Kim DW, et al. Korean J Radiol. 2019 Jun;20(6):880-893. https://doi.org/10.3348/kjr.2018.0812

Kim N, et al. Korean J Radiol. 2022 Nov;23(11):1089-1101. https://doi.org/10.3348/kjr.2022.0225

Target identification is a crucial process in drug development, aiming to identify key proteins, genes, and signal pathways involved in disease progression and their relevance in potential therapeutic interventions. While C-C chemokine receptor 8 (CCR8) has been investigated as a candidate anti-cancer target, comprehensive multi-omics analyzes across various indications are limited. In this study, we conducted an extensive bioinformatics analysis integrating genomics, proteomics, and transcriptomics data to establish CCR8 as a promising anti-cancer drug target. Our approach encompassed data collection from diverse knowledge resources, gene function analysis, differential gene expression profiling, immune cell infiltration assessment, and strategic prioritization of target indications. Our findings revealed strong correlations between CCR8 and specific cancers, notably Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). This research advances our understanding of CCR8 as a potential target for anti-cancer drug development, bridging the gap between molecular insights and creating opportunities for personalized treatment of solid tumors.

The expression of the placental growth factor (PGF) in cancer cells and the tumor microenvironment can contribute to the induction of angiogenesis, supporting cancer cell metabolism by ensuring an adequate blood supply. Angiogenesis is a key component of cancer metabolism as it facilitates the delivery of nutrients and oxygen to rapidly growing tumor cells. PGF is recognized as a novel target for anti-cancer treatment due to its ability to overcome resistance to existing angiogenesis inhibitors and its impact on the tumor microenvironment. We aimed to integrate bioinformatics evidence using various data sources and analytic tools for target-indication identification of the PGF target and prioritize the indication across various cancer types as an initial step of drug development. The data analysis included PGF gene function, molecular pathway, protein interaction, gene expression and mutation across cancer type, survival prognosis and tumor immune infiltration association with PGF. The overall evaluation was conducted given the totality of evidence, to target the PGF gene to treat the cancer where the PGF level was highly expressed in a certain tumor type with poor survival prognosis as well as possibly associated with poor tumor infiltration level. PGF showed a significant impact on overall survival in several cancers through univariate or multivariate survival analysis. The cancers considered as target diseases for PGF inhibitors, due to their potential effects on PGF, are adrenocortical carcinoma, kidney cancers, liver hepatocellular carcinoma, stomach adenocarcinoma, and uveal melanoma.

This study aimed to identify hub genes involved in regulatory T cell (Treg) function and migration, offering insights into potential therapeutic targets for cancer immunotherapy. We performed a comprehensive bioinformatics analysis using three gene expression microarray datasets from the GEO database. Differentially expressed genes (DEGs) were identified to pathway enrichment analysis to explore their functional roles and potential pathways. A protein-protein interaction network was constructed to identify hub genes critical for Treg activity. We further evaluated the co-expression of these hub genes with immune checkpoint proteins (PD-1, PD-L1, CTLA4) and assessed their prognostic significance. Through this comprehensive analysis, we identified CCR8 as a key player in Treg migration and explored its potential synergistic effects with ICIs. Our findings suggest that CCR8-targeted therapies could enhance cancer immunotherapy outcomes, with breast invasive carcinoma (BRCA) emerging as a promising indication for combination therapy. This study highlights the potential of CCR8 as a biomarker and therapeutic target, contributing to the development of targeted cancer treatment strategies.

Background/Objectives: Immune checkpoint inhibitors (ICIs) show durable efficacy in tumors with deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H), yet clinical responses remain heterogeneous. This study aimed to define immune subgroups within dMMR/MSI-H tumors and develop a reproducible transcriptomic signature predictive of ICI response. Methods: Four MSI-H-enriched cancer types (UCEC, COAD, READ, STAD) from The Cancer Genome Atlas were analyzed. Tumors were stratified by immune cell infiltration (MCP-counter immune composite score) and T-cell-inflamed gene expression profiles (GEP score). Integrating these two axes defined four immune subgroups. Differential expression, random forest feature selection, and pathway enrichment were performed to identify immune programs. A 20-gene immune signature representing the most immune-active subgroup was developed and validated across TCGA, GEO (GSE39582), and IMvigor210 cohorts. Results: Among the four subgroups, the most immune-active group showed strong activation of interferon signaling, antigen presentation, and T-cell-mediated pathways. The 20-gene signature—including CD74, STAT1, TAP1, and HLA-class genes—achieved high reproducibility (mean AUC = 0.95 ± 0.02; accuracy ≈ 89%). In the IMvigor210 cohort, this signature identified tumors with higher PD-L1 blockade response (55.6% vs. 32.8%, p = 0.034) and improved survival trends in the TMB-high subset. Conclusions: The proposed 20-gene signature quantitatively captures immune heterogeneity in dMMR/MSI-H tumors and serves as a practical, interpretable biomarker to identify true ICI responders and guide precision immunotherapy.