Study reveals novel bladder cancer diagnostic model based on key mitochondrial genes

With millions of cases of bladder cancer (BC) worldwide, there is concern about the need for tools that can diagnose this condition in a timely manner. Scientists recently used mitochondria-related genes (MRGs), known to be involved in disease progression, to build a new diagnostic model using machine learning (ML).

The results were published in Scientific Reports and demonstrated the potential of this model, which awaits further validation.

Research: Machine learning prediction of a new diagnostic model using mitochondria-related genes for bladder cancer patients. Image credit: mi_viri/Shutterstock.com

bladder cancer

Bladder cancer is three to four times more common in men than women, making it the sixth leading cause of cancer in men. It is primarily caused by smoking and exposure to certain industrial chemicals and usually affects middle-aged and older men.

Bladder cancer is more prevalent in developed countries, but despite medical advances, its prognosis remains relatively poor. This has facilitated the development of better diagnostic tools, prognostic models, and therapeutic approaches.

Mitochondria, cellular organelles responsible for energy production, control cellular metabolism and regulate important cellular processes such as programmed cell death, signal transduction, and calcium ion levels.

Tumor cells, which require large amounts of energy, rely primarily on glycolysis (an inefficient (anaerobic route).

This difference in energy production is part of the “Warburg effect,” in which abnormal mitochondrial function causes changes in tumor cell metabolism. For example, malfunctioning mitochondria can prevent cancer cells from undergoing their programmed death, allowing them to survive and spread.

In addition, mitochondrial abnormalities can cause oxidative stress on cellular components such as DNA and proteins, increasing cancer risk, conferring resistance to cancer treatments, and promoting tumor growth.

“Given the important role of MRG in BC progression, it is important to screen novel MRG-based biomarkers for BC patients.”

ML forms part of the artificial intelligence (AI) arsenal, identifying patterns and knowledge from raw data without detailed instructions.

This allows the system to predict, classify, and recognize trends that may include tumor-associated transcriptional patterns. In the current study, researchers sought to leverage the power of ML on the transcriptome to build a new diagnostic model for BC based on MRG.

What did the research show?

Researchers analyzed 165 bladder cancer (BC) samples and 67 controls to study differential expression of mitochondria-related genes (MRGs). They identified 752 differentially expressed MRGs, of which 440 showed increased expression and the rest were downregulated.

These genes were significantly involved in cellular pathways related to organogenesis, cell fate, transcriptional regulation, neurodegenerative diseases, and muscle tissue disorders in the embryo.

This analysis identified approximately 50 BC-related features, which were narrowed down to 13 important genes. Among these, TRAF3-interacting protein 3 (TRAF3IP3), oxidative stress-induced growth inhibitor mitochondria (OXSM), N-myristoyltransferase 1 (NMT1), and glutaredoxin 2 (GLRX2) were found to be important targets. Did. GLRX2 in particular is important in maintaining the redox balance within mitochondria, which helps normal cellular processes continue without oxidative damage.

The expression patterns of GLRX2, NMT1, OXSM, and TRAF3IP3 showed obvious differences between BC samples and controls, achieving 90% efficacy in differentiation. GLRX2, NMT1, and OXSM were highly upregulated in BC, whereas TRAF3IP3 was significantly decreased.

These findings were consistent across two additional datasets, demonstrating that this model can distinguish BC from control samples more effectively than single-gene biomarkers.

Additionally, this study investigated where these genes are primarily expressed and found that different pathways and immune cells within the tumor microenvironment respond differently to changes in gene regulation. For example, higher levels of activated natural killer (NK) cells and plasma cells were associated with increased GLRX2 expression.

Significantly elevated NMT1 expression in several BC cell lines encodes a protein important for protein modification and signal transduction, and inhibits tumor cell-extracellular matrix interaction, a key process in cancer spread. may be enhanced. Importantly, inhibiting NMT1 inhibited BC cell proliferation, indicating a role for NMT1 in BC progression.

conclusion

The advent of transcriptomics and ML in tumor diagnostic models has driven the quest for accurate and early diagnosis of BC without the need for invasive and painful biopsies. This ML approach helps in developing personalized diagnosis and treatment plans based on biomarker selection.

It can also improve efficiency and speed decision-making. Finally, it can help understand the process of tumor development by providing insight into the underlying biology of tumors.

The current study identified four genes (GLRX2, NMT1, OXSM, and TRAF3IP3) for BC diagnosis. These were incorporated into the diagnostic model. They were also found to play an important role in the progression of BC. Further studies are essential to confirm these findings in a more diverse sample.

“Our findings may lead to increased accuracy and reliability in the diagnosis of BC and may contribute to more personalized and effective medical interventions for patients in the future.”

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