Transcriptome analysis reveals the effect of oral contraceptive use on cervical cancer

CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data

With the improvement of -omics and next-generation sequencing (NGS) methodologies, along with the lowered cost of generating these types of data, the analysis of high-throughput biological data has become standard both for forming and testing biomedical hypotheses. Our knowledge of how to normalize datasets to remove latent undesirable variances has grown extensively, making for standardized data that are easily compared between studies. Here we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper (https://github.com/ELELAB/CAncer-bioMarker-Prediction-Pipeline -CAMPP) intended to aid bioinformatic software-users with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. To avoid issues relating to R-package updates, a renv .lock file is provided to ensure R-package stability. Data-management includes missing value imputation, data normalization, and distributional checks. CAMPP performs (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis, and (VI) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist in streamlining bioinformatic analysis of quantitative biological data, whilst ensuring an appropriate bio-statistical framework.

Distinct Molecular Mechanisms Analysis of Three Lung Cancer Subtypes Based on Gene Expression Profiles

The purpose was to explore distinct molecular mechanisms of three lung cancer subtypes. GSE6044 microarray data downloaded from Gene Expression Omnibus (GEO) database were applied for identifying the differentially expressed genes (DEGs). Genetic global network was constructed to analyze the network annotation. The DEGs in the genetic global network related to small-cell lung carcinoma (SCLC), lung squamous cell carcinoma (SCC), and lung adenocarcinoma (AC) were screened. Protein-protein international networks of DEGs were constructed. Pathway enrichment analyses of DEGs in three subtypes were performed, followed by construction of interactional network among pathways. There were more DEGs screened in SCLC than in AC and SCC. The genetic global network with 341 genes and 1569 interaction edges was constructed. After annotating these DEGs into a protein interactional network, a total of 695 protein interactions related to these 36 DEGs were obtained. HSP90AA1 was the hub node with the highest degree of 81 in the annotation network. DEGs in SCLC and SCC were mainly enriched in some pathways, including cell cycle, DNA replication, and histidine metabolism; whereas DEGs in AC were enriched in complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Pathway interactional network was constructed with the hub node of a neuroactive ligand receptor interaction. The identified DEGs such as retinoid X receptor alpha (RXRA), cyclin-dependent kinase 2 (CDK2), histone deacetylase 2 (HDAC2), and KIT might be the target genes of lung cancer by participating in different pathways such as ECM-receptor interaction. Complement and coagulation cascades, and ECM-receptor interaction might be the specific pathways for AC; smoking might have a closer relationship with SCC.

Prevalence and risk factors for cervical neoplasia: a cervical cancer screening program in Beijing

Background

Cervical cancer is the second most common cancer and cause of cancer-related death for women worldwide. The aims of this study were to investigate the prevalence of cervical neoplasia and examine factors associated with high-grade cervical squamous intraepithelial lesions (HSIL) among women taking part in a cervical cancer screening program in Beijing.

Methods

Women aged 25–65 years were screened using the ThinPrep cytologic test and gynecologic examination. Univariate and multivariate logistic regressions were conducted to investigate factors associated with HSIL.

Results

Among 728,704 women screened, the prevalence of cervical intraepithelial neoplasia (CIN) I, II, III was 50.2, 34.0, and 36.4 per 100,000, respectively. Prevalence of cervical cancer was 12.2 per 100,000. Risk factors for HSIL included being in age group of 46–55 years (adjusted odds ratio [aOR] = 1.15, 95% CI: 1.07–1.44, compared with the 25–35 age group), bleeding after intercourse (aOR = 2.08, 95% CI: 1.40–3.10), and presence of trichomonas vaginalis infection (aOR = 2.62, 95% CI: 1.35–5.07), cervical inflammation (aOR = 4.22, 95% CI: 3.39–5.26), and genital warts (aOR = 3.89, 95% CI: 2.54–7.70). High education level (college and above compared with junior middle school or lower) was found to be protective (aOR = 0.79, 95% CI: 0.37–0.90).

Conclusions

The prevalence of cervical neoplasia is relatively high in Beijing. Women aged 46–55 years, those with a lower education level, those reporting bleeding after intercourse, and those affected by Trichomonas vaginalis infection, cervical inflammation and genital warts are at higher risk for HSIL. Particular efforts should be made to ensure these women are included in cervical cancer screening programs.