The Crosstalk Between Regulatory Non-Coding RNAs and Nuclear Factor Kappa B in Hepatocellular Carcinoma

Meta-analysis of the GALAD model for diagnosing primary hepatocellular carcinoma

BACKGROUND

Ever since the GALAD (gender-age-Lens culinaris agglutinin-reactive alpha-fetoprotein-alpha-fetoprotein-des-gamma-carboxy prothrombin) logistic regression model was established to diagnose hepatocellular carcinoma (HCC), there has been no high-level evidence that evaluates and summarizes it.

OBJECTIVE

This meta-analysis was performed to assess the diagnostic ability of the GALAD model.

METHODS

The following databases were systematically searched for original diagnostic studies on HCC: PubMed, Embase, Medline, the Web of Science, Cochrane Library, China National Knowledge Infrastructure Wanfang (China), Wiper and the Chinese BioMedical Literature Database. After screening the search results according to our criteria, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the methodologic qualities, and statistical software were used to output the statistics.

RESULTS

Ultimately, 10 studies were included and analyzed. The results revealed the pooled sensitivity and specificity of the GALAD model to be 0.86 (95% confidence interval [CI]: 0.82, 0.90) and 0.90 (95% CI: 0.87, 0.92), respectively, for all-stage HCC. The area under the curve (AUC) was 0.94. For early-stage HCC, the pooled sensitivity and specificity of the GALAD model were 0.83 (95% CI: 0.78, 0.87) and 0.81 (95% CI: 0.78, 0.83), respectively. The AUC was 0.90.

CONCLUSION

This meta-analysis confirmed that the GALAD model has excellent diagnostic performance for early-stage and all-stage HCC and can maintain high sensitivity and specificity in early-stage HCC. Therefore, the GALAD model is qualified for screening early-stage canceration from chronic liver disease.

lncRNA-microRNA axis in cancer drug resistance: particular focus on signaling pathways

Cancer drug resistance remains a formidable challenge in modern oncology, necessitating innovative therapeutic strategies. The convergence of intricate regulatory networks involving long non-coding RNAs, microRNAs, and pivotal signaling pathways has emerged as a crucial determinant of drug resistance. This review underscores the multifaceted roles of lncRNAs and miRNAs in orchestrating gene expression and cellular processes, mainly focusing on their interactions with specific signaling pathways. Dysregulation of these networks leads to the acquisition of drug resistance, dampening the efficacy of conventional treatments. The review highlights the potential therapeutic avenues unlocked by targeting these non-coding RNAs. Developing specific inhibitors or mimics for lncRNAs and miRNAs, alone or in combination with conventional chemotherapy, emerges as a promising strategy. In addition, epigenetic modulators, immunotherapies, and personalized medicine present exciting prospects in tackling drug resistance. While substantial progress has been made, challenges, including target validation and safety assessment, remain. The review emphasizes the need for continued research to overcome these hurdles and underscores the transformative potential of lncRNA-miRNA interplay in revolutionizing cancer therapy.