Machine Learning Algorithms Identify Clinical Subtypes and Cancer in Anti-TIF1γ+ Myositis: A Longitudinal Study of 87 Patients.

BACKGROUND: Anti-TIF1γ antibodies are a class of myositis-specific antibodies (MSAs) and are closely associated with adult cancer-associated myositis (CAM). The heterogeneity in anti-TIF1γ+ myositis is poorly explored, and whether anti-TIF1γ+ patients will develop cancer or not is unknown at their first diagnosis. Here, we aimed to explore the subtypes of anti-TIF1γ+ myositis and construct machine learning classifiers to predict cancer in anti-TIF1γ+ patients based on clinical features. METHODS: A cohort of 87 anti-TIF1γ+ patients were enrolled and followed up in Xiangya Hospital from June 2017 to June 2021. Sankey diagrams indicating temporal relationships between anti-TIF1γ+ myositis and cancer were plotted. Elastic net and random forest were used to select and rank the most important variables. Multidimensional scaling (MDS) plot and hierarchical cluster analysis were performed to identify subtypes of anti-TIF1γ+ myositis. The clinical characteristics were compared among subtypes of anti-TIF1γ+ patients. Machine learning classifiers were constructed to predict cancer in anti-TIF1γ+ myositis, the accuracy of which was evaluated by receiver operating characteristic (ROC) curves. RESULTS: Forty-seven (54.0%) anti-TIF1γ+ patients had cancer, 78.7% of which were diagnosed within 0.5 years of the myositis diagnosis. Fourteen variables contributing most to distinguishing cancer and non-cancer were selected and used for the calculation of the similarities (proximities) of samples and the construction of machine learning classifiers. The top 10 were disease duration, percentage of lymphocytes (L%), percentage of neutrophils (N%), neutrophil-to-lymphocyte ratio (NLR), sex, C-reactive protein (CRP), shawl sign, arthritis/arthralgia, V-neck sign, and anti-PM-Scl75 antibodies. Anti-TIF1γ+ myositis patients can be clearly separated into three clinical subtypes, which correspond to patients with low, intermediate, and high cancer risk, respectively. Machine learning classifiers [random forest, support vector machines (SVM), extreme gradient boosting (XGBoost), elastic net, and decision tree] had good predictions for cancer in anti-TIF1γ+ myositis patients. In particular, the prediction accuracy of random forest was >90%, and decision tree highlighted disease duration, NLR, and CRP as critical clinical parameters for recognizing cancer patients. CONCLUSION: Anti-TIF1γ+ myositis can be separated into three distinct subtypes with low, intermediate, and high risk of cancer. Machine learning classifiers constructed with clinical characteristics have favorable performance in predicting cancer in anti-TIF1γ+ myositis, which can help physicians in choosing appropriate cancer screening programs.

miR-504 mediated down-regulation of nuclear respiratory factor 1 leads to radio-resistance in nasopharyngeal carcinoma.

microRNAs (miRNAs) are involved in the various processes of DNA damage repair and play crucial roles in regulating response of tumors to radiation therapy. Here, we used nasopharyngeal carcinoma (NPC) radio-resistant cell lines as models and found that the expression of miR-504 was significantly up-regulated. In contrast, the expression of nuclear respiratory factor 1 (NRF1) and other mitochondrial metabolism factors, including mitochondrial transcription factor A (TFAM) and oxidative phosphorylation (OXPHOS) complex III were down-regulated in these cell lines. At the same time, the Seahorse cell mitochondrial stress test results indicated that the mitochondrial respiratory capacity was impaired in NPC radio-resistant cell lines and in a miR-504 over-expressing cell line. We also conducted dual luciferase reporter assays and verified that miR-504 could directly target NRF1. Additionally, miR-504 could down-regulate the expression of TFAM and OXPHOS complexes I, III, and IV and impaired the mitochondrial respiratory function of NPC cells. Furthermore, serum from NPC patients showed that miR-504 was up-regulated during different weeks of radiotherapy and correlated with tumor, lymph nodes and metastasis (TNM) stages and total tumor volume. The radio-therapeutic effect at three months after radiotherapy was evaluated. Results indicated that patients with high expression of miR-504 exhibited a relatively lower therapeutic effect ratio of complete response (CR), but a higher ratio of partial response (PR), compared to patients with low expression of miR-504. Taken together, these results demonstrated that miR-504 affected the radio-resistance of NPC by down-regulating the expression of NRF1 and disturbing mitochondrial respiratory function. Thus, miR-504 might become a promising biomarker of NPC radio-resistance and targeting miR-504 might improve tumor radiation response.

Knockdown of ANLN by lentivirus inhibits cell growth and migration in human breast cancer.

Anillin (ANLN), an actin-binding protein, is required for cytokinesis. Recently, ANLN has been identified as a biomarker in diverse human cancers; however, the precise role of ANLN in breast cancer remains unclear. In this study, we firstly detected the expression of ANLN in 71 patients with breast cancer by immunohistochemistry, and found ANLN was highly expressed in breast cancer tissues. To evaluate the function of ANLN in breast cancer cells, we employed lentivirus-mediated RNA interference to knock down ANLN expression in two human breast cancer cell lines, MDA-MB-231, and ZR-75-30. Knockdown of ANLN remarkably inhibited the proliferation rate and colony formation ability of both breast cancer cell lines. Moreover, flow cytometry analysis showed that depletion of ANLN in MDA-MB-231 cells blocked the cell cycle progression, with more cells delayed at G2/M phase, due to phosphorylation of Cdc2 and suppression of Cyclin D1. Furthermore, knockdown of ANLN strongly suppressed the migration of breast cancer cells, strengthening the evidence that ANLN could be involved in breast cancer progression. Our results may suggest ANLN as a potential target candidate in breast cancer.