HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients

Abstract

Background: HIGD1B (HIG1 Hypoxia Inducible Domain Family Member 1B) is a protein-coding gene linked to the occurrence and progression of various illnesses. However, its precise function in gastric cancer (GC) remains unclear.

Methods: The expression of HIGD1B is determined through the TCGA and GEO databases and verified using experiments. The association between HIGD1B and GC patients' prognosis was analyzed via the Kaplan-Meier (K-M) curve. Subsequently, the researchers utilized ROC curves to assess the diagnostic capacity of HIGD1B and employed COX analysis to investigate risk factors for GC. The differentially expressed genes (DEGs) were then subjected to functional enrichment analysis, and a nomogram was generated to forecast the survival outcome and probability of GC patients. Additionally, we evaluated the interaction between HIGD1B and the immune cell infiltration and predicted the susceptibility of GC patients to therapy.

Results: HIGD1B is markedly elevated in GC tissue and cell lines, and patients with high HIGD1B expression have a poorer outcome. In addition, HIGD1B is related to distinct grades, stages, and T stages. The survival ROC curves of HIGD1B and nomogram for five years were 0.741 and 0.735, suggesting appropriate levels of diagnostic efficacy. According to Cox regression analysis, HIGD1B represents a separate risk factor for the prognosis of gastric cancer (p<0.01). GSEA analysis demonstrated that the HIGD1B is closely related to cancer formation and advanced pathways. Moreover, patients with high HIGD1B expression exhibited a higher level of Tumor-infiltration immune cells (TIICs) and were more likely to experience immune escape and drug resistance after chemotherapy and immunotherapy.

Conclusion: This study explored the potential mechanisms and diagnostic and prognostic utility of HIGD1B in GC, as well as identified HIGD1B as a valuable biomarker and possible therapeutic target for GC.

Keywords: HIGD1B; TME; gastric cancer; immune infiltration; immunotherapy; prognostic biomarker.

Figure 1

Analyzing and validating the expression of HIGD1B in pan-cancer and gastric cancer. (A) Expression of HIGD1B in pan-cancer non-paired samples. (B) Expression of HIGD1B in GC and adjacent tissues (non-paired) of the TCGA-STAD cohort. (C) Expression of HIGD1B in pan-cancer paired samples. (D) Expression of HIGD1B in GC and paired adjacent tissues of the TCGA-STAD cohort. (E) Expression of HIGD1B in GC and adjacent tissues in the GSE54129 and GSE29272 cohorts. (F) Detection of HIGD1B expression in gastric epithelial cells (GSE-1) and GC cell lines (AGS and HGC-27) by the qRT-PCT assay. (G) Detection of HIGD1B expression in gastric epithelial cells (GSE-1) and GC cell lines (AGS and HGC-27) by Western blot assay. *P < 0.05; **P < 0.01; ***P < 0.001. TCGA, The Cancer Genome Atlas; STAD, stomach adenocarcinoma; GC, gastric cancer.

Figure 2

Systematic evaluation the relationship between the HIGD1B and clinicopathological features. (A) Kaplan-Meier curves of high and low HIGD1B expression subgroups in the TCGA-STAD, GSE62254 and GSE84437 queues. (B) ROC curve of HIGD1B for predicting 1, 3, and 5-year survival in the TCGA-STAD queue. (C) The expression levels of HIGD1B in the surviving (fustat=0) and deceased (fustat=1) populations. (D) Expression of HIGD1B in age subgroups. (E) Expression of HIGD1B in gender subgroups. (F) The expression of HIGD1B in different pathological grading populations. (G) The expression of HIGD1B in staging subgroups. (H–J) The expression of HIGD1B in T stage, N stage, and M stage subgroups. TCGA, The Cancer Genome Atlas; STAD, stomach adenocarcinoma; ROC; receiver operating characteristic; GC, gastric cancer.

Figure 3

The relationship between HIGD1B and the prognosis of GC. (A) The relationship between HIGD1B’s expression and PFS, DFS, and DSS. (B) K-M curves of PFS, DFS, and DSS in the high and low HIGD1B expression subgroups. (C–G) The K-M curve of OS between different HIGD1B groups based on age, gender, pathological grading, stage, T-stage stratification. (H) The K-M curve between different HIGD1B subgroups in N0 population. (I) The K-M curve between different HIGD1B subgroups in M0 population. GC, gastric cancer; PFS, Progression Free Survival; DFS, Disease Free Survival; DSS, Disease Free Survival; K-M Kaplan-Meier; OS, overall survival.

Figure 4

Functional enrichment analysis and Cox regression analysis. (A) Volcano maps of all DEGs between high and low HIGD1B expression groups. (B) GO analysis of DEGs between high and low HIGD1B subgroups. (C) KEGG analysis of DEGs between high and low HIGD1B subgroups. (D) GSEA analysis of the primary enriched pathways in high and low HIGD1B groups. (E) Univariate Cox regression analysis of HIGD1B and clinical parameters in the TCGA cohort. (F) Multivariate Cox regression analysis of HIGD1B and clinical parameters. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis.

Figure 5

Construction of nomogram and evaluation of TME. (A) The nomogram created based on the HIGD1B, Age and Stage. (B) Calibration plots for nomograms at 1, 3, and 5-years. (C) ROC curve of the nomogram for predicting 1, 3, and 5-year survival. (D–F) ROC curve for predicting 1, 3, and 5-year survival according to the nomogram and other clinical features. (G) The proportion of stromal, immune, and tumor cells in the TME. **P < 0.01; ***P < 0.001. ROC, receiver operating characteristic; TME, tumor microenvironment.

Figure 6

Immune cell infiltration analysis. (A) Evaluating the proportion of 22 types of TIICs employing the CIBERSORT algorithm. (B) Expression levels of 22 TIICs in high and low HIGD1B expression groups. (C) Examining the infiltration of TIICs in high and low HIGD1B groups using the ssGSEA algorithm. (D) Spearman analysis between HIGD1B expression and several TIICs (including Activated CD4 T cell, Regulatory T cell, Macrophage cell, MDSC and so on). ns P>0.05; *P < 0.05; **P < 0.01; ***P < 0.001. TIICs, tumor-infiltrating immune cells; ssGSEA, single-sample gene set enrichment analysis.

Figure 7

Prediction of immunotherapy for GC. (A) The scores TIDE, dysfunction, and exclusion in high and low HIGD1B groups. (B) Analysis of HIGD1B and microsatellite state (MSI). (C) Waterfall plotting of somatic mutations. (D) TMB levels in high and low HIGD1B expression groups. (E) Kaplan-Meier curve of OS in high and low-TMB groups. (F) Kaplan-Meier curve show different survival among the four groups that combined TMB with HIGD1B. (G) Analysis of the combined application of anti-PD-1 and anti-CTLA-4 antibodies in distinct HIGD1B groups. ***P < 0.001. GC gastric cancer; TIDE, tumor immune dysfunction and exclusion; TMB, tumor mutational burden; OS, overall survival.

Figure 8

ICIS and drug sensitivity analyses. (A) Expression of ICIs in high and low HIGD1B expression groups. (B–E) Sensitivity analysis of chemotherapy drugs used for standard treatment of gastric cancer in clinical practice. Differences in sensitivity to chemotherapy drugs (including Oxaliplatin, Irinotecan, Cisplatin, 5-fluorouracil) among different subgroups of HIGD1B, and correlation between chemotherapy drugs IC50 value and HIGD1B expression. (F, G) Sensitivity analysis of targeted drugs (like Sorafenib and Savolitinib) in populations with high and low expression of HIGD1B. *P < 0.05; **P < 0.01; ***P < 0.001. ICIs, immune checkpoint inhibitors.