An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Abstract

BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

Exploration and Validation of A Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration

“…For instance, Jiang et al found that the in ammatory gene polymorphisms were closely related to the occurrence and progression of osteosarcoma [29]. Recent researches also indicated that several in ammatory response-associated genes could combine as the prognostic signature in different cancers [11,12]. However, none of this signature had been established in osteosarcoma.…”

“…What's more, with the advances in microarray and next-generation sequencing technology, numbers of in ammatory response-associated genes were discovered [10]. Several studies revealed that these genes could combine as the signature to predict the prognosis of patients with malignant tumors and they might be applied in clinic [11,12]. However, few studies focused on the relationship between osteosarcoma and in ammatory response and no such signature had been established.…”

Comprehensive Analysis of Inflammation Response Related Genes and Immune Infiltrates in Osteosarcoma

“…The expression and clinical data from TARGET and GEO database were both public and complied with data access policy and publication guidelines of database. In ammatory response-related genes (IRG) list was collected from Molecular Signatures database [14]. IRG expression of TARGET was extracted as the modeling subgroup.…”

“…LASSO-penalized Cox regression analysis was variables selection algorithm which could minimize the risk of over tting [15]. Prognostic scores of each osteosarcoma patient were calculated as previously described [14]. And these osteosarcoma patients were strati ed into high and low risk subgroups according to median prognostic score value.…”

Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

“…The connection between inflammation and cancer has been well established (4). Inflammation could not only inhibit tumors but also promote cancers (5). Recently, numerous studies indicated that inflammatory response regulates the development and progression of cancer, which has attracted increasing attention from researchers (6).…”