Genomic alteration profiles of lung cancer and their relationship to clinical features and prognosis value using individualized genetic testing

Abstract

This study aimed to use a panel targeting 197 genes and 38 fusions to observe the features of gene variations in lung cancer patients, as well as their prognostic values. Methods:Patients admitted to our hospital between 2016 and 2017 were enrolled. All patients received OseqTM-Drug genetic testing using peripheral venous blood, followed by 1-2 years of observation. Results:For all included patients, 32 genes were observed with mutations. EGFR exhibited the highest mutation rate (46.5%), followed by TP53. The majority of patients carried only one mutant gene. Interestingly, 18 (41.8%) patients showed no mutations, and some cases carried mutations in six genes simultaneously. There was no statistical relationship between mutations and demographic influence.Pathological subtypes were associated with mutations including FLI1, IGF1R, and NOTCH1. A significant correlation was observed between mutant genes and stage at diagnosis, however this requires further confirmation as there was only one case in these mutations: AKT2, AR, STK11, VEGFA, HDAC6, and ASPSCR. For the 33 patients with lymph node metastases at the time of diagnosis, no correlation with any gene mutant was found. Finally, no associations between the survival or prognosis indices (1-year survival, 1-year progression, progression free survival (PFS), and overall survival (OS)) were observed with gene mutations. Conclusions:Together, individualized genetic testing is a feasible and minimally invasive approach in cancer genetic analysis. However, gene mutation detection has a limited efficacy in the prediction of prognosis.

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

“…Although the biomarkers and therapeutic targets previously identified have contributed to the diagnosis and treatment of LUAD, a demand remains for novel genetic data for optimizing treatment protocols due to its biological complexity and poor prognosis ( 8 ). To explore common biomarkers associated with cancer that can be used for treatment, diagnosis and assessment of prognosis, large quantities of cancer microarray and high-throughput sequence data has been reported and become available over recent years ( 8 , 9 ). In addition, to overcome the limitations caused by small sample sizes, differential platform data and standards, bioinformatics are becoming increasingly popular in the field of cancer biology, which have yielded valuable information ( 8 ).…”