Long non­coding RNAs in gallbladder cancer: From mechanisms to therapeutic opportunities (Review).

Due to the lack of specific symptoms, characteristic diagnostic markers and effective comprehensive treatment, gallbladder cancer (GBC) is currently considered one of the most malignant abdominal tumors. With the rapid development of biological technologies, long non­coding RNAs (lncRNAs), once regarded as transcriptional junk, have been demonstrated to participate in almost the whole process of the central dogma of molecular biology. The central dogma deals with the transfer of sequential information at the level of individual residues. LncRNAs have an effect on multiple cancer types. However, evidence of dysregulated lncRNA functions in GBC is limited. In the present review, the regulatory mechanisms of lncRNA function on gene expression were examined, including epigenetic modification, transcriptional regulation and post­translational modulation. These mechanisms are strongly associated with tumor development and metastasis. Next, it was summarized how lncRNAs affect GBC diverse malignant phenotypes through various mechanisms. Moreover, predictions of lncRNA interactions with other functional molecules in malignancies were made using several valuable databases, including crosstalk between lncRNA and DNA, mRNA, microRNA, and protein. Additionally, several potential therapeutic methods targeting pathological lncRNAs in tumors were identified. Finally, perspectives about lncRNA research and applications in GBC were presented in the current study, including viewpoints of coding potential of lncRNAs and feasible usage of micropeptides encoded by lncRNAs; roles of lncRNAs in tumor cell metabolic reprogramming and tumor microenvironment; and function of lncRNAs as possible biomarkers and therapeutic targets for improving GBC diagnosis, treatment and prognosis.

Single-Cell RNA Sequencing Revealed a 3-Gene Panel Predicted the Diagnosis and Prognosis of Thyroid Papillary Carcinoma and Associated With Tumor Immune Microenvironment.

Objective: The objective of this research was to screen prognostic related genes of thyroid papillary carcinoma (PTC) by single-cell RNA sequencing (scRNA-seq), to construct the diagnostic and prognostic models based on The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) data, and to evaluate the association between tumor immune microenvironment and the prognostic model. Method: The differentially expressed genes (DEGs) and tumor evolution were analyzed by scRNA-seq based on public databases. The potential regulatory networks of DEGs related to prognosis were analyzed by multi-omics data in the THCA. Logistic regression and Cox proportional hazards regression were utilized to construct the diagnosis and prognostic model of PTC. The performance of the diagnostic model was verified by bulk RNA sequencing (RNA-seq) of our cohort. The tumor immune microenvironment associated with the prognostic model was evaluated using multi-omics data. In addition, qRT-PCR was performed on tumor tissues and adjacent normal tissues of 20 patients to verify the expression levels of DEGs. Results: The DEGs screened by scRNA-seq can distinguish between tumor and healthy samples. DEGs play different roles in the evolution from normal epithelial cells to malignant cells. Three DEGs ((FN1, CLU, and ANXA1)) related to prognosis were filtered, which may be regulated by DNA methylation, RNA methylation (m6A) and upstream transcription factors. The area under curve (AUC) of the diagnostic model based on 3-gene in the validation of our RNA-seq was 1. In the prognostic model based on 3-gene, the overall survival (OS) of high-risk patients was shorter. Combined with the clinical information of patients, a nomogram was constructed by using tumor size (pT) and risk score to quantify the prognostic risk. The age and tumor size of high-risk patients in the prognostic model were greater. In addition, the increase of tumor mutation burden (TMB) and diversity of T cell receptor (TCR), and the decrease of CD8+ T cells in high-risk group suggest the existence of immunosuppressive microenvironment. Conclusion: We applied the scRNA-seq pipeline to focus on epithelial cells in PTC, simulated the process of tumor evolution, and revealed a prognostic prediction model based on 3 genes, which is related to tumor immune microenvironment.

Central Compartment Lymph Nodes Have Distinct Metastatic Patterns in Different Age Groups.

BACKGROUND AND PURPOSE: Central compartment lymph node metastasis (CLNM) is a manifestation of tumor aggressiveness and an indicator of tumor prognosis. The purpose of this study was to construct a nomogram for evaluating CLNM patterns in papillary thyroid carcinoma (PTC) in different age groups. METHOD: A total of 907 patients diagnosed with PTC from August 2014 to December 2018 were enrolled. A nomogram illustrating CLNM was generated using the results of multivariate logistic regression analysis. RESULTS: According to the best Youden index, we set the cut-off age at 45 years. Multivariate logistic regression analysis showed that in patients aged <45 years, large tumor size (P<0.05), extra-thyroid extension (P<0.05) and thyroglobulin level >40 ng/ml (OR=2.985, 95% CI 1.379-6.462; P<0.05) were independent risk factors; meanwhile, Hashimoto's thyroiditis (OR=0.532, 95% CI 0.324-0.874; P<0.05) was a protective factor of CLNM. In the subgroup with age ≥45 years, large tumor size (P<0.05), extra-thyroid extension (P<0.05), unclear margin (OR=1.604, 95% CI 1.065-2.416; P<0.05), male gender (OR=2.009, 95% CI 1.257-3.212; P<0.05) were independent risk factors for CLNM. In the subgroup with age <45 years, an area under the curve (AUC) of 0.729 (95% CI 0.680-0.777); P<0.05) was obtained. In the ≥45 years subgroup, the AUC was 0.668 (95% CI 0.619-0.716; P<0.05). CONCLUSION: CLNM of PTC in different age groups may have distinct patterns. Based on the potential risk factors for CLNM in patients with different age stratification, a user-friendly predictive model was established.