Traitement des autres tumeurs solides métastatiques MSI/dMMR

MDIG, a 2‑oxoglutarate‑dependent oxygenase, acts as an oncogene and predicts the prognosis of multiple types of cancer

Recent studies have indicated that mineral dust‑induced gene (MDIG) is an oncogene induced by environmental factors, which has a key role in the development and progression of various tumor types, through epigenetic modifications; however, there are no previous pan‑cancer analyses of MDIG. In the present study, a comprehensive pan‑cancer analysis of MDIG was performed using public databases. The results demonstrated that MDIG was upregulated in tumor tissue samples compared with normal tissue, that it was present in all cancer cell lines and it was closely associated with the prognosis of patients with different tumor types. Furthermore, MDIG expression was closely associated with the immunological characteristics of the tumor microenvironment (TME), such as the frequency of tumor‑infiltrating immune cells, TME‑relevant signatures, immunostimulatory genes, immune checkpoint genes, chemokine receptor genes, tumor mutational burden and microsatellite instability. In parallel, high expression of MDIG was associated with improved overall survival of patients and this was verified in a cohort of patients who had received anti‑programmed cell death 1 ligand 1 treatment. Furthermore, high expression of MDIG led to multiple drug resistance in The Cancer Genome Atlas‑lung adenocarcinoma cohort. In addition, gene set variant analysis and gene set enrichment analysis indicated that MDIG was involved in cell cycle regulation. In vitro experiments suggested that MDIG promoted cell proliferation through the mTOR complex 2/Akt and pyruvate dehydrogenase kinase 1/Akt signaling pathways. In summary, the present study suggests that MDIG may be a prognostic biomarker and therapeutic target for various cancer types.

Computational detection of a genome instability-derived lncRNA signature for predicting the clinical outcome of lung adenocarcinoma

Evidence has been emerging of the importance of long non-coding RNAs (lncRNAs) in genome instability. However, no study has established how to classify such lncRNAs linked to genomic instability, and whether that connection poses a therapeutic significance. Here, we established a computational frame derived from mutator hypothesis by combining profiles of lncRNA expression and those of somatic mutations in a tumor genome, and identified 185 candidate lncRNAs associated with genomic instability in lung adenocarcinoma (LUAD). Through further studies, we established a six lncRNA-based signature, which assigned patients to the high- and low-risk groups with different prognosis. Further validation of this signature was performed in a number of separate cohorts of LUAD patients. In addition, the signature was found closely linked to genomic mutation rates in patients, indicating it could be a useful way to quantify genomic instability. In summary, this research offered a novel method by through which more studies may explore the function of lncRNAs and presented a possible new way for detecting biomarkers associated with genomic instability in cancers.