Exploration of prognostic and treatment markers in hepatocellular carcinoma via GPCR-related genes analysis.

Background: G protein-coupled receptors (GPCRs), the biggest family of signaling receptors, account for 34 % of all the drug targets approved by the Food and Drug Administration (FDA). It has been gradually recognized that GPCRs are of significance for tumorigenesis, but in-depth studies are still required to explore specific mechanisms. In this study, the role of GPCRs in hepatocellular carcinoma (HCC) was elucidated, and GPCR-related genes were employed for building a risk-score model for the prognosis and treatment efficacy prediction of HCC patients. Methods: Patients' data on HCC were sourced from the Liver Hepatocellular Carcinoma-Japan (LIRI-JP) and The Cancer Genome Atlas (TCGA) databases, while GPCR-related genes were obtained from the Molecular Signatures Database (MSigDB). Univariant and multivariant Cox regression analyses, as well as least absolute shrinkage and selection operator (LASSO) were performed with the aim of identifying differentially expressed GPCR-related genes and grouping patients. Differential expression and functional enrichment analyses were performed; protein-protein interaction (PPI) mechanisms were explored; hub genes and micro ribonucleic acid (miRNA)-target gene regulatory networks were constructed. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to evaluate immune infiltration levels and genetic variations. Sensitivity to immunotherapy and common antitumor drugs was predicted via the database Genomics of Drug Sensitivity in Cancer (GDSC). Results: A GPCR-related risk score containing eight GPCR-related genes (atypical chemokine receptor 3 (ACKR3), C-C chemokine receptor type 3 (CCR3), CCR7, frizzled homolog 5 (FZD5), metabotropic glutamate receptor 8 (GRM8), hydroxycarboxylic acid receptor 1 (HCAR1), 5-hydroxytryptamine receptor 5A (HTR5A) and nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6)) was set up. In addition, patients were classified into groups with high and low risks. Patients in the high-risk group exhibited a worse prognosis but demonstrated a more favorable immunotherapy response rate compared with those in the low-risk group. Distinct sensitivity to chemotherapeutic drugs was observed. A clinical prediction model on the basis of GPCR-related risk scores was constructed. Areas under the curves (AUC) corresponding to one-, three- and five-year survival were 0.731, 0.765 and 0.731, respectively. Conclusions: In this study, an efficient HCC prognostic prediction model was constructed by only GPCR-related genes, which are all potential targets for HCC treatment.

Immune checkpoint inhibitor-related molecular markers predict prognosis in extrahepatic cholangiocarcinoma.

BACKGROUND: Therapeutic approaches for extrahepatic cholangiocarcinoma (EHCC) are limited, due to insufficient understanding to biomarkers related to prognosis and drug response. Here, we comprehensively assess the molecular characterization of EHCC with clinical implications. METHODS: Whole-exome sequencing (WES) on 37 tissue samples of EHCC were performed to evaluate genomic alterations, tumor mutational burden (TMB) and microsatellite instability (MSI). RESULTS: Mutation of KRAS (16%) was significantly correlated to poor OS. ERBB2 mutation was associated with improved OS. ERBB2, KRAS, and ARID1A were three potentially actionable targets. TMB ≥10 mutations per megabase was detected in 13 (35.1%) cases. Six patients (16.2%) with MSIsensor scores ≥10 were found. In multivariate Cox analysis, patients with MSIsensor sore exceed a certain threshold (MSIsensor score ≥0.36, value approximately above the 20th percentile as thresholds) showed a significant association with the improved OS (HR = 0.16; 95% CI: 0.056-0.46, p < 0.001), as well as patients with both TMB ≥3.47 mutations per megabase (value approximately above the 20th percentile) and MSIsensor score ≥0.36. CONCLUSIONS: TMB and MSI are potential biomarkers associated with better prognosis for EHCC patients. Furthermore, our study highlights important genetic alteration and potential therapeutic targets in EHCC.

Gene signature and connectivity mapping to assist with drug prediction for pancreatic ductal adenocarcinoma.

INTRODUCTION: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is highly variable and there is a paucity of effective treatment options for patients with PDAC. Genome-wide analyses may allow for potential drugs to be identified using differentially expressed genes, as well as constructing protein interaction networks and molecule-gene connectivity mapping. METHODS: Microarray data of RNA expression profiling of PDAC and normal pancreas tissues were downloaded from the Gene Expression Omnibus (GEO). Functional and pathway enrichment information of the DEGs was obtained using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Corresponding homologous proteins were analyzed by protein-protein interaction analysis. Survival-related hub genes were screened and potential therapeutic drugs for PDAC were identified using the connectivity mapping (cMap). RESULTS: Of 18,229 PDAC genes assessed using RNA expression profiling from 118 PDAC tumor samples and 13 normal pancreatic tissue samples, 1502 and 744 genes were upregulated and downregulated, respectively, versus normal pancreas tissue. Protein-protein interaction analysis revealed 10 upregulated hub genes (ITGB1, ITGAV, SDC1, KRAS, CCNB2, COL1A2, AURKA, CDC20, COL1A1, COL3A1) and 10 downregulated hub genes (CPB1, CPA1, CPA2, CTRB2, CTRC, CELA3A, CELA2B, PRSS3, CELA2A, REG1A). The connectivity mapping score related to this hub gene list was used to generate the candidate drugs for PDAC treatment, which includes tyrosine kinase inhibitors (lucitanib, lapatinib, ceritinib and CYT-387), serine/threonine protein kinase inhibitors (roscovitine, BS-181, purvalanol-a, MK-2206 and palomid-529) and other small molecules. CONCLUSION: Using available genetic atlas data, potential drug candidates for treatment of PDAC were identified based on differentially expressed genes, protein interaction analysis and connectivity mapping. These results may help focus efforts on identifying targeted agents with potential therapeutic efficacy for evaluation in prospective clinical trials of patients with PDAC.

Novel Drug Candidate Prediction for Intrahepatic Cholangiocarcinoma via Hub Gene Network Analysis and Connectivity Mapping.

Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy, and there is a need for effective systemic therapies. Gene expression profile-based analyses may allow for efficient screening of potential drug candidates to serve as novel therapeutics for patients with ICC. The RNA expression profile of ICC and normal biliary epithelial cells were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Function annotation and enrichment pathway analyses of the differentially expressed genes (DEGs) were finished using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. A weighted gene co-expression network (WGCN) was constructed by WGCN analysis (WGCNA). Key genes from the DEGs and co-expression gene modules were analyzed to generate a protein-protein interaction (PPI) network. The association between the top 10 screened hub genes and the overall and disease-free survival of ICC patients was examined. The Connectivity Map (cMap) analysis was performed to identify possible drugs for ICC using hub genes. A total of 151 key genes were selected from the overlapping genes of 1287 GSE-DEGs, 8183 TCGA-DEGs and 1226 genes in the mixed modules. A total of 10 hub genes of interest (CTNNB1, SPP1, COL1A2, COL3A1, SMAD3, SRC, VCAN, PKLR, GART, MRPS5) were found analyzing protein-protein interaction. Using the cMap, candidate drugs screened with potential efficacy for ICC included three tyrosine kinase inhibitors (dasatinib, NVP-BHG712, tivantinib), two cannabinoid receptor agonists (palmitoylethanolamide, arachidonamide), two antibiotics (moxifloxacin, amoxicillin), one estrogen receptor agonist (levonorgestrel), one serine/threonine protein kinase inhibitor (MK-2206) and other small molecules. Key genes from network and PPI analysis allowed us to identify potential drugs for ICC. The identification of novel gene expression profiles and related drug screening may accelerate the identification of potential novel drug therapies for ICC.