The clinical significance of integrin subunit alpha V in cancers: from small cell lung carcinoma to pan-cancer

Abstract

Background: Little is known about the relationship between integrin subunit alpha V (ITGAV) and cancers, including small cell lung cancer (SCLC).

Methods: Using large sample size from multiple sources, the clinical roles of ITGAV expression in SCLC were explored using differential expression analysis, receiver operating characteristic curves, Kaplan-Meier curves, etc. RESULTS: Decreased mRNA (SMD = - 1.05) and increased protein levels of ITGAV were detected in SCLC (n = 865). Transcription factors-ZEB2, IK2F1, and EGR2-may regulate ITGAV expression in SCLC, as they had ChIP-Seq (chromatin immunoprecipitation followed by sequencing) peaks upstream of the transcription start site of ITGAV. ITGAV expression made it feasible to distinguish SCLC from non-SCLC (AUC = 0.88, sensitivity = 0.78, specificity = 0.84), and represented a risk role in the prognosis of SCLC (p < 0.05). ITGAV may play a role in cancers by influencing several immunity-related signaling pathways and immune cells. Further, the extensive pan-cancer analysis verified the differential expression of ITGAV and its clinical significance in multiple cancers.

Conclusion: ITGAV served as a potential marker for prognosis and identification of cancers including SCLC.

Keywords: Area under the curve; Integrin subunit alpha V; Prognosis; Small cell lung cancer; Standardized mean difference.

Fig. 1

The expression of ITGAV in small cell lung carcinoma (SCLC). Panel A Violin plots of ITGAV expression in SCLC. Panel B A forest plot of evaluating standard mean difference (SMD) of ITGAV expression between SCLC and non-SCLC groups. Panel C A funnel plot with Begg’s test for publication bias test. Panel D A violin plot of ITGAV protein levels between SCLC and non-SCLC groups. TMA tissue microarray

Fig. 2

ITGAV protein levels, and the clinical significance of ITGAV expression in small cell lung carcinoma (SCLC). Panels A–L The protein levels of ITGAV in non-SCLC (A–F) and SCLC (G–L) tissues under the microscope by in-house tissue microarrays. The left figure of each two combined figures is 200×, and the right figure is 400×. Panel M A Kaplan–Meier curve of overall survival between high- and low-ITGAV mRNA expression groups. Panel N A Kaplan–Meier curve of disease-free survival between high- and low-ITGAV mRNA expression groups. Panel O Summary receiver operating characteristic curve for identifying SCLC based on ITGAV mRNA expression. SENS sensitivity, SPEC specificity, AUC area under the curve

Fig. 3

Identification of potential transcription factors regulating ITGAV expression. A The venn plot for screening predicted transcription factors for ITGAV. B For the three transcription factors, there existed binding sites with the potential promoter region of ITGAV

Fig. 4

Disease ontology terms, gene ontology terms, kyoto encyclopedia of genes and genomes and reactome pathways of ITGAV-positively-related downregulated expression genes. DO disease ontology, CC cellular component, BP biological process, MF molecular function

Fig. 5

Gene set enrichment analysis (GSEA), and immune infiltration levels analyses. Panels A and B GSEA for Kyoto Encyclopedia of Genes and Genomes (panel A), and Reactome pathways (panel B) of ITGAV-positively-related downregulated expression genes. Panels C and D Analyses of immune infiltration levels based on ESTIMATE (panel C) and CIBERSORT (panel D) algorithms. SCLC small cell lung carcinoma

Fig. 6

The expression of ITGAV, and its associations with clinical parameters and prognosis in pan-cancer. Panel A The expression of ITGAV mRNA expression in pan-cancers. *p < 0.05. Panel B Associations of ITGAV in clinical parameters in multiple cancers. Panel C The correlation of ITGAV expression with the overall survival of cancers patients. Panel D The correlation of ITGAV expression with the disease-free survival of cancers patients

Fig. 7

Receiver operating characteristic curves and a summary receiver operating characteristic curve for identifying cancers from non-cancers based on ITGAV expression. SENS sensitivity, SPEC specificity, AUC area under the curve

Fig. 8

The association of ITGAV expression with immune infiltration level in pan-cancer