invasion via upregulating Integrin/FAK/Akt pathway

PeerJ ◽

10.7717/peerj.6885 ◽

2019 ◽

Vol 7 ◽

pp. e6885 ◽

Cited By ~ 6

Author(s):

Ruoxi Yu ◽

Zhi Li ◽

Chuang Zhang ◽

Huicong Song ◽

Mingming Deng ◽

...

Keyword(s):

Gastric Cancer ◽

Heart Development ◽

Prognostic Biomarker ◽

Gene Set Enrichment Analysis ◽

Limb Bud ◽

Akt Pathway ◽

Receptor Interaction ◽

Cox Regression Analysis ◽

Public Datasets ◽

Proliferation And Invasion

The limb-bud and heart development (LBH) gene is a highly conserved, tissue-specific transcription cofactor in vertebrates that regulates multiple key genes in embryonic development. The role of LBH in various cancer types is still controversial, and its specific role and molecular mechanism in the oncogenesis of gastric cancer (GC) remains largely unexplored. In the present study, the prognostic significance and clinicopathological characteristics of LBH in GC was determined. The LBH mRNA expression was first investigated in four independent public datasets (TCGA-STAD, GSE15459, GSE29272, and GSE62254) and then validated with our samples at the protein level. LBH was overexpressed at both the mRNA and protein levels in cancer compared with normal tissues. High LBH expression was correlated with advanced T, N, and M stages. Kaplan–Meier analysis and log-rank test indicated that higher LBH expression was statistically correlated with shorter overall survival (OS) in the public datasets and our study samples. Univariate and multivariate Cox regression analysis showed that LBH was an independent prognostic biomarker for survival in TCGA-STAD, GSE15459, GSE62254 cohorts, and our GC patients.In vitroexperiments showed that knockdown of LBH can significantly inhibit the proliferation and invasion of HGC-27 cells, while overexpression of LBH can significantly enhance the proliferation and invasion of BGC-823 cells. Gene Set Enrichment Analysis (GSEA), Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) indicated that high LBH expression is associated with the PI3K-Akt pathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction. Western blot analysis showed that knockdown of LBH significantly inhibited the expression of integrin α5, integrin β1, p-FAK, and p-Akt. Therefore, results from the present study indicate that LBH is a potential independent prognostic biomarker and promotes proliferation and invasion of GC cells by activating the integrin/FAK/Akt pathway.

Download Full-text

NR3C2-Related Transcriptome Profile and Clinical Outcome in Invasive Breast Carcinoma

BioMed Research International ◽

10.1155/2021/9025481 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jianjun Lu ◽

Fang Hu ◽

Yingling Zhou

Keyword(s):

Breast Carcinoma ◽

Prognostic Value ◽

Prognostic Biomarker ◽

Prognostic Significance ◽

Invasive Breast Carcinoma ◽

Gene Set Enrichment Analysis ◽

T Cell Subsets ◽

Receptor Interaction ◽

Diagnostic Efficacy ◽

Cox Regression Analysis

Background. Increasing evidence has indicated that the nuclear receptor subfamily 3 group C member 2 (NR3C2) may be associated with tumorigenesis and patient prognosis for certain types of tumors. However, the clinical significance of NR3C2 is unclear in invasive breast carcinoma (BRCA). Methods. We used bioinformatics to broadly investigate and obtain a deeper understanding of the prognostic significance between NR3C2 and BRCA. RNA-sequencing data and clinical information of patients with BRCA from the Cancer Genome Atlas database were collected for subsequent analysis. The diagnostic efficacy of NR3C2 was evaluated by calculating the receiver operating characteristic curve. The prognostic value of NR3C2 was evaluated by Kaplan-Meier analysis and Cox regression analysis for patients with BRCA. Moreover, the OSbrca database was used to validate NR3C2 as a prognostic biomarker for BRCA. Gene set enrichment analysis (GSEA) and tumor immune infiltration analysis were conducted to explore the molecular mechanism of NR3C2 in BRCA. Results. The expression level of NR3C2 in BRCA tissues decreased compared to that in normal breast tissues ( P < 0.001 ). NR3C2 presented good diagnostic efficacy ( AUC = 0.908 ). Moreover, the expression of NR3C2 was verified using the Oncomine database. High expression of NR3C2 was statistically associated with prolonged overall survival ( HR = 0.65 , 95% CI: 0.47-0.91, and P = 0.012 ), progression-free interval ( HR = 0.68 , 95% CI: 0.49-0.95, and P = 0.024 ), and disease-specific survival ( HR = 0.57 , 95% CI: 0.36-0.89, and P = 0.015 ) for patients with BRCA. Besides, the prognostic value of NR3C2 was verified by the OSbrca database. GSEA results suggested that enriched pathways included neuroactive ligand-receptor interaction, focal adhesion, and ECM-receptor interaction. NR3C2 expression was moderately correlated with mast cells and some T cell subsets in BRCA. Conclusion. NR3C2 is a potential prognostic biomarker that could help clinicians develop more appropriate treatment plans for individual patients with BRCA.

Download Full-text

Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.744677 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hang Zheng ◽

Heshu Liu ◽

Huayu Li ◽

Weidong Dou ◽

Xin Wang

Keyword(s):

Gastric Cancer ◽

Network Analysis ◽

Risk Score ◽

Molecular Mechanisms ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Therapeutic Effects ◽

Mesenchymal Transition ◽

Spearman Test

Background: Cancer-associated fibroblasts (CAFs) are the most prominent cellular components in gastric cancer (GC) stroma that contribute to GC progression, treatment resistance, and immunosuppression. This study aimed at exploring stromal CAF-related factors and developing a CAF-related classifier for predicting prognosis and therapeutic effects in GC.Methods: We downloaded mRNA expression and clinical information of 431 GC samples from Gene Expression Omnibus (GEO) and 330 GC samples from The Cancer Genome Atlas (TCGA) databases. CAF infiltrations were quantified by the estimate the proportion of immune and cancer cells (EPIC) method, and stromal scores were calculated via the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Stromal CAF-related genes were identified by weighted gene co-expression network analysis (WGCNA). A CAF risk signature was then developed using the univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. We applied the Spearman test to determine the correlation among CAF risk score, CAF markers, and CAF infiltrations (estimated via EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms). The TIDE algorithm was further used to assess immunotherapy response. Gene set enrichment analysis (GSEA) was applied to clarify the molecular mechanisms.Results: The 4-gene (COL8A1, SPOCK1, AEBP1, and TIMP2) prognostic CAF model was constructed. GC patients were classified into high– and low–CAF-risk groups in accordance with their median CAF risk score, and patients in the high–CAF-risk group had significant worse prognosis. Spearman correlation analyses revealed the CAF risk score was strongly and positively correlated with stromal and CAF infiltrations, and the four model genes also exhibited positive correlations with CAF markers. Furthermore, TIDE analysis revealed high–CAF-risk patients were less likely to respond to immunotherapy. GSEA revealed that epithelial–mesenchymal transition (EMT), TGF-β signaling, hypoxia, and angiogenesis gene sets were significantly enriched in high–CAF-risk group patients.Conclusion: The present four-gene prognostic CAF signature was not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy response for GC patients, which might provide significant clinical implications for guiding tailored anti-CAF therapy in combination with immunotherapy for GC patients.

Download Full-text