Multiple Omics Data Integration to Identify Long Noncoding RNA Responsible for Breast Cancer-Related Mortality

Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data

Osteosarcoma is the most common primary malignancy in the musculoskeletal system. It is reported that copy number variation- (CNV-) derived lncRNAs contribute to the progression of osteosarcoma. However, whether CNV-derived lncRNAs affect the prognosis of osteosarcoma remains unclear. Here, we obtained osteosarcoma-related CNV data and gene expression profiles from The Cancer Genome Atlas (TCGA) database. CNV landscape analysis indicated that copy number amplification of lncRNAs was more frequent than deletion in osteosarcoma samples. Thirty-four CNV-lncRNAs with DNA-CNV frequencies greater than 30% and their corresponding 294 mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses revealed that these mRNAs were mainly enriched in olfaction, olfactory receptor activity, and olfactory transduction processes. Furthermore, we predicted that a total of 23 genes were cis-regulated by 16 CNV-lncRNAs, while 30 transcription factors (TFs) were trans-regulated by 5 CNV-lncRNAs. Through $t$ -tests, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO), we constructed a CNV-related risk model including 3 lncRNAs (AC129492.1, PSMB1, and AC037459.4). The Kaplan-Meier (K-M) curves indicated that patients with high-risk scores showed poor prognoses. The areas under the receiver operating characteristic (ROC) curves (AUC) for predicting 3-, 5-, and 7-year overall survival (OS) were greater than 0.7, showing a satisfactory predictive efficiency. Gene set enrichment analysis (GSEA) revealed that the prognostic signature was intimately linked to skeletal system development, immune regulation, and inflammatory response. Collectively, our study developed a novel 3-CNV-lncRNA prognostic signature that would provide theoretical guidance for the clinical prognostic management of osteosarcoma.

Prediction and prognostic significance of ALOX12B and PACSIN1 expression in gastric cancer by genome-wide RNA expression and methylation analysis

Background

Stomach adenocarcinoma (STAD) is one of the common gastrointestinal cancers, characterized by late discovery and metastasis. However, research of gene methylation and expression in gastric cancer (GC) metastasis has been quite limited. This study aimed to investigate the altered gene expression patterns between metastasis and non-metastasis samples using high-throughput RNA and methylation profiles from a large number of patients. Another aim was to identify a specific potential metastasis biomarker, with the ability to predict the metastasis possibility and prognosis of patients with STAD.

Methods

In this study, we integrated The Cancer Genome Atlas (TCGA) program STAD datasets, analyzed the RNA expression and DNA methylation data between non-metastasis (M0) and distant metastasis (M1) samples, and evaluated the candidate biomarker in survival and prognosis of GC.

Results

Among all patients enrolled, 329 with M0 and M1 information were positive for RNA analysis, and 353 with M0 and M1 information were positive for methylation analysis. We found 29 upregulated and 200 downregulated genes in RNA level, and 5,046 hypermethylated and 8,563 hypomethylated probes in methylation level. Among these genes, we found high RNA expression level and low DNA methylation level of ALOX12B and PACSIN1 in GC metastasis samples. Patients with high expression of these 2 genes had poor overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS).

Conclusions

The expression levels of ALOX12B and PACSIN1 were higher in the metastasis than non-metastasis group, and participants with high expression of these 2 genes were found to have poor survival. The genes ALOX12B and PACSIN1 are potential biomarkers of metastasis and poor prognosis, especially in early stage GC, and provide additional information for subsequent comprehensive treatment of GC.

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.