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Abstract Artificial intelligence (AI)‐driven analysis of comprehensive clinical parameters is bringing about a significant transformation in traditional routine clinical laboratory testing. This transformation impacts the prediction, prevention, diagnosis, and prognosis of human diseases. AI possesses the capability to efficiently analyze and process vast and intricate datasets, thereby facilitating the development of diverse and efficient diagnostic or predictive models. This advancement is fueling significant improvements in laboratory quality, automation, and the accuracy of diagnoses. In this context, we conducted a thorough review and discussion on the progression of AI applications in clinical laboratory medicine, encompassing advancements, implementation, and challenges. Our conclusion underscores that integrating AI into clinical laboratory testing will notably propel personalized precision medicine forward and enhance diagnostic accuracy, especially benefiting patients for whom accurate diagnoses are elusive through traditional laboratory testing systems.

Abstract Malignant breast cancer (BC) remains incurable mainly due to the cancer cell metastasis, which is mostly related to the status of Estrogen receptor alpha (ERα). However, our understanding of the mechanisms through which ERα regulates cancer cell metastasis remains limited. Here we identified a miR-29a-PTEN-AKT axis as a downstream signaling pathway of ERα governing breast cancer progression and metastasis. Two estrogen response element (ERE) half sites were identified in the promoter and enhancer regions of miR-29a, which mediated transcriptional regulation of miR-29a by ERα. Low level of miR-29a showed association with reduced metastasis and better survival in ERα+ luminal subtype of BC. In contrast, high level of miR-29a was detected in ERα- triple negative breast cancer (TNBC) in association with distant metastasis and poor survival. miR-29a overexpression in BC tumors increased the number of circulating tumor cells and promoted lung metastasis in mice. Targeted knockdown of miR-29a in TNBC cells in vitro or administration of a nanotechnology-based anti-miR-29a delivery in TNBC tumor-bearing mice in vivo suppressed cellular invasion, EMT and lung metastasis. PTEN was identified as a direct target of miR-29a, inducing EMT and metastasis via AKT signaling. A small molecular inhibitor of AKT attenuated miR-29a-induced EMT. These findings demonstrate a novel mechanism responsible for ERα-regulated breast cancer metastasis, and reveal the combination of ERα status and miR-29a levels as a new risk indicator in BC.

Abstract Objective: Breast cancer (BC) is becoming the leading cause of cancer-related death in women all over the word. Identification of diagnostic biomarkers for early detection of BC is one of the most effective ways to reduce the mortality.Methods: Plasma samples from BC patients (n = 120) and normal controls (n = 50) were collected to determine the differentially expressed circulating miRNAs in BC patients. Binary logistic regression was applied to develop miRNA diagnostic models. Receiver operating characteristic (ROC) curves were applied to calculate the area under the curve (AUC). MMTV-PYMT mammary tumor mice were used to validate the expression change of those circulating miRNAs. Plasma samples from patients with other tumor types were collected to determine the specificity of the model in diagnosis of BC.Results: In the screening phase, 5 circulating miRNAs (miR-16, miR-17, miR-19b, miR-27a, and miR-106a) were identified as the most significantly upregulated miRNAs in plasma of BC patients. In consistence, the 5 miRNAs showed upregulation in the circulation of additional 80 BC patients in a tumor stage-dependent manner. Application of a tumor-burden mice model further confirmed upregulation of the 5 miRNAs in circulation. Based on these data, five models with diagnostic potential of BC were developed. Among the 5 miRNAs, miR-19b ranked at the top position with the highest specificity and the biggest contribution. In combination with miR-16 and miR-106a, a miR-19b-based 3-circulating miRNA model was selected as the best for further validation. Taken the samples together, the model showed 92% of sensitivity and 90% of specificity in diagnosis of BC. In addition, three other tumor types including prostate cancer, thyroid cancer and colorectal cancer further verified the specificity of the BC diagnostic model. Conclusion: The current study developed a miR-19b-based 3-miRNA model holding potential for diagnosis of BC using blood samples.

P-element-induced wimpy testis (Piwi)-interacting RNAs (piRNAs) are a class of germline-enriched small non-coding RNA that associate with Piwi family proteins and mostly induce transposon silencing and epigenetic regulation. Emerging evidence indicated the aberrant expression of Piwil proteins and associated piRNAs in multiple types of human cancer including breast cancer. Although the majority of piRNAs in breast cancer remains unclear of the function mainly due to the variety of regulatory mechanisms, the potential of piRNAs serving as biomarkers for cancer diagnosis and prognosis or therapeutic targets for cancer treatment has been demonstrated by in vitro and in vivo studies. Herein we summarized the research progress of oncogenic or tumor suppressing piRNAs and their regulatory mechanisms in regulating human breast cancer, including piR-021285, piR-823, piR-932, piR-36712, piR-016658, piR-016975 and piR-4987. The challenges and perspectives of piRNAs in the field of human cancer were discussed.