Gene expression levels of gamma-glutamyl hydrolase in tumor tissues may be a useful biomarker for the proper use of S-1 and tegafur-uracil/leucovorin in preoperative chemoradiotherapy for patients with rectal cancer

Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer

Background

Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment.

Methods

Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo.

Results

Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4⁺ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model.

Conclusions

Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.

Novel candidate factors predicting the effect of S-1 adjuvant chemotherapy of pancreatic cancer

The collagen gel droplet-embedded drug sensitivity test (CD-DST) was revealed to be useful for predicting the effect of S-1 adjuvant chemotherapy for pancreatic ductal adenocarcinoma (PDAC). However, collection of an adequate number of PDAC cells is difficult due to the surrounding fibroblasts. Thus, the aim of this study was to discover novel biomarkers to predict chemosensitivity based on the CD-DST results. Proteomics analysis was performed using liquid chromatography tandem mass spectrometry (LC–MS/MS). Candidate proteins were validated in patients with 5-FU CD-DST results via immunohistochemistry (IHC). The relationships between the candidate proteins and the effect of the adjuvant S-1 were investigated via IHC. Among the 2696 proteins extracted by LC–MS/MS, C1TC and SAHH could accurately predict the CD-DST results. Recurrence-free survival (RFS) was significantly improved in the IHC-positive group compared with the IHC-negative group in both factors. The negative group did not show a significant difference from the group that did not receive S-1. The double-positive group was associated with significantly prolonged RFS compared to the no adjuvant chemotherapy group. C1TC and SAHH have been shown to be useful biomarkers for predicting 5-FU sensitivity as a substitute for the CD-DST in adjuvant chemotherapy for PDAC.

Gallbladder cancer integrated bioinformatics analysis of protein profile data

AIM

Identifying the critical genes that differentiate gall bladder cancer from a normal gall bladder and the related biological terms was the aim of this study.

BACKGROUND

The molecular mechanism underlying gall bladder cancer (GBC) trigger and development still requires investigations. Potential therapeutic biomarkers can be identified through protein-protein interaction network prediction of proteome as a complementary study.

METHODS

Here, a literature review of proteomics studies of gall bladder cancer from 2010 to 2019 was undertaken to screen differentially expressed proteins in this cancer. A network of 27 differentially expressed proteins (DEPs) via Cytoscape 3.7.1 and its plug-ins was constructed and analyzed.

RESULTS

Ten proteins were introduced as hub-bottlenecks among which four were from DEPs. The gene ontology analysis also indicated that positive regulation of multi-organism process and regulation of response to biotic stimulus are the most disrupted biological processes of GBC considering their relationships with the DEPs.

CONCLUSION

ACTG, ALB, GGH, and DYNC1H1, and relative biological terms were introduced as drug targets and possible diagnostic biomarkers.