IL-6, a pleiotropic cytokine, has been investigated for its role in regulating autophagy. Yet, its mechanism of action remains unclear. Here, we show that IL-6 exerted anti-autophagic effects on U937 cells through the STAT3 signaling pathway in vitro. The addition of IL-6 to starved U937 cells significantly activated the phosphorylation level of STAT3 (p-STAT3) at Tyr705 and reduced the protein levels of microtubule-associated protein 1 light chain 3 of type II (LC3-II) and Beclin 1. By immunoblotting, we also observed a positive correlation between the p-STAT3 level and Bcl-2 level. Furthermore, treatment with a STAT3 inhibitor, LLL12, or overexpression of a mutant form, STAT3Y705F, reversed the inhibitory effect of IL-6 on autophagy. Knockdown of Beclin 1 or Atg14 by siRNA and over-expression of Beclin 1 indicated the involvement of class III PI3K complex in IL-6-mediated inhibition of autophagy. Taken together, these data indicate that IL-6 inhibits starvation-induced autophagy and that p-STAT3 mediates the signal transduction from IL-6 to downstream proteins including Bcl-2 and Beclin1.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis. Cancer-associated fibroblasts (CAFs) play a critical role in regulating TNBC tumor development. This study aimed to identify and characterize a specific subtype of CAFs associated with TNBC. Initially, using high-throughput bulk transcriptomic data in two cohorts, we identified three CAF-related subtypes (CS1, CS2, CS3) in TNBC samples. These three CAFs subtypes were closely linked to the tumor microenvironment. The CS1 subtype exhibited a relatively immune-rich microenvironment and a favourable prognosis, whereas the CS3 subtype displayed an immune-deprived tumor microenvironment and an unfavourable prognosis. Through WGCNA analysis, POSTN was identified as a key biomarker for CAFs associated with TNBC. Then, POSTN⁺CAFs was identified and characterized. Both POSTN and POSTN⁺CAFs showed significant positive correlations with stromal molecules HGF and MET at both the transcriptional and protein levels. Specifically co-localized with CAFs in the tumor stromal area, POSTN, produced by POSTN⁺CAFs, could modulate the HGF-MET axis, serving as a bypass activation pathway to regulate tumor cell proliferation in response to EGFR inhibitor and MET inhibitor. This study underscores the significance of POSTN and POSTN⁺CAFs as crucial targets for the diagnosis and treatment of TNBC.

The function of centromere protein U (CENPU) gene in breast cancer has not been well understood. Therefore, we explored the expression profiles of CENPU gene in breast carcinoma to better understand the functions of this gene, as well as the relationship between CENPU expression and the prognosis of breast carcinoma patients. Our results indicate that CENPU was expressed at significantly higher levels in cancerous tissues than in normal tissues. Furthermore, CENPU expression correlated significantly with many clinicopathological characteristics of breast cancer. In addition, we discovered that high levels of CENPU expression predicted poor prognosis in patients with breast cancer. Functional investigation revealed that 180 genes exhibited co-expression with CENPU. Functional annotation indicated that 17 of these genes were involved in the PLK1 signaling pathway, with most of them (16/17) being expressed at significantly higher levels in malignant tissues compared with normal controls and correlating with a poor prognosis. Subsequently, we found that four miRNAs, namely hsa-miR-543, hsa-miR-495-3p, hsa-miR-485-3p, and hsa-miR-337-3p, could be regarded as potential CENPU expression regulators. Then, five lncRNAs were predicted to potentially bind to the four miRNAs. Combination of the results from expression, survival, correlation analysis and functional experiments analysis demonstrated the link between lncRNA GATA3-AS1/miR-495-3p/CENPU axis and prognosis of breast cancer. In conclusion, CENPU could be involved in cell cycle progression through PLK1 signaling pathway.

Background: tRNA-derived RNA fragments (tRFs) are a novel class of small ncRNA that are derived from precursor or mature tRNAs. Recently, the general relevance of their roles and clinical values in tumorigenesis, metastasis, and recurrence have been increasingly highlighted. However, there has been no specific systematic study to elucidate any potential clinical significance for these tRFs in prostate adenocarcinoma (PRAD), one of the most common and malignant cancers that threatens male health worldwide. Here, we investigate the clinical value of 5′-tRFs in PRAD. Methods: Small RNA sequencing data were analyzed to discover new 5′-tRFs biomarkers for PRAD. Machine learning algorithms were used to identify 5′-tRF classifiers to distinguish PRAD tumors from normal tissues. LASSO and Cox regression analyses were used to construct 5′-tRF prognostic predictive models. NMF and consensus clustering analyses were performed on 5′-tRF profiles to identify molecular subtypes of PRAD. Results: The overall levels of 5′-tRFs were significantly upregulated in the PRAD tumor samples compared to their adjacent normal samples. tRF classifiers composed of 13 5′-tRFs achieved AUC values as high as 0.963, showing high sensitivity and specificity in distinguishing PRAD tumors from normal samples. Multiple 5′-tRFs were identified as being associated with the PRAD prognosis. The tRF score, defined by a set of eight 5′-tRFs, was highly predictive of survival in PRAD patients. The combination of tRF and Gleason scores showed a significantly better performance than the Gleason score alone, suggesting that 5′-tRFs can offer PRAD patients additional and improved prognostic information. Four molecular subtypes of the PRAD tumor were identified based on their 5′-tRF expression profiles. Genetically, these 5′-tRFs PRAD tumor subtypes exhibited distinct genomic landscapes in tumor cells. Clinically, they showed marked differences in survival and clinicopathological features. Conclusions: 5′-tRFs are potential clinical biomarkers for the diagnosis, prognosis, and classification of tumor subtypes on a molecular level. These can help clinicians formulate personalized treatment plans for PRAD patients and may have similar potential applications for other disease types.