Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression

MicroRNA-21 (miR-21) in breast cancer: From apoptosis dysregulation to therapeutic opportunities

Breast cancer, a pervasive and complex disease, continues to pose significant challenges in the field of oncology. Its heterogeneous nature and diverse molecular profiles necessitate a nuanced understanding of the underlying mechanisms driving tumorigenesis and progression. MicroRNA-21 (miR-21) has emerged as a crucial player in breast cancer development and progression by modulating apoptosis, a programmed cell death mechanism that eliminates aberrant cells. MiR-21 overexpression is a hallmark of breast cancer, and it is associated with poor prognosis and resistance to conventional therapies. This miRNA exerts its oncogenic effects by targeting various pro-apoptotic genes, including Fas ligand (FasL), programmed cell death protein 4 (PDCD4), and phosphatase and tensin homolog (PTEN). By suppressing these genes, miR-21 promotes breast cancer cell survival, proliferation, invasion, and metastasis. The identification of miR-21 as a critical regulator of apoptosis in breast cancer has opened new avenues for therapeutic intervention. This review investigates the intricate mechanisms through which miR-21 influences apoptosis, offering insights into the molecular pathways and signaling cascades involved. The dysregulation of apoptosis is a hallmark of cancer, and understanding the role of miR-21 in this context holds immense therapeutic potential. Additionally, the review highlights the clinical significance of miR-21 as a diagnostic and prognostic biomarker in breast cancer, underscoring its potential as a therapeutic target.

Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression

Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.

miR-125 in Breast Cancer Etiopathogenesis: An Emerging Role as a Biomarker in Differential Diagnosis, Regenerative Medicine, and the Challenges of Personalized Medicine

Breast Cancer (BC) is one of the most common cancer types worldwide, and it is characterized by a complex etiopathogenesis, resulting in an equally complex classification of subtypes. MicroRNA (miRNA or miR) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to tumor development and angiogenesis in different types of cancer. Recently, complex interactions among coding and non-coding RNA have been elucidated, further shedding light on the complexity of the roles these molecules fulfill in cancer formation. In this context, knowledge about the role of miR in BC has significantly improved, highlighting the deregulation of these molecules as additional factors influencing BC occurrence, development and classification. A considerable number of papers has been published over the past few years regarding the role of miR-125 in human pathology in general and in several types of cancer formation in particular. Interestingly, miR-125 family members have been recently linked to BC formation as well, and complex interactions (competing endogenous RNA networks, or ceRNET) between this molecule and target mRNA have been described. In this review, we summarize the state-of-the-art about research on this topic.

miR-133b Promotes Esophageal Squamous Cell Carcinoma Metastasis

Background

Evaluation of biological changes at the molecular level has important clinical implications for improving the survival rate of esophageal squamous cell carcinoma (ESCC). Therefore, we plan to analyze and elucidate the expression of microRNA-133b (miR-133b), M2 pyruvate kinase (PKM2), and signal transducer and activator of transcription 3 (STAT3) in ESCC and their associated clinicopathological significance.

Methods

The 72 patients with ESCC were selected as the experimental study group. Normal adjacent tissues (NAT) were matched as the control group. In this study, in situ hybridization was used to detect the expression of miR-133b in ESCC, and tissue expressions of PKM2 and STAT3 were detected by immunohistochemistry, and literature review was conducted.

Results

Studies had shown that the positive expression of miR-133b in NAT was significantly higher than that in ESCC (χ2 = 9.007, P = .003). PKM2 and STAT3 in ESCC had a significantly higher positive expression levels than those of NAT (χ2 = 56.523, P = .000; χ2 = 72.939, P = .000). From correlation analysis, there was a negative correlation between miR-133b and PKM2(r = -0.515, P < .001), a negative correlation between miR-133b and STAT3(r = -0.314, P = .007), and a positive correlation between PKM2 and STAT3(r = 0.771, P < .001).

Conclusions

In ESCC, our study demonstrated that downregulation of miR-133b and upregulation of PKM2 and STAT3. We predict that miR-133b may inhibit the STAT3 pathway by downregulating PKM2.