A comprehensive analysis of cancer-driving mutations and genes in kidney cancer

DrGA: cancer driver gene analysis in a simpler manner

Background

To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient.

Results

This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism.

Conclusions

DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA.

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.

SETD2, GIGYF2, FGFR3, BCR, KMT2C, and TSC2 as candidate genes for differentiating multilocular cystic renal neoplasm of low malignant potential from clear cell renal cell carcinoma with cystic change

Purpose

Multilocular cystic renal neoplasm of low malignant potential (MCRNLMP) and clear cell renal cell carcinoma with cystic change (MCRCC) have different prognoses despite similar histologic characteristics. The aim of this study was to identify differentially mutated genes in resected tumor specimens from patients diagnosed with MCRNLMP and MCRCC using a kidney cancer gene panel.

Materials and Methods

Between 2009 and 2016, 13 MCRNLMP and 17 MCRCC cases were selected. Tumor tissues from 5 MCRNLMP and 16 MCRCC cases were subjected to gene sequencing to detect mutations among 88 genes selected from a kidney cancer gene panel after quality control. Fisher's exact test was used to compare gene mutation profiles between the two diseases. Genes were considered to be positive for mutation according to the presence of an in-frame/frameshift deletion or insertion, missense/nonsense mutation, or multi-hit mutation.

Results

During a median follow-up period of 66.2 months, there was only one case of MCRCC recurrence among all 30 patients. Target gene sequencing showed that 35 genes tended to be more frequently positive in either disease group, with six genes showing a significantly different frequency of mutation between the groups: GIGYF2 (odds ratio [OR], 5.735), FGFR3 (OR, 6.787), SETD2 (OR, 4.588), BCR (OR, 6.266), KMT2C (OR, 8.167), and TSC2 (OR, 4.474).

Conclusions

Six candidate genes showed significantly different mutation patterns between MCRNLMP and MCRCC, providing insight into their pathogenic mechanisms and differential prognoses.