Identification of an immune-related gene signature to improve prognosis prediction of colorectal cancer patients

Dai, Siqi; Xu, Shengwen; Yao, Ye; Ding, Kefeng

doi:10.21203/rs.3.rs-56380/v1

Cited by 4 publications

(5 citation statements)

References 46 publications

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“…By taking advantage of rapid development of omics sequencing technologies, researchers can investigate the underlying molecular mechanisms of CC progression (27,(37)(38)(39). Moreover, transcriptome profiling of colon tumors provided valuable information for researchers to develop biomarkers-based risk score models to promote the prognosis of CC (12)(13)(14)40,41). The ECM, which can convey specific signals to cells, thereby can regulate several critical processes including immune cell migration, immune cell activation, proliferation, and their differentiation (19,42,43).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is essential to establish novel and robust prognostic biomarker signatures to identify the molecular changes that can reliably estimate clinical outcomes of CC patients, which would have tremendous value of guiding appropriate individualized clinical managements and treatments for CC patients. Much effort has been devoted to determining how the changes of distinct gene signatures such as ferroptosis-related, RNA binding proteins (RBPs)-related, immune-related gene signatures can be utilized to predict CC prognosis recently (12)(13)(14). The extracellular matrix (ECM) is composed of complex network of non-cellular components (CCs) of tissue, including glycoproteins, collagens, and proteoglycans, that provides both essential structural and biomedical supports for its cellular constituents, which can regulate the development and maintain tissue homeostasis (15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

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Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment

Chai¹,

Su²,

Zhao³

et al. 2023

Transl Cancer Res

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Background: The extracellular matrix (ECM) plays a vital role in progression, expansion, and prognosis of malignancies. In this study, we aimed to explore a novel ECM-based prognostic model for patients with colon cancer (CC).Methods: ECM-related genes were obtained from Molecular Signatures database. Differential expression analysis was performed using the CC dataset from The Cancer Genome Atlas (TCGA) database. Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis.Then an ECM-related signature was developed and verified in three independent CC cohorts (GSE33882, GSE39582 and GSE29621) from the Gene Expression Omnibus (GEO). A prognostic nomogram was developed incorporating the ECM-related gene signature with clinical risk factors. CIBERSORT was used to explore the immune cell infiltration level. Human Protein Atlas (HPA) database was utilized to validate the expression levels of identified prognostic ECM genes.Results: Four ECM-related genes (CXCL13, CXCL14, SFRP5 and THBS4) were identified to develop an ECM-based gene signature and demarcated CC patients into the high-and low-risk groups. In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001). In addition, ECM-related signature was significantly associated with consensus molecular subtype 4 (CMS4) as well as other known clinical risk factors such as a higher Tumor, Nodal Involvement, Metastasis (TNM) stage. Moreover, the risk score derived from the ECM-based gene signature could be utilized as an independent prognostic factor for CC patients. A nomogram including the ECM-related gene signature, age and stage was developed to serve clinical practice. CIBERSORT analysis showed immune cell infiltration was different between high-and low-risk groups. The immunohistochemical results derived from HPA indicated differential expression of prognosis-related ECM genes in CC and normal tissues. Conclusions: In the present study, a novel risk model based on ECM-signature could effectively reflect individual risk classification and provide potential therapeutic targets for CC patients. Moreover, the prognostic nomogram may help predict individualized survival.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment

Chai¹,

Su²,

Zhao³

et al. 2023

Transl Cancer Res

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“…In this respect, recent studies demonstrated that distinct immune gene signatures are prognostically relevant in cancer patients. [19][20][21][22] However, the IRGs selected in most studies are derived from the ImmuPort database or from ssGSEA. In the present study, the 14-IRG signature for sarcoma patients is based on ImmucellAI and WGCNA, which provides a novel and innovative method to identify prognostic biomarkers.…”

Section: Discussionmentioning

confidence: 99%

A novel immune-related gene signature predicting survival in sarcoma patients

Ren

Bazhin

Pretzsch

et al. 2022

Molecular Therapy - Oncolytics

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Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified highrisk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.

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“…angiogenesis and tumor cell migration [24]. IL20RB has been reported as a prognosis-related immune gene for pancreatic cancer [25], lung cancer [26] and colorectal cancer [27]; KLRC2 has also been predicted to play a role among many immune cell subpopulations [28,29]. The current study also demonstrates that IL20RB and KLRC2 affect the infiltration of immune T-cell subsets in renal cancer, which is expected to further explain the mechanism of immune action of IL20RB and KLRC2 in renal cancer.…”

Section: P R E P R I N Tmentioning

confidence: 99%

A novel seven-immune-genes-based model to improve prognosis prediction of clear cell renal cell carcinoma

Chen

Guo

et al. 2022

Arch Med Sci

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IntroductionThe aim of our study is to investigate the correlation of immune-related genes with clear cell renal cell carcinoma (ccRCC) prognosis and the role of immune-related genes in tumor immune microenvironment (TIME) and to build a new prognostic model and prognostic scoring system for renal cancer.Material and methodsWe downloaded the mRNA expression data of 610 samples (538 ccRCC and 72 normal tissues) from TCGA database and constructed a immune-related prognostic model using Cox regression analysis and LASSO analysis. Then we internally verified the scientific validity and accuracy of the model using Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves. Subsequently, Cytoscape was used to construct a TF-miRNA-mRNA network. The "CIBERSORT" package was used to perform the immune-infiltration analysis. Finally, validation of key gene expression was performed by immunohistochemistry (IHC) and quantitative reverse transcription-PCR (qRT-PCR).ResultsThe prognostic model constructed for ccRCC includes 7 genes (KLRC2, PGLYRP2, AGER, CHGA, AVPR1B, IL20RB, LAT). And it was proven to have good prognostic performance through the KM analysis and the ROC curves. We also constructed an accurate prognostic predictive scoring system by establishing a nomogram. Furthermore, the TF-miRNA-mRNA network revealed the potential mechanism of the model and the immune infiltration analysis revealed a correlation between this model and TIME.ConclusionsThe results suggest that the newly developed 7 immune-related genes model can be a practical and reliable prognostic tool for ccRCC. It also shows T cell infiltration characteristics in TIME can therefore be used as an immune biomarker for the diagnosis and treatment of ccRCC.

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Identification of an immune-related gene signature to improve prognosis prediction of colorectal cancer patients

Cited by 4 publications

References 46 publications

Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment

Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment

A novel immune-related gene signature predicting survival in sarcoma patients

A novel seven-immune-genes-based model to improve prognosis prediction of clear cell renal cell carcinoma

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