Abstract
Background: Heat shock 70kDa protein 5 (HSPA5), also known as GRP78, is widely expressed in most malignant cells and has been shown to have a significant role in the spread of most malignancies by transferring them to the cell membrane. High-level HSPA5 may serve as an independent prognostic marker for various malignancies due to its ability to accelerate tumor growth and migration, inhibit cell apoptosis and closely connect to prognosis. Therefore, it is crucial to examine HSPA5 using pan-cancer research, which might result in the discovery of novel cancer treatment targets.
Methods: The GTEx and TCGA databases have both provided evidence of the expression of various amounts of HSPA5 in various tissues. The Clinical Proteomics Tumor Analysis Consortium (CPTAC) evaluated the levels of HSPA5 protein expression, while qPCR investigations also evaluated the expression of HSPA5 mRNA in certain tumors. HSPA5 was studied using the Kaplan-Meier method to examine how it influences overall survival and disease-free survival in malignancies. GEPIA2 was used to investigate the correlation between HSPA5 expression and the clinical stage of cancer. The tumor-immune system interaction database (TISIDB) examined the expression of HSPA5 in association with molecular and tumor immune subtypes. The co-expressed genes of HSPA5 were extracted from the STRING database, and the top 5 co-expressed genes of HSPA5 in 33 cancers were identified using the TIMER database. Further research examined the relationship between tumor mutations and HSPA5. Microsatellite Instability (MSI) and Tumor Mutation Burden (TMB) were the primary areas of interest. The association between HSPA5 mRNA expression and immune infiltration was also explored using the TIMER database. Additionally, through the Linkedomics database, we examined the enrichment of GO and KEGG for HSPA5 in glioblastoma. Finally, the Cluster Analyzer tool was used to carry out a GSEA functional enrichment investigation.
Results: HSPA5 mRNA expression was found to be greater in all 23 tumor tissues than in the equivalent normal tissues, and high HSPA5 expression appeared to be strongly related to a poor prognosis in the majority of cancers, as observed by survival plots. In the tumour clinical stage display map, HSPA5 showed differential expression in most tumours. HSPA5 is strongly associated with Tumor Mutation Burden (TMB) and Microsatellite Instability (MSI). Cancer-associated Fibroblasts (CAFs) infiltration was strongly associated with HSPA5, as were nine immunological subtypes of malignancy and seven molecular subtypes of malignancy. According to the results of GO and KEGG enrichment analyses, HSPA5 in GBM is mostly involved in neutrophil-mediated immunological and collagen metabolic activities. Additionally, GSEA enrichment analyses of HSPA5 and associated genes demonstrated a substantial link between HSPA5 and the immunological milieu of tumors, cell division and nervous system regulation. By using qPCR, we were able to further corroborate the enhanced expression in the GBM, COAD, LUAD and CESC cell lines.
Conclusion: Our bioinformatics research leads us to hypothesize that HSPA5 may be involved in immune infiltration as well as tumor growth and progression. Additionally, it was found that differentially expressed HSPA5 is linked to a poor prognosis for cancer, with the neurological system, the tumor immunological microenvironment and cytokinesis being potential contributing factors. As a result, HSPA5 mRNA and the associated protein might be used as therapeutic targets and possible prognostic markers for a range of malignancies.
Graphical Abstract
[http://dx.doi.org/10.1371/journal.pone.0008625] [PMID: 20072699]
[http://dx.doi.org/10.1016/j.canlet.2021.10.004] [PMID: 34637844]
[http://dx.doi.org/10.1016/j.jconrel.2020.10.055] [PMID: 33129921]
[http://dx.doi.org/10.1002/jcb.22679] [PMID: 20506407]
[http://dx.doi.org/10.1074/jbc.RA119.009091] [PMID: 31358620]
[http://dx.doi.org/10.1002/jcp.30030] [PMID: 32864780]
[http://dx.doi.org/10.7314/APJCP.2014.15.17.7245] [PMID: 25227822]
[http://dx.doi.org/10.1002/iub.2157] [PMID: 31441584]
[http://dx.doi.org/10.1371/journal.pone.0086951] [PMID: 24475200]
[http://dx.doi.org/10.1016/j.phrs.2020.104823] [PMID: 32305494]
[http://dx.doi.org/10.1186/s12967-021-02786-6] [PMID: 33743739]
[http://dx.doi.org/10.1080/17474086.2020.1830372] [PMID: 32990063]
[http://dx.doi.org/10.1245/s10434-014-4061-3] [PMID: 25212833]
[http://dx.doi.org/10.1111/j.1447-0756.2012.01970.x] [PMID: 22889453]
[http://dx.doi.org/10.1038/s41598-021-98544-1] [PMID: 34580365]
[http://dx.doi.org/10.1038/srep16067] [PMID: 26530532]
[PMID: 30649790]
[http://dx.doi.org/10.1016/j.humpath.2007.11.009] [PMID: 18482745]
[http://dx.doi.org/10.1016/j.bcp.2019.02.038] [PMID: 30831072]
[http://dx.doi.org/10.1111/j.1365-2141.2011.08671.x] [PMID: 21517817]
[http://dx.doi.org/10.1182/blood-2010-03-275628] [PMID: 20628148]
[http://dx.doi.org/10.1182/blood-2010-04-278853] [PMID: 21106982]
[http://dx.doi.org/10.1016/j.otohns.2010.05.007] [PMID: 20723767]
[http://dx.doi.org/10.1111/trf.15725] [PMID: 32077507]
[http://dx.doi.org/10.3389/fonc.2020.582667] [PMID: 33014884]
[http://dx.doi.org/10.1093/bioinformatics/btz210] [PMID: 30903160]
[http://dx.doi.org/10.1158/0008-5472.CAN-17-0307] [PMID: 29092952]
[http://dx.doi.org/10.1093/nar/gkg034] [PMID: 12519996]
[http://dx.doi.org/10.1093/nar/gkx1090] [PMID: 29136207]
[http://dx.doi.org/10.1038/s41423-020-0488-6] [PMID: 32612154]
[http://dx.doi.org/10.1186/s12943-021-01428-1] [PMID: 34635121]
[http://dx.doi.org/10.3390/cancers13184720] [PMID: 34572947]
[http://dx.doi.org/10.1016/j.molcel.2014.03.022] [PMID: 24746698]
[PMID: 25069067]
[http://dx.doi.org/10.1016/j.bbrc.2010.01.058] [PMID: 20097177]
[http://dx.doi.org/10.1371/journal.pone.0125634] [PMID: 25973748]
[http://dx.doi.org/10.1016/j.semcancer.2006.07.014] [PMID: 16904903]