Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Lishan Song; Chaojie Xu; Tong Zhang; Shengyang Chen; Shuiquan Hu; Bingbing Cheng; Hao Tong; Xiaoyong Li

doi:10.3389/fgene.2022.989779

Clinical neutrophil-associated genes as reliable predictors of hepatocellular carcinoma

Front Genet. 2022 Oct 6:13:989779. doi: 10.3389/fgene.2022.989779. eCollection 2022.

Authors

Lishan Song¹, Chaojie Xu¹, Tong Zhang¹, Shengyang Chen¹, Shuiquan Hu¹, Bingbing Cheng¹, Hao Tong¹, Xiaoyong Li¹

Affiliation

¹ The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.

Abstract

Background: Growing evidence suggests that infiltrating neutrophils are key players in hepatocellular carcinoma (HCC) tumor progression. However, a comprehensive analysis of the biological roles of neutrophil infiltration and related genes in clinical outcomes and immunotherapy is lacking. Methods: HCC samples were obtained from the TCGA and GEO databases. The CIBERSORT algorithm was used to reveal the TIME landscape. Gene modules significantly associated with neutrophils were found using weighted gene co-expression network analysis (WGCNA), a "dynamic tree-cut" algorithm, and Pearson correlation analysis. Genes were screened using Cox regression analysis and LASSO and prognostic value validation was performed using Kaplan-Meier curves and receiver operating characteristic (ROC) curves. Risk scores (RS) were calculated and nomograms were constructed incorporating clinical variables. Gene set variation analysis (GSVA) was used to calculate signaling pathway activity. Immunophenoscore (IPS) was used to analyze differences in immunotherapy among samples with different risk scores. Finally, the relationship between RS and drug sensitivity was explored using the pRRophetic algorithm. Results: 10530 genes in 424 samples (50 normal samples, 374 tumor samples) were obtained from the TCGA database. Using WGCNA, the "MEbrown" gene module was most associated with neutrophils. Nine genes with prognostic value in HCC (PDLIM3, KLF2, ROR2, PGF, EFNB1, PDZD4, PLN, PCDH17, DOK5) were finally screened. Prognostic nomograms based on RS, gender, tumor grade, clinical stage, T, N, and M stages were constructed. The nomogram performed well after calibration curve validation. There is an intrinsic link between risk score and TMB and TIME. Samples with different risk scores differed in different signaling pathway activity, immunopharmaceutical treatment and chemotherapy sensitivity. Conclusion: In conclusion, a comprehensive analysis of neutrophil-related prognostic features will help in prognostic prediction and advance individualized treatment.

Keywords: WGCNA; hepatocellular carcinoma; neutrophils; risk score; tumor immune microenvironment.