Background Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis. Material/Methods Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues. Results In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in “cell division”, “chromosome, centromeric region” and “microtubule motor activity”. KEGG pathway analysis enriched mostly in “Cell cycle” and “Oocyte meiosis”. PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly. Conclusions The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC.

Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 cervical cancer samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and KEGG pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and HPA database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNMT1, CHAF1B, CHAF1A, MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA databases and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and KNTC1, as potential diagnostic and prognostic biomarkers of CC.

Background Cervical squamous cell carcinoma (CSCC), caused by the infection of high‐risk human papillomavirus, is one of the most common malignancies in women worldwide. Methods RNA expression data, including those from the Cancer Genome Atlas, Gene Expression Omnibus, and Genotype‐Tissue Expression databases, were used to identify the expression of RNAs in normal and tumor tissue. Correlation analysis was performed to identify the immune‐related long noncoding RNAs (IRLs) and hypoxia‐related genes (IRHs) that can influence the activity of the immune system. Prognosis models of immune‐related RNAs (IRRs) were used to construct a coexpression network of the immune system. We identified the role of IRRs in immunotherapy by correlation analysis with immune checkpoint genes (ICGs). We then validated the expression data by integrating two single‐cell sequencing data sets of CSCC to identify the key immune features. Results In total, six immune‐related gene (IRG), four IRL, and five IRH signatures that can significantly influence the characteristics of the tumor immune microenvironment (TIME) were selected using machine learning methods. The expression level of ICGs was significantly upregulated in GZMB + CD8 + T‐cells and tumor‐associated macrophages (TAMs) in tumor tissues. TGFBI + TAMs are a kind of blood‐derived monocyte‐derived M0‐like TAM linked to hypoxia and a poor prognosis. IFI30 + M1‐like TAMs participate in the process of immune‐regulation and showed a role in the promotion of CD8 + T‐cells and Type 1 T helper (Th1)/Th2 cells in the coexpression network, together with several IRLs, IRGs, and ICGs. Conclusions CD16 + monocyte‐derived IFI30 + TAMs participated in our coexpression network to regulate the TIME, showing the potential to be a novel immunotherapy target. The enrichment of M0‐like TAMs was associated with a worse prognosis in the high‐risk score group with IRH signatures. Remarkably, M0‐like TAMs in tumor tissues overexpressed TGFBI and were associated with several well‐known tumor‐proliferation pathways.

The global prevalence of overweight and obesity has increased markedly, and obesity has become a serious health concern (Afshin et al., 2017). Moreover, obesity is a risk factor for the development of many diseases, including type-2 diabetes, nonalcoholic fatty liver disease, cardiovascular disease, and several cancer types among others (Hruby & Hu, 2015;Ren et al., 2021). Emerging evidence suggests the occurrence of pathogenic events in the development and progression of obesity-associated comorbidities, including lowgrade systemic inflammation, oxidative stress, and insulin resistance (Hernandez-Bautista et al., 2019;Romeo et al., 2012). Therefore, it is critical to investigate the physiological mechanisms during the occurrence and development of obesity.In mammals, brown adipose tissue (BAT) is metabolically highly active and dissipates energy as heat through nonshivering thermogenesis (Cannon & Nedergaard, 2004). In addition, BAT is a multilocular organ that contains a relatively large number of mitochondria that produce heat via uncoupling of the respiratory chain (Poekes et al., 2015). BAT maintains temperature in newborn infants and small mammals (McMillan & White, 2015). Recent studies have demonstrated that adults also possess metabolically active brown fat or "beige adipocyte" (browning of white adipose

Background. Non-small cell lung cancer (NSCLC) is one of the cardinal trigger of cancer-related death worldwide. Most cases are diagnosed at an advanced stage using current tumor markers. Here, our study intended to identify the potential biomarkers to NSCLC diagnosis. Material/Methods. Four independent datasets (GSE18842, GSE19188, GSE30219 and GSE40791) were downloaded from the Gene Expression Omnibus. In order to explore the hub genes, various bioinformatics tools including the DAVID, STRING, GEPIA database, and R, Cytoscape software were used to analyze the dataset. Serum levels of RRM2 were verified by ELISA. The levels of CEA, CYFRA21-1, NSE and ProGRP were analyzed by electrochemiluminescence immunoassay. Results: A total of 258 genes were co-expression, of which 91 were up-regulated and 167 were down-regulated. A PPI network was constructed and 10 hub genes were screened, including RRM2, CDK1, UBE2C, MAD2L1, BUB1B, CCNA2, KIF20A, BUB1, KIF11, and CCNB2. The results of ELISA analysis showed that RRM2 is not only significantly up-regulated in NSCLC patient compared with the healthy control, but also the serum RRM2 level of NSCLC patient is related to distant metastasis, TNM stage and histological type (P < 0.05), but not to tumor size, lymph node metastasis (P > 0.05). ROC curve analysis showed that the combined detection of RRM2, CEA and NSE has the highest efficacy in diagnosing NSCLC. Conclusions. The comprehensive analysis showed that RRM2 could be used as a potential biomarker for diagnosis of NSCLC.