Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations

Programmed cell death-index (PCDi) as a prognostic biomarker and predictor of drug sensitivity in cervical cancer: a machine learning-based analysis of mRNA signatures

Purpose: Cervical cancer is a significant public health concern, particularly in developing countries. Despite available treatment strategies, the prognosis for patients with locally advanced cervical cancer and beyond remains poor. Therefore, an accurate prediction model that can reliably forecast prognosis is essential in clinical setting. Programmed cell death (PCD) mechanisms are diverse and play a critical role in tumor growth, survival, and metastasis, making PCD a potential reliable prognostic marker for cervical cancer. Methods: In this study, we created a novel prognostic indicator, programmed cell death-index (PCDi), based on a 10-fold cross-validation framework for comprehensive analysis of PCD-associated genes. Results: Our PCDi-based prognostic model outperformed previously published signature models, stratifying cervical cancer patients into two distinct groups with significant differences in overall survival prognosis, tumor immune features, and drug sensitivity. Higher PCDi scores were associated with poorer prognosis. The nomogram survival model integrated PCDi and clinical characteristics, demonstrating higher prognostic prediction performance. Furthermore, our study investigated the immune features of cervical cancer patients and found that those with high PCDi scores had lower infiltrating immune cells, lower potential of T cell dysfunction, and higher potential of T cell exclusion. Patients with high PCDi scores were resistant to classic chemotherapy regimens, including cisplatin, docetaxel, and paclitaxel, but showed sensitivity to the inhibitor SB505124 and Trametinib. Conclusion: Our findings suggest that PCD-related gene signature could serve as a useful biomarker to reliably predict prognosis and guide treatment decisions in cervical cancer.

Predictive biomarkers for colorectal cancer: a state-of-the-art systematic review

INTRODUCTION

Colorectal cancer (CRC) poses a substantial health burden, with early detection paramount for improved prognosis. This study aims to evaluate potential CRC biomarkers and detection techniques.

MATERIALS AND METHODS

This systematic review, reported in adherence to PRISMA Statement 2020 guidelines, collates the latest research on potential biomarkers and detection/prognosis methods for CRC, spanning the last decade.

RESULTS

Out of the 38 included studies, diverse biomarkers and detection methods emerged, with DNA methylation markers like SFRP2 and SDC2, microRNAs including miR-1290, miR-506, and miR-4316, and serum and plasma markers such as NTS levels and U2 snRNA fragments standing out. Methylated cfDNA and m5C methylation alteration in immune cells of the blood, along with circular RNA, showed promise as diagnostic markers. Meanwhile, techniques involving extracellular vesicles and lateral flow immunoassays exhibited potential for swift and effective CRC screening.

DISCUSSION

Our state-of-the-art review identifies potential biomarkers, including SFRP2, SDC2, miR-1290, miR-506, miR-4316, and U2 snRNA fragments, with significant potential in enhancing CRC detection. However, comprehensive validation studies and a rigorous evaluation of clinical utility and cost-effectiveness remain necessary before integration into routine clinical practice.

CONCLUSION

The findings emphasize the need for continued research into biomarkers and detection methods to improve patient outcomes.