Construction of a prognostic signature associated with liver metastases for prognosis and immune response prediction in colorectal cancer

Abstract

BackgroundAs the most common gastrointestinal malignancy worldwide, liver metastases occur in half colorectal cancer (CRC) patients. Early detection can help treat them early and reduce mortality in patients with colorectal cancer liver metastases (CRLM). Finding useful biomarkers for CRLM is thus essential.MethodsThe TCGA and GEO databases were used to download the expression profiles and clinical data of the patients. Differential analysis screened for genes associated with CRLM, and univariate Cox regression analysis identified genes associated with prognosis. The least absolute shrinkage and selection operator (LASSO) method further preferred genes to construct a prognostic signature. Kaplan-Meier survival curves were used to show patients’ overall survival (OS). Receiver operating characteristic (ROC) curves showed the accuracy of the model. Risk scores and clinical characteristics of patients were included in multivariate Cox regression analysis to identify independent risk factors, and a nomogram was constructed. The proportion of immune cells and infiltration were assessed using the ‘CIBERSORT’ package and the ‘ESTIMATE’ package.ResultsWe constructed a signature consisting of seven CRLM-associated genes, and signature-based risk scores have great potential in estimating the prognosis of CRC patients. Moreover, the poor response to immunotherapy in high-risk patients might contribute to the poor prognosis of individuals. Furthermore, we found that overexpression of Hepcidin antimicrobial peptide (HAMP), the only gene highly expressed in CRC and liver metastatic tissues, promoted CRC development and that it was associated with tumor mutation burden (TMB), DNA mismatch repair (MMR) genes, and microsatellite instability (MSI) in various tumors. Finally, we found that in CRC patients, low expression of HAMP also represented a better immunotherapeutic outcome, reflecting the critical role of HAMP in guiding immunotherapy.ConclusionWe identified a prognostic signature containing 7 CRLM-associated genes, and the signature was specified as an independent predictor and a nomogram containing the risk score was built accordingly. In addition, the derived gene HAMP could help guide the exploration of profitable immunotherapeutic strategies.

Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma

“…However, the capacity of the ESTIMATE immune scores to predict immunotherapy response in cancer is controversial. 70 , 71 , 72 , 73 , 74 Gao et al. observed that the low-risk group classified by a novel pyroptosis-related signature had higher ESTIMATE immune scores and exhibited better responses to ICIs than the high-risk group in oral squamous cell carcinoma.…”

“…However, there was no difference in the ESTIMATE immune scores between the two groups. 74 In the future, optimal predicting biomarkers should not only concern the composition of the immune infiltrates and the feature of the inflammatory response but also disclose heterogeneity in immunological composition, spatial distribution and function. 75 Finally, GSVA indicated that compared to the low-risk group, the high-risk group had higher enrichment scores in tumour-related signalling pathways.…”

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

“…Regarding other molecular biomarkers, Marfa et al used the CART analysis to construct a proteomic signature in a series of 85 patients, which differentiates mild from severe cases (based on predicted OS) according to the four most significant protein peaks [81]. Another emerging predictor is differentially expressed genes (DEGs), or differentially expressed exosomal miRNAs, which has already been used in five studies [22,44,59,62,68]. After selection of candidate genes, LASSO regression, which applies a penalty to candidate predictors and eliminates some variables from the final model, was applied in all studies to construct the final gene panel.…”

“…After selection of candidate genes, LASSO regression, which applies a penalty to candidate predictors and eliminates some variables from the final model, was applied in all studies to construct the final gene panel. All five studies combined the DEGs with clinical predictors and reported merged scores [22,44,59,62,68].…”