Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Abstract

Background Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. Results We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. Conclusions This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Differential DNA Methylation in Prostate Tumors from Puerto Rican Men

“…Troponin I2 ( TNNI2 ) was identified as a candidate biomarker for prediction of poor outcomes in various cancers. Overexpression of TNNI2 is associated with recurrence and metastasis in gastric cancer [ 39 ] and with poor survival in lung cancer [ 40 ]. TNNI2 was hypomethylated in liver tumor [ 41 ].…”

Selected Research Articles from the 2019 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)

“…After the workshop, ten original research papers [1][2][3][4][5][6][7][8][9][10] were accepted for publication in the CNB-MAC 2019 partner journals: BMC Bioinformatics and BMC Genomics. In the following we provide a brief summary of these selected papers.…”

“…Zengin and Önal-Süzek [9] propose a reusable and open-source R pipeline for the discovery of prognostic signatures by the integration of multiple dimensions of TCGA Lung cancer (LUAD) dataset. The authors generate 4 different gene categories using the significant SNVs, CNVs, DEGs and active subnetwork DEGs.…”

“…Byung-Jun Yoon, Xiaoning Qian, Tamer Kahveci, and Ranadip Pal. The workshop featured original research papers [1][2][3][4][5][6][7][8][9][10], a highlight presentation of a recently published journal paper [11], and poster presentations [12,13], which were selected by the workshop chairs based on the reviews performed by the technical committee members. Reports from previous CNB-MAC workshops are available at [14][15][16].…”

Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinoma

“…of 30Expression signature for LUAD consists of 35 gene which 27 of are protein-coding genes while 2 of them are long intergenic non-protein coding RNA, 1 is antisense RNA, 3 of them are pseudogenes and 2 of them are novel transcripts. Many of the coding genes are lung cancer or other cancer types related such as ADAMTS15, CCDC181 and IRX2 Long intergenic non-protein coding RNA, LINC01426, promotes cancer progression via AZGP1 and predicts poor prognosis in patients with LUAD[54].COL28A1 has prognostic values and is a potential tumor suppressor identified bySomInaClust R package in our previous analysis[55]. And the many genes such as ADAMTS17, JHY, PLAAT1, PNMA8B, RPL37P6, SNX32, UGGT2 and Y_RNA have not been related with any cancer, yet.Gene expression signatures of LUAD and LUSC share 8 pathways which are mostly metabolic pathways.…”