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Byline: Jian-Wu. Ni, Cai-Xia. Li, Xiong-Wei. Chen, Wen-Pin. Cai Microglia play a crucial role in the activation of immune defense mechanism as the resident macrophages in the central nervous system [...]

Insulin receptor substrate 1 and 2 (IRS1/2) have been found involved in many cancers development and their inhibitors exert significant tumor‐suppressive effects. Here, we tried to explore the function of NT157, an IGF1R‐IRS1/2 inhibitor, in ovarian cancer. We treated ovarian cancer cells with varying doses of NT157. The MTT assay was employed to evaluate cell proliferation and colony formation assay was used for detecting colony‐forming ability. TUNEL assay was adopted to test cell apoptosis. Cell invasion was checked by the Transwell assay. The expression of apoptosis‐related proteins, autophagy markers, IRS1/2, and PI3K/AKT/mTOR pathway was compared by Western blot, immunofluorescence, or qRT‐PCR. As indicated by the data, NT157 abated the viability, proliferation, and induced autophagy of ovarian cancer cells. Overexpressing IRS1/2 attenuated the tumor‐suppressive effect of NT157 and heightened the PI3K/AKT/mTOR pathway activation. Inhibition of the PI3K/AKT/mTOR pathway enhanced the tumor‐suppressive effect of NT157 and facilitated NT157‐mediated autophagy. However, the autophagy inhibitor 3‐MA partly reversed NT‐157‐mediated antitumor effects. In conclusion, this study disclosed that NT157 suppressed the malignant phenotypes of ovarian cancer cells by inducing autophagy and hampering the expression of IRS1/2 and PI3K/AKT/mTOR pathway. [ABSTRACT FROM AUTHOR]

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy., (© 2024. The Author(s).)

Objectives: To provide a guideline for genealogy inference and family lineage investigation through a study of the mismatch tolerance distribution of Y-STR loci in Chinese Han male lineage., Methods: Three Han lineages with clear genetic relationships were selected. YFiler Platinum PCR amplification Kit was used to obtain the typing data of 35 Y-STR loci in male samples. The variation of Y-STR haplotypes in generation inheritance and the mismatch tolerance at 1-7 kinship levels were statistically analyzed., Results: Mutations in Y-STR were family-specific with different mutation loci and numbers of mutation in different lineages. Among all the mutations, 66.03% were observed on rapidly and fast mutating loci. At 1-7 kinship levels, the number of mismatch tolerance ranged from 0 to 5 on all 35 Y-STR loci, with a maximum step size of 6. On medium and slow mutant loci, the number of mismatch tolerance ranged from 0 to 2, with a maximum step size of 3; on rapidly and fast mutant loci, the number of mismatch tolerance ranged from 0 to 3, with a maximum step size of 6., Conclusions: Combined use of SNP genealogy inference and Y-STR lineage investigation, both 0 and multiple mismatch tolerance need to be considered. Family lineage with 0-3 mismatch tolerance on all 35 Y-STR loci and 0-1 mismatch tolerance on medium and slow loci can be prioritized for screening. When the number of mismatch tolerance is eligible, family lineages with long steps should be carefully excluded. Meanwhile, adding fast mutant loci should also be handled with caution.