PurposeThe m5C RNA methylation regulators are closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the gene expression, clinicopathological characteristics, and prognostic value of m5C regulators in triple-negative breast cancer (TNBC) and their correlation with the tumor immune microenvironment (TIM).MethodsThe TNBC data, Luminal BC data and HER2 positive BC data set were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and 11 m5C RNA methylation regulators were analyzed. Univariate Cox regression and the least absolute shrinkage and selection operator regression models were used to develop a prognostic risk signature. The UALCAN and cBioportal databases were used to analyze the gene characteristics and gene alteration frequency of prognosis-related m5C RNA methylation regulators. Gene set enrichment analysis was used to analyze cellular pathways enriched by prognostic factors. The Tumor Immune Single Cell Hub (TISCH) and Timer online databases were used to explore the relationship between prognosis-related genes and the TIM.ResultsMost of the 11 m5C RNA methylation regulators were differentially expressed in TNBC and normal samples. The prognostic risk signature showed good reliability and an independent prognostic value. Prognosis-related gene mutations were mainly amplified. Concurrently, the NOP2/Sun domain family member 2 (NSUN2) upregulation was closely related to spliceosome, RNA degradation, cell cycle signaling pathways, and RNA polymerase. Meanwhile, NSUN6 downregulation was related to extracellular matrix receptor interaction, metabolism, and cell adhesion. Analysis of the TISCH and Timer databases showed that prognosis-related genes affected the TIM, and the subtypes of immune-infiltrating cells differed between NSUN2 and NSUN6.ConclusionRegulatory factors of m5C RNA methylation can predict the clinical prognostic risk of TNBC patients and affect tumor development and the TIM. Thus, they have the potential to be a novel prognostic marker of TNBC, providing clues for understanding the RNA epigenetic modification of TNBC.

The microbiome plays diverse roles in many diseases and can potentially contribute to cancer development. Breast cancer is the most commonly diagnosed cancer in women worldwide. Thus, we investigated whether the gut microbiota differs between patients with breast carcinoma and those with benign tumors. The DNA of the fecal microbiota community was detected by Illumina sequencing and the taxonomy of 16S rRNA genes. The α-diversity and β-diversity analyses were used to determine richness and evenness of the gut microbiota. Gene function prediction of the microbiota in patients with benign and malignant carcinoma was performed using PICRUSt. There was no significant difference in the α-diversity between patients with benign and malignant tumors ( P = 3.15e−1 for the Chao index and P = 3.1e−1 for the ACE index). The microbiota composition was different between the 2 groups, although no statistical difference was observed in β-diversity. Of the 31 different genera compared between the 2 groups, level of only Citrobacter was significantly higher in the malignant tumor group than that in benign tumor group. The metabolic pathways of the gut microbiome in the malignant tumor group were significantly different from those in benign tumor group. Furthermore, the study establishes the distinct richness of the gut microbiome in patients with breast cancer with different clinicopathological factors, including ER, PR, Ki-67 level, Her2 status, and tumor grade. These findings suggest that the gut microbiome may be useful for the diagnosis and treatment of malignant breast carcinoma.

Triple-negative breast cancer (TNBC) is characterized by fast progression with high potential for metastasis, and poor prognosis. The dysregulation of microRNAs (miRNAs) occurring in the initiation or progression of cancers often leads to aberrant gene expression. The aim of the present study was to explore the function of miR-126 in TNBC cells. Expression levels of miR-126-3p were determined by quantitative real-time PCR. Then, the effects of miR-126-3p on migration, proliferation, invasion, and angiogenesis were assessed through in vitro experiments including Cell Counting Kit-8, colony formation, Transwell invasion and vasculogenic mimicry formation assays. One of the target genes for miR-126-3p predicted by TargetScan was confirmed by luciferase activity assay. Results indicated that miR-126-3p expression was reduced in TNBC cell lines. Functional assays revealed that miR-126-3p overexpression inhibited cell proliferation, migration, invasion, colony formation capacity and vasculogenesis by 1.2-, 1.8-, 2.3-, 2.0-and 3.3-fold, respectively, compared to the miRNA-negative control group of MDA-MB-231 cells (P<0.001, respectively). In addition, the regulator of G-protein signaling 3 (RGS3) was hypothesized and validated as a direct target of miR-126-3p in TNBC. The proliferation, migration, invasion, colony formation capacity and vasculogenesis of MDA-MB-231 cells were significantly increased by 1.4-, 2.0-, 1.8-, 1.4-and 3.2-fold, respectively, in cells transfected with pcDNA3.0-RGS3 compared to pcDNA3.0-negative control groups (P<0.001, respectively). The influence of miR-126-3p expression was reversed by RGS3 restoration. Collectively, the present study revealed that miR-126-3p plays a role as a tumor suppressor in regulating TNBC cell activities by targeting RGS3, indicating that the miR-126-3p/RGS3 axis may be a potential treatment target.

PA28 γ was involved in the early events of CRC. It contributes to the carcinogenesis and progression of CRC and may be a biomarker for the early diagnosis of CRC.

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC.Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples.Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm.Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

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Background The oncogenic drivers of triple-negative breast cancer (TNBC), which is characterized by worst prognosis compared with other subtypes, are poorly understood. Although next-generation sequencing technology has facilitated identifying potential targets, few of the findings have been translated into daily clinical practice. The present study is aimed to explore ZNF703 (Zinc finger 703) function and its underlying mechanism in TNBC. Methods ZNF703 expressions in tissue microarray were retrospectively examined by immunohistochemistry. The cell proliferation by SRB assay and colony formation assay, as well as cell cycle distribution by flow cytometry were assessed. The protein levels associated with possible underlying molecular mechanisms were evaluated by western blotting. Kaplan-Meier analysis was used to plot survival analysis. Results Our data suggest that ZNF703 expressed in 34.2% of triple-negative human breast tumors by immunohistochemistry. In vitro, ZNF703 knockdown had potent inhibitory effects on TNBC cell proliferation and cell cycle, with cyclin D1, CDK4, CDK6, and E2F1 downregulated, while Rb1 upregulated. Moreover, Kaplan-Meier analysis showed that high mRNA expression of ZNF703 was correlated to worse overall survival (HR for high expression was 3.04; 95% CI, 1.22 to 7.57, P = 0.017). Conclusions Taken together, the results identified that targeting ZNF703 contributed to the anti-proliferative effects in TNBC cells, due to induced G1-phase arrest. This study is the first to identify ZNF703 as a potentially important protein that is involved in TNBC progression.

Visualization of gene expression at single RNA molecular level represents a great challenge to both imaging technologies and molecular engineering. Here we show a single molecule chromogenic in situ hybridization (smCISH) assay that enables counting and localizing individual RNA molecules in fixed cells and tissue under bright-field microscopy. Our method is based on in situ padlock probe assays directly using RNA as a ligation template and rolling circle amplification combined with enzyme catalyzed chromogenic reaction for amplification product visualization. We show potential applications of our method by detecting gene expression variations in single cells, subcellular localization information of expressed genes, and gene expression heterogeneity in formalin-fixed, paraffin-embedded tissue sections. This facile and straightforward method can in principle be applied to any type of RNA molecules in different samples.