Ribonucleotide reductase regulatory subunit M2 (RRM2) as a potential sero-diagnostic biomarker in non-small cell lung cancer.

OBJECTIVES: Non-small cell lung cancer (NSCLC) is a major cause of cancer-related death worldwide. Most cases are diagnosed at an advanced stage using current tumor markers. Here, we aimed to identify potential novel potential biomarkers for NSCLC. MATERIAL/METHODS: Four independent datasets from the Gene Expression Omnibus database were analyzed. The relative expression of ribonucleotide reductase regulatory subunit M2 (RRM2) mRNA in 30 paired of NSCLC paired tissues was measured by reverse transcription quantitative PCR. Serum levels of cytokeratin fragment 21-1 (CYFRA21-1), pro-gastrin-releasing peptide (ProGRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) were measured using electrochemiluminescence immunoassays, and serum RRM2 levels were evaluated by an enzyme-linked immunosorbent assay. RESULTS: The mRNA expression level of RRM2 was significantly increased in most NSCLC lesions compared to para-adjacent tissues. Serum RRM2 levels in NSCLC patients were significantly elevated compared to healthy controls and were also associated with distant metastasis and histological type, but not with tumor size or lymph node metastasis. Receiver operating characteristic curve analysis showed a higher diagnostic ratio for NSCLC using RRM2 alone compared to other traditional tumor markers. CONCLUSIONS: RRM2 is a potential sero-diagnostic biomarker for NSCLC.

Characterizing the landscape of cervical squamous cell carcinoma immune microenvironment by integrating the single-cell transcriptomics and RNA-Seq.

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.

Green tea aqueous extract (GTAE) prevents high-fat diet-induced obesity by activating fat browning.

Adipose browning leads to increased energy expenditure and reduced adiposity and has, therefore, become an attractive therapeutic strategy for obesity. In this study, we elucidated the effect of green tea aqueous extract (GTAE) on the browning of inguinal white adipose tissue (Ing-WAT) and brown adipose tissue (BAT) in high-fat diet (HFD)-fed mice. The main phytochemical components identified in GTAE through high-performance liquid chromatography (HPLC) included (-)-gallocatechin, (-)-epigallocatechin, (-)-catechin, (-)-epigallocatechin-3-gallate, caffeine, (-)-epicatechin, (-)-gallocatechin gallate, and (-)-epicatechin-3-gallate. Daily supplementation with 1% GTAE for 12 weeks markedly reduced bodyweight gain, systemic inflammation, oxidative stress, and improved insulin resistance. Additionally, histological analysis revealed that dietary supplementation with 1% GTAE reversed HFD-induced adipocyte size and hepatic steatosis. These effects were associated with activation of browning in the Ing-WAT and BAT, which mediate systemic metabolic dysfunction in HFD-fed mice. Taken together, our data support the use of GTAE, a natural product, for the attenuation of obesity through the activation of fat browning.

Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer.

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 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (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 Human Protein Atlas (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. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (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 database 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 Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer.

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.

Binuclear cyclopentadienylmetal cyclooctatetraene derivatives of the first row transition metals: effects of ring conformation on the bonding of an eight-membered carbocyclic ring to a pair of metal atoms.

Binuclear Cp(2)M(2)(µ-C(8)H(8)) derivatives have been synthesized for M = V, Cr, Co, and Ni and have now been studied theoretically for the entire first row of transition metals from Ti to Ni. The early transition metal derivatives Cp(2)M(2)(µ-C(8)H(8)) (M = Ti, V, Cr. Mn) are predicted to form low-energy cis-Cp(2)M(2)(µ-C(8)H(8)) structures with a folded C(8)H(8) ring (dihedral angle ∼130°) and short metal-metal distances suggesting multiple bonding. These predicted structures are close to the experimental structures for M = V, Cr with V≡V and Cr≡Cr bond lengths of ∼2.48 and ∼2.36 Å, respectively. The middle to late transition metals form trans-Cp(2)M(2)(µ-C(8)H(8)) structures (M = Mn, Fe, Co, Ni) with a twisted µ-C(8)H(8) ring and no metal-metal bonding. The hapticity of the central µ-C(8)H(8) ring in such structures ranges from five for Mn and Fe to four for Co and three for Ni and thus depend on the electronic requirements of the central metal atom. This leads to the favored 18-electron configuration for both metal atoms in the singlet Fe, Co, and Ni structures but only 17-electron metal configurations in the triplet Mn structure. In addition, the late transition metals form trans-Cp(2)M(2)(µ-C(8)H(8)) structures (M = Fe, Co, Ni), with the tub conformation of the µ-C(8)H(8) ring functioning as a tetrahapto (M = Fe, Co) or trihapto (M = Ni) ligand to each CpM group. A µ-C(8)H(8) ring in the tub conformation also bonds to two CpFe units as a bis(tetrahapto) ligand in both singlet and triplet cis-Cp(2)Fe(2)(µ-C(8)H(8)) structures.