Nonlinear relationship between viral load and TCT in single/multiple HPV52 infection.

PURPOSE: To determine the correlation between HPV (human papillomavirus) 52 viral load, multiple infections and ThinPrep cytology test (TCT), to inform clinical management of HPV52-positive women after cervical cancer screening. METHODS: A total of 1,882 female patients who had positive quantitative HPV tests at Yuebei People's Hospital from January 2020 to December 2022, of whom 533 tested positive for HPV52. We excluded patients who combined HPV16 and/or HPV 18 positivity and whom HPV52 viral load could not be calculated. The final enrollment was 488 patients, including 400 NILM, 48 ASC-US, 28 LSIL and 12 HSIL. The HPV test is a quantitative multiplexed fluorescent PCR assay that provides both HPV genotyping and viral load. RESULTS: In our study, there were differences in the median distribution of viral loads among various cytological class categories. The risk of TCT results (LSIL or worse) was increased with the increase of HPV52 viral load, for every LOG unit increase in HPV52 viral load, the risk increased by 26.6%. More importantly, we found a nonlinear relationship between HPV52 viral load and TCT results (LSIL or worse) in both single and multiple infections. When the viral load reaches a threshold, the risk of abnormal cytological results increases significantly. CONCLUSION: HPV52 viral load is an independent risk factor for TCT results (LSIL or worse). The relationship between HPV52 viral load and TCT results (LSIL or worse) is not linear. Viral load may be used as a triage indicator for HPV52-positive patients, thus improving the post-screening clinical management of HPV52-positive women.

The prevalence of human papillomavirus among women in northern Guangdong Province of China.

Globally, cervical cancer, whose etiologic factor is Human papillomavirus (HPV), is the third most common cancer among women. In cervical cancer screening, HPV testing is important. However, the prevalence of HPV in northern Guangdong Province has not been conclusively determined. A total of 100,994 women attending Yuebei People's Hospital Affiliated to Shantou University Medical College between 2012 and 2020 were recruited. HPV was tested by a polymerase chain reaction (PCR)-based hybridization gene chip assay. The prevalence of HPV among these women was established to be19.04%. Peak prevalence was observed in women aged 40-49 (7.29%). Besides, the prevalence of single-type HPV infection (14.46%) was significantly high, compared to multiple-type infection (4.58%) (p < 0.01), while the prevalence of high-risk HPV infection (19.97%) was significantly higher than that of low-risk genotypes (5.48%) (p < 0.01). The most prevalent high-risk genotypes were HPV52 (4.16%), HPV16 (2.98%), HPV58 (2.15%), HPV53 (1.58%) and HPV68 (1.34%). HPV co-infection with up to 10 genotypes was reported for the first time. Our findings suggested a high burden of HPV infections among women in northern Guangdong. Establishing the prevalence and genotype distribution characteristics of HPV infections in the region can contribute to cervical cancer prevention through HPV vaccination.

m6A RNA Methylation Regulators Elicit Malignant Progression and Predict Clinical Outcome in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and N6-methyladenosine (m6A) is a predominant internal modification of RNA in various cancers. We obtained the expression profiles of m6A-related genes for HCC patients from the International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Most of the m6A RNA methylation regulators were confirmed to be differentially expressed among groups stratified by clinical characteristics and tissues. The clinical factors (including stage, grade, and gender) were correlated with the two subgroups (cluster 1/2). We identified an m6A RNA methylation regulator-based signature (including METTL3, YTHDC2, and YTHDF2) that could effectively stratify a high-risk subset of these patients by univariate and LASSO Cox regression, and receiver operating characteristic (ROC) analysis indicated that the signature had a powerful predictive ability. Immune cell analysis revealed that the genes in the signature were correlated with B cell, CD4 T cell, CD8 T cell, dendritic cell, macrophage, and neutrophil. Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to HCC. Moreover, the nomogram was established based on the signature integrated with clinicopathological features. The calibration curve and the area under ROC also demonstrated the good performance of the nomogram in predicting 3- and 5-year OS in the ICGC and TCGA cohorts. In summary, we demonstrated the vital role of m6A RNA methylation regulators in the initial presentation and progression of HCC and constructed a nomogram which would predict the clinical outcome and provide a basis for individualized therapy.

Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance.

High-throughput sequencing is gaining popularity in clinical diagnoses, but more and more novel gene variants with unknown clinical significance are being found, giving difficulties to interpretations of people's genetic data, precise disease diagnoses, and the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret such variants. In this work, BRCA1 gene variants with unknown clinical significance were identified from clinical sequencing data, and then, we developed machine learning models so as to predict the pathogenicity for variants with unknown clinical significance. Through performance benchmarking, we found that the optimized random forest model scored 0.85 in area under receiver operating characteristic curve, which outperformed other models. Finally, we applied the best random forest model to predict the pathogenicity of 6321 BRCA1 variants from both sequencing data and ClinVar database. As a result, we obtained the predictive pathogenic risks of BRCA1 variants of unknown significance.

Therapeutic effects of puerarin on polycystic ovary syndrome: A randomized trial in Chinese women.

BACKGROUND: This study aims to assess the therapeutic effects of a well-known component (puerarin) obtained from a Chinese herb root in patients with polycystic ovary syndrome (PCOS). METHODS: Women with premature ovarian failure (POF) were assigned to the obese group (body mass index [BMI] ≥24 kg/m2 and waist hip ratio [WHR] >0.85) or non-obese group (group 3, n = 21). Obese patients were further randomly assigned to the obese treatment group (group 1, n = 15) and obese control group (group 1, n = 15). All patients received standard treatment (Diane-35, 1 tablet/d, orally, plus metformin, 1.5 g/d, orally). In addition to the standard modality, patients in group 1 and group 3 also orally received 150 mg/d of puerarin tablets for 3 months. Venous blood was drawn before and after treatment. Then, the metabolic and antioxidant biomarkers were measured. The normality of distribution of the data was tested using the Kolmogorov-Smirnov method. The baseline characteristics were analyzed using one-factor analysis of variance (ANOVA), and post-hoc was performed using the least significance difference (LSD)-t test. RESULTS: Significantly improved blood levels of sex hormone binding globulin (SHBG) and superoxide dismutase (SOD) were observed in patients who received the additional treatment of puerarin, regardless of their lean or obese status, while these were not observed in patients who did not receive puerarin. Furthermore, obese patients with PCOS had significantly lower systolic blood pressure, total cholesterol, and testosterone blood levels, when compared with before treatment. CONCLUSION: The addition of puerarin to the present treatment protocol can be considered for the management of metabolic disorders and hyperandrogenism in PCOS patients.

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. METHODS: We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. RESULTS: Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. CONCLUSION: This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Multi-omics Analysis of Microenvironment Characteristics and Immune Escape Mechanisms of Hepatocellular Carcinoma.

The immune environment in primary tumor has a profound impact on immunotherapy. However, the clinical relevance of immune environment in hepatocellular carcinoma (HCC) is largely unknown. Here, the immune profile and its clinical response in HCC were investigated. The gene expression profiles of 569 HCCs from three cohorts (The Cancer Genome Atlas, TCGA, n = 257; Gene Expression Omnibus, GEO, n = 170; International Cancer Genome Consortium, ICGC, n = 142) were used in the current study. Five gene expression subtypes (C1-C5) responsible for global immune genes were identified in HCCs at stage I/II. It was found that subtype C4 was associated with upregulation and subtype C5 was associated with downregulation of immune profiles in most metagenes. Immune-correlation analysis of the five subtypes demonstrated that C3 and C4 had higher immune score and better prognostic outcome, as compared with other subtypes. Moreover, the mutation frequencies of TP53, CTNNB1, and AXIN1 had significant difference in the five subgroups. Further, the expression of PDCD1, CD274, PDCD1LG2, CTLA4, CD86, and CD80 was higher in subtype C4 in comparison with the other subtypes. The WGCNA of immune-related genes in the five subtypes revealed that blue and turquoise modules were positively correlated with subtype C4 and were associated with 12 common pathways in the KEGG database. These results were validated in external cohorts from the NCI (National Cancer Institute) cohort (GSE14520) and the ICGC (International Cancer Genome Consortium) cohort. In summary, one immune-enhanced subtype and one immune-decreased subtype having different immune and clinical characteristics may provide guidance for developing novel treatment strategies for immune system malfunction-related cancer.

Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC. METHODS: A microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein-protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks. RESULTS: We found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA-miRNA-mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC. CONCLUSION: We found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis.

Evidence suggests that hepatitis C virus (HCV) infection is among the main causes of hepatocellular carcinoma (HCC). In addition, HCV-induced HCC (HCV-HCC) exhibits adverse clinical outcomes and limited therapeutic treatments are available for this condition. To investigate key biomarkers in the occurrence and development of HCV-HCC, microarray datasets GSE62232, GSE69715 and GSE107170 were downloaded from the Gene Expression Omnibus database for analysis. The differentially expressed genes between HCV-HCC and normal tissue were identified using the GEO2R online tool. The function enrichment analyses including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed using the Database for Annotation, Visualization and Integrated Discovery online tool. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. A total of 368 DEGs were identified, and the top 10 hub genes with a high degree of connectivity were selected for further analysis. Subsequently, overall survival and disease-free survival analysis revealed that there was a significant association between altered expression of HMMR, CCNB1 and KIF20A, and poor clinical outcome. In summary, these results indicate that HMMR, CCNB1 and KIF20A are potential targets for diagnosis and therapy of HCV-HCC.

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma.

Background: Nowadays, clinical treatment outcomes of patients with hepatocellular carcinoma (HCC) have been improved. However, due to the complexity of the molecular mechanisms, the recurrence rate and mortality in HCC inpatients are still at a high level. Therefore, there is an urgent need in screening biomarkers of HCC to show therapeutic effects and improve the prognosis. Methods: In this study, we aim to establish a gene signature that can predict the prognosis of HCC patients by downloading and analyzing RNA sequencing data and clinical information from three independent public databases. Firstly, we applied the limma R package to analyze biomarkers by the genetic data and clinical information downloaded from the Gene Expression Omnibus database (GEO), and then used the least absolute shrinkage and selection operator (LASSO) Cox regression and survival analysis to establish a gene signature and a prediction model by data from the Cancer Genome Atlas (TCGA). Besides, messenger RNA (mRNA) and protein expressions of the six-gene signature were explored using Oncomine, Human Protein Atlas (HPA) and the International Cancer Genome Consortium (ICGC). Results: A total of 8,306 differentially expressed genes (DEGs) were obtained between HCC (n = 115) and normal tissues (n = 52). Top 5,000 significant genes were selected and subjected to the weighted correlation network analysis (WGCNA), which constructed nine gene co-expression modules that assign these genes to different modules by cluster dendrogram trees. By analyzing the most significant module (red module), six genes (SQSTM1, AHSA1, VNN2, SMG5, SRXN1, and GLS) were screened by univariate, LASSO, and multivariate Cox regression analysis. By a survival analysis with the HCC data in TCGA, we established a nomogram based on the six-gene signature and multiple clinicopathological features. The six-gene signature was then validated as an independent prognostic factor in independent HCC cohort from ICGC. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the six-gene signature and nomogram. Besides, overexpression of the six genes at the mRNA and protein levels was validated using Oncomine and HPA, respectively. Conclusion: The predictive six-gene signature and nomograms established in this study can assist clinicians in selecting personalized treatment for patients with HCC.

Oxidized Low-density Lipoprotein (ox-LDL) Cholesterol Induces the Expression of miRNA-223 and L-type Calcium Channel Protein in Atrial Fibrillation.

Atrial fibrillation (AF) is the most common sustained arrhythmia causing high morbidity and mortality. While changing of the cellular calcium homeostasis plays a critical role in AF, the L-type calcium channel α1c protein has suggested as an important regulator of reentrant spiral dynamics and is a major component of AF-related electrical remodeling. Our computational modeling predicted that miRNA-223 may regulate the CACNA1C gene which encodes the cardiac L-type calcium channel α1c subunit. We found that oxidized low-density lipoprotein (ox-LDL) cholesterol significantly up-regulates both the expression of miRNA-223 and L-type calcium channel protein. In contrast, knockdown of miRNA-223 reduced L-type calcium channel protein expression, while genetic knockdown of endogenous miRNA-223 dampened AF vulnerability. Transfection of miRNA-223 by adenovirus-mediated expression enhanced L-type calcium currents and promoted AF in mice while co-injection of a CACNA1C-specific miR-mimic counteracted the effect. Taken together, ox-LDL, as a known factor in AF-associated remodeling, positively regulates miRNA-223 transcription and L-type calcium channel protein expression. Our results implicate a new molecular mechanism for AF in which miRNA-223 can be used as an biomarker of AF rheumatic heart disease.