A Panel of Five-lncRNA Signature as a Potential Biomarker for Predicting Survival in Gastric and Thoracic Cancers

Identification of Novel m6A-Related Long Non-Coding RNA Signatures for Cholangiocarcinoma Using Integrated Bioinformatics Analyses

Traditional methods used to treat cholangiocarcinoma are less effective, and the identification of new CHOL signature genes can help in the early clinical diagnosis and intervention of cholangiocarcinoma. In this work, we used integrated bioinformatics analysis to find new m6a-associated lncRNA signatures in cholangiocarcinoma. Pearson correlation test was used to identify m6A-lncRNAs by co-expression analysis of m6A-mrna and lncRNAs. we then selected m6A-lncRNAs co-expressed with METTL3 and METTL14 genes and screened for DEm6A-lncRNAs by comparing expression differences. we then used R package of Spearman coefficient correlation analysis to investigate the relevance of m6A-lncrna expression in CHOL. To determine the relative levels of immune cell infiltration, we performed ssGSEA analysis on all samples using the R package, and then we used graphs to illustrate the differences in immune cell infiltration between the CHOL and NC groups. The results of this study will help to identify new CHOL-causing biosignatures, which are important for the early clinical detection and management of CHOL.

Thymic epithelial tumors: examining the GTF2I mutation and developing a novel prognostic signature with LncRNA pairs to predict tumor recurrence

Background

General transcription factor IIi (GTF2I) mutations are very common in thymic epithelial tumors (TETs) and are related to a more favorable prognosis in TET patients. However, limited research has been conducted on the role of GTF2I in the tumor immune microenvironment (TIME). Further, long non-coding RNAs (lncRNAs) have been associated with the survival of patients with TETs. Therefore, this study aimed to explore the relationship between GTF2I mutations and TIME and build a new potential signature for predicting tumor recurrence in the TETs. Research data was downloaded from The Cancer Genome Atlas database and the CIBERSORT algorithm was used to evaluate TIME differences between GTF2I mutant and wild-type TETs. Relevant differentially expressed lncRNAs based on differentially expressed immune-related genes were identified to establish lncRNA pairs. We constructed a signature using univariate and multivariate Cox regression analyses.

Results

GTF2I is the most commonly mutated gene in TETs, and is associated with an increased number of early-stage pathological types, as well as no history of myasthenia gravis or radiotherapy treatment. In the GTF2I wild-type group, immune score and immune cell infiltrations with M2 macrophages, activated mast cells, neutrophils, plasma, T helper follicular cells, and activated memory CD4 T cells were higher than the GTF2I mutant group. A risk model was built using five lncRNA pairs, and the 1-, 3-, and 5-year area under the curves were 0.782, 0.873, and 0.895, respectively. A higher risk score was related to more advanced histologic type.

Conclusion

We can define the GTF2I mutant-type TET as an immune stable type and the GTF2I wild-type as an immune stressed type. A signature based on lncRNA pairs was also constructed to effectively predict tumor recurrence.

Phosphoserine phosphatase as a prognostic biomarker in patients with gastric cancer and its potential association with immune cells

Background

Because of dismal prognosis in gastric cancer, identifying relevant prognostic factors is necessary. Phosphoserine phosphatase (PSPH) exhibits different expression patterns in many cancers and has been reported to affect the prognosis of patients with cancer. In this study, we examined the prognostic role of metabolic gene PSPH in gastric cancer based on the TCGA dataset and our hospital–based cohort cases.

Methods

We collected and analysed RNA-seq data of Pan-cancer and gastric cancer in the TCGA dataset and PSPH expression data obtained from immunohistochemical analysis of 243 patients with gastric cancer from Sun Yat-sen University cancer center. Further, Kaplan–Meier survival analysis and Cox analysis were used to assess the effect of PSPH on prognosis. The ESTIMATE and Cibersort algorithms were used to elucidate the relationship between PSPH and the abundance of immune cells using the TCGA dataset.

Results

We observed that PSPH expression displayed considerably high in gastric cancer and it was significantly associated with inferior prognosis (P = 0.043). Surprisingly, there was a significant relationship between lower immune scores and high expression of PSPH (P < 0.05). Furthermore, patients with a low amount of immune cells exhibited poor prognosis (P = 0.046). The expression of PSPH significantly increased in activated memory CD4 T cells, resting NK cells and M0 macrophages (P = 0.037, < 0.001, and 0.005, respectively).

Conclusions

This study highlighted that PSPH influences the prognosis of patients with gastric cancer, and this is associated with the infiltration of tumour immune cells, indicating that PSPH may be a new immune-related target for treating gastric cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-02073-0.

High diagnostic value of miRNAs for NSCLC: quantitative analysis for both single and combined miRNAs in lung cancer

BACKGROUND

MicroRNAs (miRNAs) are good candidates as biomarkers for Lung cancer (LC). The aim of this article is to figure out the diagnostic value of both single and combined miRNAs in LC.

METHODS

Normative meta-analysis was conducted based on PRISMA. We assessed the diagnostic value by calculating the combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) and the area under the curve (AUC) of single and combined miRNAs for LC and specific subgroups.

RESULTS

A total of 80 qualified studies with a total of 8971 patients and 10758 controls were included. In non-small cell lung carcinoma (NSCLC), we involved 20 single-miRNAs and found their Sen, Spe and AUC ranged from 0.52-0.81, 0.66-0.88, and 0.68-0.90, respectively, specially, miR-19 with the maximum Sen, miR-20 and miR-10 with the highest Spe as well as miR-17 with the maximum AUC. Additionally, we detected miR-21 with the maximum Sen of 0.74 [95%CI: 0.62-0.83], miR-146 with the maximum Spe and AUC of 0.93 [95%CI: 0.79-0.98] and 0.89 [95%CI: 0.86-0.92] for early-stage NSCLC. We also identified the diagnostic power of available panel (miR-210, miR-31 and miR-21) for NSCLC with satisfying Sen, Spe and AUC of 0.82 [95%CI: 0.78-0.84], 0.87 [95%CI: 0.84-0.89] and 0.91 [95%CI: 0.88-0.93], and furtherly constructed 2 models for better diagnosis.

CONCLUSIONS

We identified several single miRNAs and combined groups with high diagnostic power for NSCLC through pooled quantitative analysis, which shows that specific miRNAs are good biomarker candidates for NSCLC and further researches needed.