Prognostic, diagnostic and clinicopathological roles of tsRNAs: a meta-analysis in breast cancer

Background Colorectal cancer (CRC) is a highly aggressive, high-incidence malignancy. CRC accounted for approximately one out of every ten cancer cases and deaths. Although miRNAs are often used for medical diagnostic purposes, their diagnostic effectiveness in CRC remains uncertain. Methods Therefore, from January 2016 to April 2024, we conducted a comprehensive search of China National Knowledge Internet (CNKI), PubMed, Cochrane Library, Web of Science (WoS) and other resources. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC) and Fagan plot analysis were used to assess the overall test performance of machine learning approaches. Moreover, we evaluated the publication bias by the Deeks’funnel plot asymmetry test. Results Ultimately, a total of 23 publications were identified and incorporated into this meta-analysis. The aggregated diagnostic data were as follows: The sensitivity of the test was 0.83, with a 95% confidence interval of 0.81–0.84. The specificity was found to be 0.83 with a 95% confidence interval (CI) of 0.81–0.84. The PLR was 4.60 with a 95% CI of 3.77–5.62. The NLR was 0.22 with a 95% CI of 0.17–0.27. The DOR was 23.79 with a 95% CI of 16.26–34.81. The AUC was 0.90 with a 95% CI of 0.87–0.92. The Deek funnel plot suggests that publication bias has no statistical significance. The Fagan plot analysis that the positive probability is 50% and the nagative probability is 5%. Conclusion In summary, our results suggest the high accuracy of miRNAs in diagnosing CRC.

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SVGs as Therapeutic Targets and Biomarkers in Cancer

Gupta,

Bhardwaj

2025

Advances in Medical Diagnosis, Treatment, and Care

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The advent of precision oncology has brought about a paradigm shift in the management of cancer, with increased emphasis on personalized treatment approaches. The key aspects of this approach are to identify spatially variable genes, which exhibit significant expression differences across distinct regions of a tumor. These spatially variable genes have emerged as potential therapeutic targets and biomarkers in cancer, as they reflect the inherent heterogeneity within the disease. By capturing the spatial variability of gene expression across a tumor, researchers and clinicians learn important things about the underlying biology and develop more targeted and effective treatment strategies for individual patients. The recent advances in spatial variable genes and the associated methods in oncology are the main topics of this chapter. By understanding how gene expression varies spatially within tumors, we can potentially improve diagnostic accuracy, treatment efficacy, and patient outcomes in oncology.

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Prognostic, diagnostic and clinicopathological roles of tsRNAs: a meta-analysis in breast cancer

Cited by 3 publications

References 46 publications

Exploring the regulatory role of tsRNAs in the TNF signaling pathway: Implications for cancer and non-cancer diseases

Exploring the regulatory role of tsRNAs in the TNF signaling pathway: Implications for cancer and non-cancer diseases

Diagnostic Accuracy of miRNA to Identify Colorectal cancer: A Systematic Review and Meta-Analysis

SVGs as Therapeutic Targets and Biomarkers in Cancer

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