RTP4 is a novel prognosis-related hub gene in cutaneous melanoma

Identification of RTP4 facilitating ovarian cancer by bioinformatics analysis and experimental validation

Ovarian cancer (OV) is the most malignant gynecological tumor in women, with poor prognosis and high mortality rate. This study aims to identify hub genes in OV and explore the role of Receptor transporter 4 (RTP4) in OV progression. Common differentially expressed genes (DEGs) were screened from two microarray datasets. GO and KEGG enrichment analysis were performed. Protein-protein interaction (PPI) network was constructed by STING. Kaplan-Meier plotter was used to analyze prognosis. The effect of target gene on immune infiltration was analyzed by TIMER. The proliferation, migration, and invasion of OV cells were measured by CCK-8, wound healing assay, and trans-well assay, respectively. A total of 293 common DEGs were selected from GSE12470 and GSE16709 datasets. Hub genes, EPCAM, KIFC1, RTP4, TAGLN, and ZFP36 were selected by PPI network. Kaplan-Meier plotter demonstrated that high expression of RTP4 was related to low overall survival in OV patients. The TIMER result showed that high expression of RTP4 promoted immune infiltration of CD8+ T cells, B cells, neutrophils, and dendritic cells in OV. Moreover, silencing RTP4 significantly inhibited the proliferation, migration, and invasion of OV cells. RTP4 was associated with the poor prognosis in OV. In summary, silencing RTP4 inhibited the proliferation, migration, and invasion of OV cells, having the potential to be a novel therapeutic target for OV.

Investigation of shared genetic features and related mechanisms between diabetes and tuberculosis

OBJECTIVE

This study aimed to integrate bioinformatics technology to explore shared hub genes and related mechanisms between diabetes and tuberculosis and to provide a theoretical basis for revealing the disease mechanisms in patients with both diabetes and tuberculosis.

METHODS

Differentially expressed genes and Venn analysis were used to identify shared genes between diabetes and tuberculosis. PPI network analysis was used to screen key hub genes. GO and KEGG analyses were used to analyze the potential biological functions of these key hub genes. Immune infiltration analysis was performed using the ssGSEA algorithm. EnrichR online analysis website was used to explore potential therapeutic drugs.

RESULTS

The dataset analysis showed that PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3 were six hub genes shared by diabetes and tuberculosis, which not only could distinguish between the two disease samples but also had a high diagnostic rate. GO and KEGG analyses showed that these six genes mainly mediate immune-related biological processes such as interferon, interleukin, and chemokine receptor binding, as well as signaling pathways such as RIG-I-like receptor, NOD-like receptor, and proteasome. Immune infiltration analysis showed that high expression of TIL may mediate the development of both diabetes and tuberculosis. In addition, suloctidil HL60 UP, thioridazine HL60 UP, mefloquine HL60 UP, 1-NITROPYRENE CTD 00001569, and chlorophyllin CTD 00000324 were the candidate drugs predicted by this study that were most likely to target hub genes.

CONCLUSION

Six differentially expressed genes shared by both diseases (PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3) may play a key role in the disease progression of patients with both diabetes and tuberculosis. Candidate drugs targeting these hub genes have therapeutic potential and are worthy of further research. In summary, this study reveals potential shared pathogenic mechanisms between tuberculosis and diabetes.

Discovering common pathogenetic processes between COVID-19 and tuberculosis by bioinformatics and system biology approach

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood.

Methods

To elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs.

Results

A total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3'-azido-3'-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol.

Conclusion

This research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.

Associations between prediagnostic aspirin use and ovarian tumor gene expression

BACKGROUND

Aspirin use has been associated with reduced ovarian cancer risk, yet the underlying biological mechanisms are not fully understood. To gain mechanistic insights, we assessed the association between prediagnosis low and regular-dose aspirin use and gene expression profiles in ovarian tumors.

METHODS

RNA sequencing was performed on high-grade serous, poorly differentiated, and high-grade endometrioid ovarian cancer tumors from the Nurses' Health Study (NHS), NHSII, and New England Case-Control Study (n = 92 cases for low, 153 cases for regular-dose aspirin). Linear regression identified differentially expressed genes associated with aspirin use, adjusted for birth decade and cohort. False discovery rates (FDR) were used to account for multiple testing and gene set enrichment analysis was used to identify biological pathways.

RESULTS

No individual genes were significantly differentially expressed in ovarian tumors in low or regular-dose aspirin users accounting for multiple comparisons. However, current versus never use of low-dose aspirin was associated with upregulation of immune pathways (e.g., allograft rejection, FDR = 5.8 × 10-10 ; interferon-gamma response, FDR = 2.0 × 10-4 ) and downregulation of estrogen response pathways (e.g., estrogen response late, FDR = 4.9 × 10-8 ). Ovarian tumors from current regular aspirin users versus never users were also associated with upregulation in interferon pathways (FDR <1.5 × 10-4 ) and downregulation of multiple extracellular matrix (ECM) architecture pathways (e.g., ECM organization, 4.7 × 10-8 ).

CONCLUSION

Our results suggest low and regular-dose aspirin may impair ovarian tumorigenesis in part via enhancing adaptive immune response and decreasing metastatic potential supporting the likely differential effects on ovarian carcinogenesis and progression by dose of aspirin.

Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma

Advanced esophageal squamous cell carcinoma (ESCC) still has a dismal prognostic outcome. However, the current approaches are unable to evaluate patient survival. Pyroptosis represents a novel programmed cell death type which widely investigated in various disorders and can influence tumor growth, migration, and invasion. Furthermore, few existing studies have used pyroptosis-related genes (PRGs) to construct a model for predicting ESCC survival. Therefore, the present study utilized bioinformatics approaches for analyzing ESCC patient data obtained from the TCGA database to construct the prognostic risk model and applied it to the GSE53625 dataset for validation. There were 12 differentially expressed PRGs in healthy and ESCC tissue samples, among which eight were selected through univariate and LASSO cox regression for constructing the prognostic risk model. According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes. Based on the cell validation analysis, C2, CD14, RTP4, FCER3A, and SLC7A7 were expressed higher in KYSE410 and KYSE510 than in normal cells (HET-1A). Hence, ESCC patient prognostic outcomes can be assessed by our PRGs-based risk model. Further, these PRGs may also serve as therapeutic targets.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

RTP4, a Biomarker Associated with Diagnosing Pulmonary Tuberculosis and Pan-Cancer Analysis

Background

Pulmonary tuberculosis (PTB) is a global epidemic of infectious disease; the purpose of our study was to explore new potential biomarkers for the diagnosis of pulmonary tuberculosis and to use the biomarkers for further pan-cancer analysis.

Methods

Four microarray gene expression sets were downloaded from the GEO public databases and conducted for further analysis. Healthy control (HC) samples and samples of pulmonary tuberculosis (PTB) were calculated with enrichment scores in folate biosynthesis pathways. The scores acted as a new phenotype combined with clinical information (control or PTB) for subsequent analysis. Weight gene coexpression network analysis (WGCNA) was used to seek the modules mostly related to PTB and folate biosynthesis in training sets. Twenty-nine coexistence genes were screened by intersecting the genes in the green-yellow module of GSE28623 and the brown module of GSE83456. We used the protein-protein interaction network analysis to narrow the gene range to search for hub genes. Then, we downloaded the unified and standardized pan-cancer data set from the UCSC database for correlations between biomarkers and prognosis and tumor stage differences.

Results

Eventually, RTP4 was selected as a biomarker. To verify the reliability of this biomarker, an area under the ROC (AUC) was calculated in gene sets (GSE28623, GSE83456, and GSE34608). Lastly, to explore the difference in RTP4 expression before and after antituberculosis treatment, the GSE31348 gene set was enrolled to compare the expressions in weeks 0 and 26. The results showed significant differences between these two time points (p < 0.001). RTP4 was significantly upregulated in the pulmonary tuberculosis group compared to the healthy control group in three gene sets and downregulated after antituberculosis therapy in one gene set. These results suggest that RTP4 can be used as a potential biomarker in diagnosing tuberculosis. The results of pan-cancer analysis showed that high expression of RTP4 in 4 tumor types was positively correlated with poor prognosis and high expression of RTP4 in 6 tumor types was negatively correlated with poor prognosis. We found significant differences in the expression of the RTP4 gene at different stages in 5 types of tumors.

Conclusion

RTP4 might be a new potential biomarker for diagnosing pulmonary tuberculosis.

Apprising Diagnostic and Prognostic Biomarkers in Cutaneous Melanoma—Persistent Updating

The incidence of melanoma, a very aggressive skin cancer, has increased over the past few decades. Although there are well-established clinical, dermoscopic and histopathological criteria, the diagnosis is often performed late, which has important implications on the patient’s clinical outcome. Unfortunately, melanoma is one of the most challenging tumors to diagnose because it is a heterogeneous neoplasm at the clinical, histopathological, and molecular level. The use of reliable biomarkers for the diagnosis and monitoring of disease progression is becoming a standard of care in modern medicine. In this review, we discuss the latest studies, which highlight findings from the genomics, epitranscriptomics, proteomics and metabolomics areas, pointing out different genes, molecules and cells as potential diagnostic and prognostic biomarkers in cutaneous melanoma.

Insights from two independent transcriptomic studies of the bovine corpus luteum during pregnancy

Several recent studies have used transcriptomics to investigate luteal changes during the maternal recognition of the pregnancy period in ruminants. Although these studies have contributed to our understanding of luteal function during early pregnancy, few attempts have been made to integrate information across these studies and distinguish key luteal transcripts or functions that are repeatably identified across multiple studies. Therefore, in this study, two independent studies of the luteal transcriptome during early pregnancy were combined and compared. In the first study, corpora lutea (CL) from day 20 of pregnancy were compared with CL collected on day 14 of pregnancy, prior to embryonic signaling. The cattle were nonlactating. In the second study, CL from day 20 of pregnancy were compared with CL collected from day 20 cyclic cattle that had been confirmed as not yet undergoing luteal regression. These were lactating cattle. Three methods were used to compare these two datasets, to identify key luteal regulators. In the first method, all transcripts with Benjamini-Hochberg-adjusted P-value (Q value) < 0.05 in both datasets were considered. This yielded 22 transcripts, including several classical interferon-stimulated genes, as well as regulators of transforming growth factor-beta (TGFB) and latent TGFB-binding proteins (LTBP)1 and 2. In the second, less conservative method, all transcripts with P < 0.01 and changed in the same direction in both datasets were considered. This yielded an additional 20 transcripts that were not identified in the first analysis, for a total of 42 common transcripts. These transcripts were regulators of functions such as inflammatory balance and matrix remodeling. In the third method, transcripts with Q < 0.10 were subject to pathway analysis, and common pathways were identified. Retinoic acid signaling and classical interferon signaling pathways were identified with this method. Finally, regulation by interferon tau (IFNT) was investigated. Among the 42 transcripts identified, 32 were regulated by IFNT in cultured luteal cells (Q < 0.05). Among those not regulated by IFNT were LTBP1 and 2, which are TGFB-binding proteins. In summary, common transcripts from two studies of the luteal transcriptome during early pregnancy were combined and shared changes were identified. This not only generated a list of potential key luteal regulators, which were mostly IFNT regulated, but also included transcripts not regulated by IFNT, including LTBP1 and 2.

Identification and characterization of circular RNAs in association with the feed efficiency in Hu lambs

Background

Circular RNA (circRNA), as a new members of noncoding RNA family, have vital functions in many biological processes by as microRNA sponges or competing endogenous RNAs (ceRNAs). However, little has been reported about the genetic mechanism of circRNAs regulation of feed efficiency in sheep.

Results

This study aimed to explore the expression of circRNAs in the liver of Hu sheep with High-RFI (High residual feed intake) and Low-RFI (Low residual feed intake) using transcriptome sequencing. A total of 20,729 circRNAs were identified in two groups, in which 219 circRNAs were found as significantly differentially expressed. Several circRNAs were validated by using RT-PCR, sanger sequencing and RT-qPCR methods. These results demonstrated that the RNA-seq result and expression level of circRNAs identified are reliable. Subsequently, GO and KEGG enrichment analysis of the parental genes of the differentially expressed (DE) circRNAs were mainly involved in immunity response and metabolic process. Finally, the ceRNA regulatory networks analysis showed that the target binding sites for miRNA such as novel_41, novel_115, novel_171 and oar-miR-485-3p in the identified DE cirRNAs. Importantly, two metabolic (SHISA3 and PLEKHH2) and four (RTP4, CD274, OAS1, and RFC3) immune-related target mRNAs were identified from 4 miRNAs. Association analysis showed that the polymorphism (RTP4 c.399 A > G) in the target gene RTP4 were significantly associated with RFI (P < 0.05).

Conclusions

Analysis of sequencing data showed some candidate ceRNAs that may play key roles in the feed efficiency in sheep by regulating animal immune and metabolic. These results provide the basis data for further study of the biological functions of circRNAs in regulating sheep feed efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08517-5.