A new bioinformatics study suggests a novel and reliable predictive signature for bladder cancer. Researchers from the First Affiliated Hospital of Nanjing Medical University identified a list of survival-associated metabolism-related genes. They developed a signature for bladder cancer prognosis prediction.
Published in Genomics, the work demonstrated that the signature possessed a powerful predictive ability and was identified as an independent prognostic factor.
Globally, bladder cancer has become one of most prevalent cancers, with over half a million new cases reported annually. While therapeutic options have developed in recent years and provided patients with prolonged survival opportunities, prognosis remains relatively poor. Current predictive methods have severe limitations and are often unreliable. Identifying reliable biomarkers of prognosis in bladder cancer is valuable and necessary.
The reprogramming of metabolism is becoming a novel hallmark of cancer. Tumour cells can reprogram their metabolic systems in a process called metabolic coupling. With the increasing application of bioinformatics analysis, metabolome has been found to be connected with the genome in the diagnosis and prognosis prediction of some kinds of malignant tumours.
Raw RNA-sequencing identified over 300 metabolism-related genes
The team performed bioinformatics analyses of metabolism-related genes in bladder cancer.
Raw RNA-sequencing (RNA-Seq) data of bladder urothelial carcinoma (BLCA) tissues and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. Clinical information, including age, gender, tumour size, metastasis status, survival status and survival time for these TCGA-BLCA patients was also compiled for further analyses.
A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Further analyses identified 16 survival-associated metabolism-related genes that the authors proposed could be useful for prognosis.
Creating a risk score
Taking survival time and clinical information into consideration, the team constructed a risk score to predict clinical prognosis.
Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that the risk score was an independent prognostic indicator in bladder cancer. Subsequent analyses proved that the risk score had a better ability to predict prognosis than other individual indicators.
However, the bioinformatics study has some limitations. The authors acknowledged that the work was a retrospectively designed study, which could cause potential bias in unbalanced clinical or pathological features. Looking to the future, the potential relationship between metabolic genes and clinical outcomes should be further explored.
Nevertheless, the authors stated, “This signature was confirmed to be an effective prognostic biomarker in bladder cancer.”
Written by Poppy Jayne Morgan, Front Line Genomics
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