Association of endometriosis with Sjögren's syndrome: Genetic insights (Review)

Patients with a history of endometriosis have an increased risk of developing various autoimmune diseases such as rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, multiple sclerosis and celiac disease. There is a potential association between endometriosis and an increased susceptibility for Sjögren's syndrome (SS). SS is a common chronic, inflammatory, systemic, autoimmune, multifactorial disease of complex pathology, with genetic, epigenetic and environmental factors contributing to the development of this condition. It occurs in 0.5‑1% of the population, is characterized by the presence of ocular dryness, lymphocytic infiltrations and contributes to neurological, gastrointestinal, vascular and dermatological manifestations. Endometriosis is an inflammatory, estrogen‑dependent, multifactorial, heterogeneous gynecological disease, affecting ≤10% of reproductive‑age women. It is characterized by the occurrence of endometrial tissue outside the uterine cavity, mainly in the pelvic cavity, and is associated with pelvic pain, dysmenorrhea, deep dyspareunia and either subfertility or infertility. It is still unclear whether SS appears as a secondary response to endometriosis, or it is developed due to any potential shared mechanisms of these conditions. The aim of the present review was to explore further the biological basis only of the co‑occurrence of these disorders but not their association at clinical basis, focusing on the analysis of the partially shared genetic background between endometriosis and SS, and the clarification of the possible similarities in the underlying pathogenetic mechanisms and the relevant molecular pathways.

A transcription factor signature predicts the survival of patients with adrenocortical carcinoma

Background

Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels and is associated with poor clinical outcomes. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for the prediction of survival of ACC patients.

Methods

The gene expression profile and clinical information for ACC patients were downloaded from The Cancer Genome Atlas (TCGA, training set) and Gene Expression Omnibus (GEO, validation set) datasets after obtained 1,639 human TFs from a previously published study. The univariate Cox regression analysis was applied to identify the survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature based on these survival-associated TFs candidates. Then, multivariate analysis was used to reveal the independent prognostic factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was performed to analyze the significance of the TFs constituting the prognostic signature.

Results

LASSO Cox regression and multivariate Cox regression identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6. The risk score based on the TF signature could classify patients into low- and high-risk groups. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival (OS) compared to the low-risk patients. Receiver operating characteristic (ROC) curves showed that the prognostic signature predicted the OS of ACC patients with good sensitivity and specificity both in the training set (AUC > 0.9) and the validation set (AUC > 0.7). Furthermore, the TF-risk score was an independent prognostic factor.

Conclusions

Taken together, we identified a 13-TF prognostic marker to predict OS in ACC patients.

Regulatory Network of MicroRNAs, Host Genes, Target Genes and Transcription Factors in Human Esophageal Squamous Cell Carcinoma

Abnormally expressed microRNAs (miRNAs) and genes have been found to play key roles in esophageal squamous cell carcinoma (ESCC), but little is known about the underlying mechanisms. The aim of this paper was to assess inter-relationships and the regulatory mechanisms of ESCC through a network-based approach. We built three regulatory networks: an abnormally expressed network, a related network and a global network. Unlike previous examples, containing information only on genes or miRNAs, the prime focus was on relationships. It is worth noting that abnormally expressed network emerged as a fault map of ESCC. Theoretically, ESCC might be treated and prevented by correcting the included errors. In addition, the predicted transcription factors (TFs) obtained by the P-match method also warrant further study. Our results may further guide gene therapy researchers in the study of ESCC.

Regulatory network analysis of microRNAs and genes in neuroblastoma

Neuroblastoma (NB), the most common extracranial solid tumor, accounts for 10% of childhood cancer. To date, scientists have gained quite a lot of knowledge about microRNAs (miRNAs) and their genes in NB. Discovering inner regulation networks, however, still presents problems. Our study was focused on determining differentially-expressed miRNAs, their target genes and transcription factors (TFs) which exert profound influence on the pathogenesis of NB. Here we constructed three regulatory networks: differentially-expressed, related and global. We compared and analyzed the differences between the three networks to distinguish key pathways and significant nodes. Certain pathways demonstrated specific features. The differentially-expressed network consists of already identified differentially-expressed genes, miRNAs and their host genes. With this network, we can clearly see how pathways of differentially expressed genes, differentially expressed miRNAs and TFs affect on the progression of NB. MYCN, for example, which is a mutated gene of NB, is targeted by hsa-miR-29a and hsa-miR-34a, and regulates another eight differentially-expressed miRNAs that target genes VEGFA, BCL2, REL2 and so on. Further related genes and miRNAs were obtained to construct the related network and it was observed that a miRNA and its target gene exhibit special features. Hsa-miR-34a, for example, targets gene MYC, which regulates hsa-miR-34a in turn. This forms a self-adaption association. TFs like MYC and PTEN having six types of adjacent nodes and other classes of TFs investigated really can help to demonstrate that TFs affect pathways through expressions of significant miRNAs involved in the pathogenesis of NB. The present study providing comprehensive data partially reveals the mechanism of NB and should facilitate future studies to gain more significant and related data results for NB.