PCMT1 confirmed as a pan-cancer immune biomarker and a contributor to breast cancer metastasis.

Protein L-isoaspartyl (D-aspartyl) methyltransferase (PIMT, gene name PCMT1) is an enzyme that repairs proteins with altered aspartate residues by methylation, restoring their normal structure and function. This study conducted a comprehensive analysis of PCMT1 in pan-cancer. The Cancer Genome Atlas, Human Protein Atlas website, and the Genotype-Tissue Expression were utilized in analysis of PCMT1 expression. We examined the association between PCMT1 expression and various factors, including gene modifications, DNA methylation, immune cell infiltration, immunological checkpoints, drug susceptibility, tumor mutation burden (TMB), and microsatellite instability (MSI). Enrichment analyses determined the potential biological roles and pathways involving PCMT1. Our focus then shifted to the role of PCMT1 in breast invasive carcinoma (BRCA). We found that PCMT1 expression was aberrant in many tumors and significantly influenced the prognosis across several cancer types. Gene alterations in PCMT1 predominantly involved deep deletions and amplifications. A negative correlation was observed between DNA methylation and PCMT1 expression across all studied cancer types except thyroid carcinoma PCMT1 exhibited positive correlations with common lymphoid progenitor and CD4(+) T helper 2 cells, whereas it was inversely correlated with central and effector memory T cells, memory CD8(+) T cells, and CD4(+) T helper 1 cells. In many cancer types, PCMT1 expression closely correlated with immunological checkpoint inhibitors, TMB, and MSI. It was also significantly linked to pathways involved in epithelial-mesenchymal transition (EMT), highlighting its role in cancer metastasis. PCMT1 emerged as a significant predictor of breast cancer progression. In vitro experiments demonstrated that reducing PCMT1 expression decreased BRCA cell migration and invasiveness. Additionally, animal studies confirmed that inhibition of PCMT1 slowed tumor growth.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma.

Background: It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear. Methods: Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach. Results: Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot. Conclusions: An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.

Targeting survivin with Tanshinone IIA inhibits tumor growth and overcomes chemoresistance in colorectal cancer.

The inhibitor of apoptosis protein survivin has a critical regulatory role in carcinogenesis and treatment tolerance in colorectal cancer (CRC). However, the targeted drugs for survivin protein are extremely limited. In the present research, we discovered that Tanshinone IIA (Tan IIA) played a dual regulatory role in inhibiting tumorigenesis and reversing 5-Fu tolerance via modulating the expression and phosphorylation of survivin in CRC cells. Mechanistically, Tan IIA suppressed the Akt/WEE1/CDK1 signaling pathway, which led to the downregulation of survivin Thr34 phosphorylation and destruction of the interaction between USP1 and survivin to promote survivin ubiquitination and degradation. Furthermore, Tan IIA significantly facilitated chemoresistant CRC cells to 5-Fu sensitivity. These results revealed that Tan IIA possessed a strong antitumor activity against CRC cells and could act as an up-and-coming agent for treating CRC and overcoming chemotherapy resistance.

Breast cancer combined with contralateral neck lymph node metastasis: a case report.

BACKGROUND: Contralateral neck lymph node metastasis is rare in primary breast cancer. Its clinical staging and treatment principles lack authoritative guidelines. A case of a 30-year-old breast cancer patient with contralateral neck lymph node metastasis is presented. The clinical treatment is discussed in combination with current research. CASE PRESENTATION: A 30-year-old woman presented with a right breast mass for 5 months and left neck lymph node enlargement for 5 days. Mammography showed a 33 mm*14.3 mm mass in the inner quadrant of the right breast. The ultrasound showed several hypoechoic nodules on the left side of the neck. Rapid intraoperative pathological examination diagnosed a right breast malignant tumor and poorly differentiated carcinoma of the left cervical lymph nodes. Then, right mastectomy was performed immediately. The patient was scheduled to undergo chemotherapy, molecular targeted therapy, radiotherapy and endocrine therapy after the operation. The long-term efficacy remains to be seen. CONCLUSION: The infrequent presentation of breast cancer with metastasis to the contralateral neck lymph node can be challenging for standard therapies.

Identification of N6-Methyladenosine-Related LncRNAs for Predicting Overall Survival and Clustering of a Potentially Novel Molecular Subtype of Breast Cancer.

We aimed to identify a signature comprising N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) and molecular subtypes associated with breast cancer (BRCA). We obtained data of BRCA samples from The Cancer Genome Atlas database. The m6A-related lncRNA prognostic signature (m6A-LPS) included 10 lncRNAs previously identified as prognostic m6A-related lncRNAs and was constructed using integrated bioinformatics analysis and validated. Accordingly, a risk score based on the m6A-LPS signature was established and shown to confirm differences in survival between high-risk and low-risk groups. Three distinct genotypes were identified, whose characteristics included features of the tumor immune microenvironment in each subtype. Our results indicated that patients in Cluster 2 might have a worse prognostic outcome than those in other clusters. The three genotypes and risk subgroups were enriched in different biological processes and pathways, respectively. We then constructed a competing endogenous RNA network based on the prognostic m6A-related lncRNAs. Finally, we validated the expression levels of target lncRNAs in 72 clinical samples. In summary, the m6A-LPS and the potentially novel genotype may provide a theoretical basis for further study of the molecular mechanism of BRCA and may provide novel insights into precision medicine.