Decoding pathogenesis factors involved in the progression of ATLL or HAM/TSP after infection by HTLV-1 through a systems virology study

The promoting effects of Grin2d expression in tumorigenesis and the aggressiveness of esophageal cancer

Grin2d is an ionotropic NMDA receptor, a subunit of glutamate-dependent, and a facilitator of cellular calcium influx in neuronal tissue. In this study, we found that Grin2d expression was higher in esophageal cancer than in normal mucosa at both the mRNA and protein level using RT-PCR, bioinformatics analysis, and western blotting (p<0.05). Grin2d mRNA expression was positively correlated with old age, white race, heavy weight, distal location, adenocarcinoma, cancer with Barrett's lesion, or high-grade columnar dysplasia (p<0.05). The differential genes associated with Grin2d mRNA were involved in fat digestion and absorption, cholesterol metabolism, lipid transfer, lipoproteins, synaptic membranes, and ABC transporters (p<0.05). The Grin2d-related genes were classified into the following categories: metabolism of glycerolipids, galactose, and O-glycan, cell adhesion binding, actin binding, cadherin binding, the Hippo signaling pathway, cell-cell junctions, desmosomes, DNA-transcription activator binding, and skin development and differentiation (p<0.05). Grin2d immunoreactivity was positively correlated with distal metastasis and unfavorable overall survival in esophageal cancer (p<0.05). Grin2d overexpression promoted proliferation, migration, and invasion in esophageal cancer cells but blocked apoptosis (p<0.05) and increased the expression of PI3K, Akt and p-mTOR. Grin2d knockout caused the opposite effects. These findings indicated that upregulated Grin2d expression played an important role in esophageal carcinogenesis via the PI3K/Akt/mTOR pathway and might be a biological marker for aggressive tumor behavior and poor prognosis. Its silencing might represent a targeted therapy approach against esophageal cancer.

Prognostic value of long noncoding RNA LINC00924 in lung adenocarcinoma and its regulatory effect on tumor progression

PURPOSE

Long non-coding RNAs (lncRNAs) have been used in the study of tumor biomarkers in recent years. However, the prognostic role of lncRNA LINC00924 (LINC00924) in lung adenocarcinoma (LUAD) has not yet been concluded. Therefore, this study investigates the prognostic value of LINC00924 in LUAD and its regulatory effect on tumor progression.

PATIENTS AND METHODS

The LUAD tissues and adjacent normal tissues of 128 subjects were extracted, and the expressions of LINC00924 and miR-196a-5p in tissues and cells were detected by RT-qPCR. The prognostic value of LINC00924 in LUAD patients was obtained by Kaplan-Meier analysis and multivariate Cox regression test. The cell counting kit-8 (CCK-8) and Transwell assay were used to detect the effect of overexpression LINC00924 on LUAD cells.

RESULTS

In LUAD tissues and cells, LINC00924 expression was down-regulated and miR-196a-5p expression was up-regulated compared with the normal control group. High expression of LINC00924 inhibited the proliferation level, migration ability and invasion situation of LUAD cells, which was more conducive to the survival and prognosis of LUAD patients. Bioinformatics studies indicated that overexpression of LINC00924 inhibited the development of LUAD by targeting miR-196a-5p, while miR-196a-5p mimic effectively weakened the inhibition.

CONCLUSION

LINC00924 sponges of miR-196a-5p may be considered as a potential prognostic biomarker for LUAD.

Comparative transcriptomic analysis of mammary gland tissues reveals the critical role of GPR110 in palmitic acid-stimulated milk protein and fat synthesis

The G protein-coupled receptors (GPCR) sensing nutritional signals (amino acids, fatty acids, glucose, etc.) are not fully understood. In this research, we used transcriptome sequencing to analyse differentially expressed genes (DEG) in mouse mammary gland tissues at puberty, lactation and involution stages, in which eight GPCR were selected out and verified by qRT-PCR assay. It was further identified the role of GPR110-mediating nutrients including palmitic acid (PA) and methionine (Met) to improve milk synthesis using mouse mammary epithelial cell line HC11. PA but not Met affected GPR110 expression in a dose-dependent manner. GPR110 knockdown decreased milk protein and fat synthesis and cell proliferation and blocked the stimulation of PA on mechanistic target of rapamycin (mTOR) phosphorylation and sterol-regulatory element binding protein 1c (SREBP-1c) expression. In summary, these experimental results disclose DEG related to lactation and reveal that GPR110 mediates PA to activate the mTOR and SREBP-1c pathways to promote milk protein and fat synthesis.

The mirror like expression of genes involved in the FOXO signaling pathway could be effective in the pathogenesis of human lymphotropic virus type 1 (HTLV-1) through disruption of the downstream pathways

OBJECTIVES

Human lymphotropic virus type 1 (HTLV-1) is the cause of two major diseases, ATLL and HAM/TSP in a percentage of carriers. Despite progress in understanding the pathogenesis of these two diseases, the exact pathogenesis mechanism is still not well understood. High-throughput technologies have revolutionized medical research. This study aims to investigate the mechanism of pathogenesis of these two diseases using the results of high-throughput analysis of microarray datasets.

RESULTS

A total of 100 differentially expressed genes were found between ATLL and HAM/TSP. After constructing protein-protein network and further analyzing, proteins including ATM, CD8, CXCR4, PIK3R1 and CD2 were found as the hub ones between ATLL and HAM/TSP. Finding the modules of the subnetwork revealed the enrichment of two common pathways including FOXO signaling pathway and Cell cycle with two common genes including ATM and CDKN2D. Unlike ATLL, ATM gene had higher expressions in HAM/TSP patients. The expression of CDKN2D was increased in ATLL patients. The results of this study could be helpful for understanding the pathogenic mechanism of these two diseases in the same signaling pathways.

Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co‑expression and support vector machine algorithms

Adult T-cell leukemia/lymphoma (ATLL) is pathogen-caused cancer that is progressed after the infection by human T-cell leukemia virus type 1. Four significant subtypes comprising acute, lymphoma, chronic, and smoldering have been identified for this cancer. However, there are no trustworthy prognostic biomarkers for these subtypes. We utilized a combination of two powerful network-based and machine-learning algorithms including differential co-expressed genes (DiffCoEx) and support vector machine-recursive feature elimination with cross-validation (SVM-RFECV) methods to categorize disparate ATLL subtypes from asymptomatic carriers (ACs). The results disclosed the significant involvement of CBX6, CNKSR1, and MAX in chronic, MYH10 and P2RY1 in acute, C22orf46 and HNRNPA0 in smoldering subtypes. These genes also can classify each ATLL subtype from AC carriers. The integration of the results of two powerful algorithms led to the identification of reliable gene classifiers and biomarkers for diverse ATLL subtypes.

Analysis of Programmed Cell Death-1 (PD-1) Gene Variations (re11568821 and rs41386349) in HTLV-1 Infection Using One Primer Pair and Proviral Load

About 90% of people infected with Human T lymphotropic virus type-1 (HTLV-1) virus are asymptomatic, so it can be said that the prevalence of this virus is not completely clear. During chronic infection, the expression of programmed cell death-1 (PD-1) protein increases and causes exhausted phenotype in T cells. Considering the role of host genetics and immune responses in HTLV-1 infection, in this case-control study, included 81 asymptomatic carriers (ACs) and 162 healthy controls (HCs), rs11568821 and rs41386349 polymorphisms of PD-1 gene were evaluated by Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method which investigated by one primer pair for both polymorphisms also, proviral load (PVL) measured by quantitative real-time PCR (Q-RT-PCR). The results showed that the mutant allele of rs11568821 (A) and rs41386349 (T) polymorphisms is associated with an increase in HTLV-1 infection significantly (p = 0.019 and p = 0.000 respectively). But there was no significant relationship between PVL and polymorphisms.

Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus

BACKGROUND

Adult T-cell Leukemia/Lymphoma (ATLL) is a rapidly progressing type of T-cell non-Hodgkin lymphoma that is developed after the infection by human T-cell leukemia virus type 1 (HTLV-1). It could be categorized into four major subtypes, acute, lymphoma, chronic, and smoldering. These different subtypes have some shared clinical manifestations, and there are no trustworthy biomarkers for diagnosis of them.

METHODS

We applied weighted-gene co-expression network analysis to find the potential gene and miRNA biomarkers for various ATLL subtypes. Afterward, we found reliable miRNA-gene interactions by identifying the experimentally validated-target genes of miRNAs.

RESULTS

The outcomes disclosed the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-575 with UBN2, miR-342-3p with ZNF280B, and miR-342-5p with FOXRED2 in ATLL_chronic, miR-940 and miR-423-3p with C6orf141, miR-940 and miR-1225-3p with CDCP1, and miR-324-3p with COL14A1 in ATLL_smoldering. These miRNA-gene interactions determine the molecular factors involved in the pathogenesis of each ATLL subtype and the unique ones could be considered biomarkers.

CONCLUSION

The above-mentioned miRNAs-genes interactions are suggested as diagnostic biomarkers for different ATLL subtypes.

Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes

BACKGROUND

Adult T-cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T-cells in Human T-Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miRNAs) in various malignancies, the analysis of high-throughput miRNA data employing computational algorithms helps to identify potential biomarkers.

METHODS

Weighted gene co-expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein-protein interaction networks (PPIN). Real-time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network.

RESULTS

After constructing co-expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL-specific modules. Next, the hub miRNA including hsa-miR-18a-3p, has-miR-187-5p, hsa-miR-196a-3p, and hsa-miR-346 were found as hub miRNAs. The protein-protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse-transcriptase PCR in ATLL patients.

CONCLUSION

The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers.

Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning

Background

Adult T-cell Leukemia/Lymphoma (ATLL) is a cancer disease that is developed due to the infection by human T-cell leukemia virus type 1. It can be classified into four main subtypes including, acute, chronic, smoldering, and lymphoma. Despite the clinical manifestations, there are no reliable diagnostic biomarkers for the classification of these subtypes.

Methods

Herein, we employed a machine learning approach, namely, Support Vector Machine-Recursive Feature Elimination with Cross-Validation (SVM-RFECV) to classify the different ATLL subtypes from Asymptomatic Carriers (ACs). The expression values of multiple mRNAs and miRNAs were used as the features. Afterward, the reliable miRNA-mRNA interactions for each subtype were identified through exploring the experimentally validated-target genes of miRNAs.

Results

The results revealed that miR-21 and its interactions with DAAM1 and E2F2 in acute, SMAD7 in chronic, MYEF2 and PARP1 in smoldering subtypes could significantly classify the diverse subtypes.

Conclusions

Considering the high accuracy of the constructed model, the identified mRNAs and miRNA are proposed as the potential therapeutic targets and the prognostic biomarkers for various ATLL subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09540-1.

Evaluating the cytotoxicity and pathogenicity of multi-walled carbon nanotube through weighted gene co-expression network analysis: a nanotoxicogenomics study

Background

Multi-walled carbon nanotube (MWCNT) is one of the most momentous carbonaceous nanoparticles which is widely used for various applications such as electronics, vehicles, and therapeutics. However, their possible toxicity and adverse effects convert them into a major health threat for humans and animals.

Results

In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify the co-expressed gene groups and dysregulated pathways due to the MWCNT exposure. For this purpose, three weighted gene co-expression networks for the microarray gene expression profiles of the mouse after 1, 6, and 12-month post-exposure to MWCNT were constructed. The module-trait analysis specified the significant modules related to different doses (1, 10, 40, and 80 µg) of MWCNT. Afterward, common genes between co-regulated and differentially expressed genes were determined. The further pathway analysis highlighted the enrichment of genes including Actb, Ube2b, Psme3, Ezh2, Alas2, S100a10, Ypel5, Rhoa, Rac1, Ube2l6, Prdx2, Ctsb, Bnip3l, Gp6, Myh9, Ube2k, Mbnl1, Kbtbd8, Riok3, Itgb1, Rap1a, and Atp5h in immune-, inflammation-, and protein metabolism-related pathways.

Conclusions

This study discloses the genotoxicity and cytotoxicity effects of various doses of MWCNT which also affect the metabolism system. The identified genes can serve as potential biomarkers and therapeutic candidates. However, further studies should be performed to validate them in human cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01031-3.

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP)

Human T-cell Leukemia Virus type-1 (HTLV-1) is an oncovirus that may cause two main life-threatening diseases including a cancer type named Adult T-cell Leukemia/Lymphoma (ATLL) and a neurological and immune disturbance known as HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP). However, a large number of the infected subjects remain as asymptomatic carriers (ACs). There is no comprehensive study that determines which dysregulated genes differentiate the pathogenesis routes toward ATLL or HAM/TSP. Therefore, two main algorithms including weighted gene co-expression analysis (WGCNA) and multi-class support vector machines (SVM) were utilized to find major gene players in each condition. WGCNA was used to find the highly co-regulated genes and multi-class SVM was employed to identify the most important classifier genes. The identified modules from WGCNA were validated in the external datasets. Furthermore, to find specific modules for ATLL and HAM/TSP, the non-preserved modules in another condition were found. In the next step, a model was constructed by multi-class SVM. The results revealed 467, 3249, and 716 classifiers for ACs, ATLL, and HAM/TSP, respectively. Eventually, the common genes between the WGCNA results and classifier genes resulted from multi-class SVM that also determined as differentially expressed genes, were identified. Through these step-wise analyses, PAIP1, BCAS2, COPS2, CTNNB1, FASLG, GTPBP1, HNRNPA1, RBBP6, TOP1, SLC9A1, JMY, PABPC3, and PBX1 were found as the possible critical genes involved in the progression of ATLL. Moreover, FBXO9, ZNF526, ERCC8, WDR5, and XRCC3 were identified as the conceivable major involved genes in the development of HAM/TSP. These genes can be proposed as specific biomarker candidates and therapeutic targets for each disease.