Study on the methylation status of SPINT2 gene and its expression in cervical carcinoma

Genome-wide association studies in the Japanese population identified genetic loci and target gene associated with epidermal turnover

The epidermis is an essential organ for life by retaining water and as a protective barrier. The epidermis is maintained through metabolism, in which basal cells produced from epidermal stem cells differentiate into spinous cells, granular cells and corneocytes, and are finally shed from the epidermal surface. This is epidermal turnover, and with aging, there is a decline in epidermis function. Other factors that may affect epidermal turnover include ultraviolet damage and genetic factors. These genetic factors are of particular interest as little is known. Although recent skin-focused genome-wide association studies (GWAS) have been conducted, the genetic regions associated with epidermal turnover are almost uninvestigated. Therefore, we conducted a GWAS on epidermal turnover in the Japanese population, using the corneocyte area, which correlates to the rate of epidermal turnover, as an indicator. As a result, rs2278431 (p = 1.29 × 10-7 ) in 19q13.2 was associated with corneocyte size. Furthermore, eQTL analysis suggested that rs2278431 was related to the SPINT2 gene. In addition, SPINT2 knockdown studies using epidermal keratinocytes revealed that SPINT2 is involved in keratinocyte proliferation and in corneocyte size regulation in reconstructed epidermis. These results suggest that rs2278431 is involved in the expression of SPINT2 and affects epidermal turnover.

Discovering Breast Cancer Biomarkers Candidates through mRNA Expression Analysis Based on The Cancer Genome Atlas Database

Background: Research on the discovery of tumor biomarkers based on big data analysis is actively being conducted. This study aimed to secure foundational data for identifying new biomarkers of breast cancer via breast cancer datasets in The Cancer Genome Atlas (TCGA). Methods: The mRNA profiles of 526 breast cancer and 60 adjacent non-cancerous breast tissues collected from TCGA datasets were analyzed via MultiExperiment Viewer and GraphPad Prism. Diagnostic performance was analyzed by identifying the pathological grades of the selected differentially expressed (DE) mRNAs and the expression patterns of molecular subtypes. Results: Via DE mRNA profile analysis, we selected 14 mRNAs with downregulated expression (HADH, CPN2, ADAM33, TDRD10, SNF1LK2, HBA2, KCNIP2, EPB42, PYGM, CEP68, ING3, EMCN, SYF2, and DTWD1) and six mRNAs with upregulated expression (ZNF8, TOMM40, EVPL, EPN3, AP1M2, and SPINT2) in breast cancer tissues compared to that in non-cancerous tissues (p < 0.001). Conclusions: In total, 20 DE mRNAs had an area under cover of 0.9 or higher, demonstrating excellent diagnostic performance in breast cancer. Therefore, the results of this study will provide foundational data for planning preliminary studies to identify new tumor biomarkers.

Identification and validation of a seven-gene prognostic marker in colon cancer based on single-cell transcriptome analysis

Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single‐cell RNA sequencing (scRNA‐seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA‐seq was used to identify reliable prognostic biomarkers in CC. ScRNA‐seq data of CC before and after 5‐fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre‐processed, and dimensionality reduction was performed using principal component analysis and t‐distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic‐related genes, including CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven‐gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high‐score groups were enriched in ‘cytoplasmic DNA sensing’, ‘Extracellular matrix receptor interactions’, and ‘focal adhesion’, and low‐score groups were enriched in ‘natural killer cell‐mediated cytotoxicity’, and ‘T‐cell receptor signalling pathways’, among other pathways. A robust seven‐gene marker for CC was identified based on scRNA‐seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.

The distribution and pathogenic risk of non-9-valent vaccine covered HPV subtypes in cervical lesions

Human papillomavirus (HPV) infection is the main cause of female precancerous lesions and cervical cancer. The development and application of HPV prophylactic vaccines have been recognized as a major effective intervention for the control of cervical lesions. However, the infection rate and clinical characters of non-9-valent vaccine covered HPV subtypes are still worth studying. In this retrospective study, we included patients diagnosed and treated in the Department of Gynecology of Shanghai General Hospital between January 2017 and February 2021. The clinical features of non-9-valent vaccine covered HPV subtypes were explored in 2179 patients who have normal results, 338 patients with cervical intraepithelial neoplasia 1 (CIN1), and 153 patients with ≥CIN2. Univariate analysis showed that compared to the normal cervix group, age ≥50, pregnancy ≥5, delivery ≥3, menopause, no condom use, and cervical transformation zone type III were risk factors for CIN1 or ≥CIN2 (p < 0.05). Thirty-one percent of CIN1 and 26% of ≥CIN2 were attributed to HPV51, HPV53, HPV56, and HPV68. Multivariate analysis revealed that HPV53, HPV81, age, menopause, cervical transformation area and involved glands were independent risk factors for ≥CIN2 group compared to the CIN1 group (p < 0.05). Additionally, among the 14 non-9-valent vaccine covered HPV subtypes, the infection rates of HPV53, 56, 51, and 68 were higher in this study. In conclusion, our study demonstrated the distribution and pathogenic risk of non-9-valent vaccine covered HPV subtypes in cervical lesions. These findings might supply a foundation for optimizing cervical cancer prevention in the post-vaccine era.

Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma

Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B.