Roles of acyl-CoA synthetase long-chain family member 5 and colony stimulating factor 2 in inhibition of palmitic or stearic acids in lung cancer cell proliferation and metabolism

A Novel Immune-Related Seventeen-Gene Signature for Predicting Early Stage Lung Squamous Cell Carcinoma Prognosis

“…In this research, a 17 immune gene survival signature (including RPRM, APOH, SSX1, MSGN1, HPR, ISM2, FGA, LBP, HAS1, CSF2, RETN, CCL2, CCL21, MMP19, PTGIS, F13A1, C1QTNF1) was generated from 401 tumor samples of early stage LUSC. Most of these genes were involved in the proliferation, invasion, and differentiation of tumor cells (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41). For example, wt-p53 downstream gene RPRM could be activated by HspB2 and further inhibited pancreatic cancer cell proliferation (42).…”

Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

“…We introduced clinical phenomics into trans-omics to evaluate network nodules cross lipidomic and phenomic layers and define phenome-specific lipid panels and lipid-specific phenome groups in acute lung injury patients with or without infections and with or without chronic diseases, as well as in patients with lung cancer subtypes. 8,15,17 To gather our present and previous studies, we found that the variation of trans-omic profiles in patients was TA B L E 8 Lipid elements significantly elevated or declined alone in lung cancer patients survived or nonsurvived (more than twofold), respectively, as compared with healthy control (P-values) more obvious, uncontrolled, and irregular due to the complex and comprehensive influencing factors in clinical practice and measurements. We also found that the integrated panel appeared lung cancer subtype-specific network and the construction to show statistical correlations and mechanistic molecular interactions between lipid metabolisms and phenome appearances in patients with lung cancer.…”

“…Studies on lipidomic profiles of lung cancer patients have experienced three phases to detect the difference of lipidomic profiles between healthy and lung cancer patients, 16 the association of multi-omics among lung cancer subtypes, 3 and the molecular mechanism of clinical lipidomics-based target lipid elements. 17 Of those lipidomics-based data, limited information could be adopted to understand the disease occurrence and development, phone progression, and response to therapy, due to the lack of link between omics data and clinical phenomes. Like other omics investigations, most genomic data were not tied with clinical information, so that with little values to be understood and applied for clinical precision medicine.…”

“…The trans-omic modules can reflect regulatory mechanisms by which cell survival and differentiation, organ function and metabolism, and systemic response and dysfunction are controlled. 17,32,33 Those lipid metabolites and related limiting enzymes can be regulated by up/down-expression of the corresponding genes and mitochondrial DNA methylation and fusion/ fission. 34,35 There were also limitations in this study.…”

“…Besides, it initially demonstrated clinical phenome‐associated lipid elements and lipid element‐associated clinical phenomes using clinical trans‐omics. Studies on lipidomic profiles of lung cancer patients have experienced three phases to detect the difference of lipidomic profiles between healthy and lung cancer patients, 16 the association of multi‐omics among lung cancer subtypes, 3 and the molecular mechanism of clinical lipidomics‐based target lipid elements 17 . Of those lipidomics‐based data, limited information could be adopted to understand the disease occurrence and development, phone progression, and response to therapy, due to the lack of link between omics data and clinical phenomes.…”