{"type": "root", "children": [{"type": "p", "children": [{"type": "t", "text": "\n Integrated multi‐platform analyses across several cancer types have begun to shed light on novel candidate genes with potential roles in tumor progression and prognosis. In one study focused on the membrane proteome in non–small cell lung cancer, researchers identified hundreds of “missing proteins,” many of which were membrane‐associated, highlighting the challenge and importance of charting these elusive components in cancer biology."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "1"}]}, {"type": "t", "text": " In another investigation, epigenetic profiling in pig skeletal muscle implicated key metabolic and cell signaling genes in adaptation and metabolic regulation"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "2"}]}, {"type": "t", "text": ", while integrated analyses in uveal melanoma revealed clusters of genes in focal copy number regions that are associated with metastasis and overall survival."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "3"}]}, {"type": "t", "text": "\n "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n Notably, in ovarian serous cancer, an investigation applying targeted maximum likelihood estimation (TMLE) within a tumor microenvironment framework identified several TME‐related genes that significantly influence 3‐year mortality outcomes. Among these, increased expression of "}, {"type": "b", "children": [{"type": "t", "text": "CROCC2"}]}, {"type": "t", "text": " was found to be positively correlated with higher mortality, suggesting that this gene may serve as an important prognostic marker and potentially play a role in modulating interactions between the tumor and its surrounding stroma."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "4"}]}, {"type": "t", "text": " The association of CROCC2 with poorer clinical outcomes underscores the need for further functional studies to elucidate its mechanistic contributions to tumor progression and to evaluate its viability as a target for therapeutic intervention.\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "Reta Birhanu Kitata, Baby Rorielyn T Dimayacyac-Esleta, Wai-Kok Choong, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "J Proteome Res (2015)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1021/acs.jproteome.5b00477"}], "href": "https://doi.org/10.1021/acs.jproteome.5b00477"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "26202522"}], "href": "https://pubmed.ncbi.nlm.nih.gov/26202522"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "Siriluck Ponsuksili, Nares Trakooljul, Sajjanar Basavaraj, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Epigenome-wide skeletal muscle DNA methylation profiles at the background of distinct metabolic types and ryanodine receptor variation in pigs."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "BMC Genomics (2019)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s12864-019-5880-1"}], "href": "https://doi.org/10.1186/s12864-019-5880-1"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "31195974"}], "href": "https://pubmed.ncbi.nlm.nih.gov/31195974"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "Hima Anbunathan, Ruth Verstraten, Arun D Singh, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Integrative Copy Number Analysis of Uveal Melanoma Reveals Novel Candidate Genes Involved in Tumorigenesis Including a Tumor Suppressor Role for "}, {"type": "a", "children": [{"type": "t", "text": "i"}], "href": "i"}, {"type": "t", "text": "PHF10/BAF45a"}, {"type": "a", "children": [{"type": "t", "text": "/i"}], "href": "/i"}, {"type": "t", "text": "."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Clin Cancer Res (2019)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1158/1078-0432.CCR-18-3052"}], "href": "https://doi.org/10.1158/1078-0432.CCR-18-3052"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "31227497"}], "href": "https://pubmed.ncbi.nlm.nih.gov/31227497"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Lu Wang, Xiaoru Sun, Chuandi Jin, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Front Genet (2021)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.3389/fgene.2021.625145"}], "href": "https://doi.org/10.3389/fgene.2021.625145"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "34149794"}], "href": "https://pubmed.ncbi.nlm.nih.gov/34149794"}]}]}]}