ALDH2 is a novel biomarker and exerts an inhibitory effect on melanoma.

Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein-protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2'-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma.

Integrative Analysis of the Age-Related Dysregulated Genes Reveals an Inflammation and Immunity-Associated Regulatory Network in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease with a long incubation period. While extensive research has led to the construction of long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) regulatory networks, which primarily derived from differential analyses between clinical AD patients and control individuals or mice, there remains a critical knowledge gap pertaining to the dynamic alterations in transcript expression profiles that occur with age, spanning from the pre-symptomatic stage to the onset of AD. In the present study, we examined the transcriptomic changes in AD model mice at three distinct stages: the unaffected (un-) stage, the pre-onset stage, and the late-onset stage, and identified 14, 57, and 99 differentially expressed mRNAs (DEmRs) in AD model mice at 3, 6, and 12 months, respectively. Among these, we pinpointed 16 mRNAs closely associated with inflammation and immunity and excavated their lncRNA-mRNA regulatory network based on a comprehensive analysis. Notably, our preliminary analysis suggested that four lncRNAs (NONMMUT102943, ENSMUST00000160309, NONMMUT083044, and NONMMUT126468), eight miRNAs (miR-34a-5p, miR-22-5p, miR-302a/b-3p, miR-340-5p, miR-376a/b-5p, and miR-487b-5p), and four mRNAs (C1qa, Cd68, Ctss, and Slc11a1) may play pivotal roles in orchestrating immune and inflammatory responses during the early stages of AD. Our study has unveiled age-related AD risk genes, and provided an analytical framework for constructing lncRNA-mRNA networks using time series data and correlation analysis. Most notably, we have successfully constructed a comprehensive regulatory ceRNA network comprising genes intricately linked to inflammatory and immune functions in AD.

Role of SARS­CoV­2 nucleocapsid protein in affecting immune cells and insights on its molecular mechanisms.

The present study aimed to explore the immune regulatory function of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein and related mechanisms. In a series of protein activity experiments, SARS-CoV-2 N protein promoted proliferation of three immune cell lines: mouse Raw264.7, human Jurkat and human Raji in a dose-dependent manner. A total of 10 µg/ml N protein could significantly change cell cycle progression of the aforementioned three immune cell lines and could promote quick entry of Raw264.7 cells into G2/M phase from S phase to achieve rapid growth. Additionally, the N protein could also stimulate Raw264.7 cells to secrete a number of proinflammatory factors such as TNF-α, IL-6 and IL-10. RNA sequencing analysis indicated that the N protein changed the expression of certain genes involved in immune-related functions and four important signaling pathways, including JAK-STAT, TNF, NF-κB and MAPK signaling pathways, which suggested that the N protein may not only regulate the expression of genes involved in the process of resisting viral infection in macrophages of the immune system, but also change cellular signal processing.

Differential transcriptome response following infection of porcine ileal enteroids with species A and C rotaviruses.

BACKGROUND: Rotavirus C (RVC) is the major causative agent of acute gastroenteritis in suckling piglets, while most RVAs mostly affect weaned animals. Besides, while most RVA strains can be propagated in MA-104 and other continuous cell lines, attempts to isolate and culture RVC strains remain largely unsuccessful. The host factors associated with these unique RVC characteristics remain unknown. METHODS: In this study, we have comparatively evaluated transcriptome responses of porcine ileal enteroids infected with RVC G1P[1] and two RVA strains (G9P[13] and G5P[7]) with a focus on innate immunity and virus-host receptor interactions. RESULTS: The analysis of differentially expressed genes regulating antiviral immune response indicated that in contrast to RVA, RVC infection resulted in robust upregulation of expression of the genes encoding pattern recognition receptors including RIG1-like receptors and melanoma differentiation-associated gene-5. RVC infection was associated with a prominent upregulation of the most of glycosyltransferase-encoding genes except for the sialyltransferase-encoding genes which were downregulated similar to the effects observed for G9P[13]. CONCLUSIONS: Our results provide novel data highlighting the unique aspects of the RVC-associated host cellular signalling and suggest that increased upregulation of the key antiviral factors maybe one of the mechanisms responsible for RVC age-specific characteristics and its inability to replicate in most cell cultures.

Molecular mechanisms investigation for liver metastasis of colorectal cancer by combined bioinformatic gene expression profile analysis.

BACKGROUND: Colorectal cancer (CRC) is one of the most common cancers worldwide. As the molecular mechanism for liver metastasis of CRC has not yet been completely discovered, identification of hub genes and pathways of this disease is of importance for revealing potential molecular mechanism of colorectal cancer progression. This study aimed to identify potential biomarkers and survival analysis of hub genes for CRC treatment. METHODS: The differentially expressed genes (DEGs) between colorectal cancer liver metastasis and primary tumor were screened using microarray data from two datasets GSE179979, GSE144259 obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and KEGG pathway enrichment analyses were performed for DEGs using DAVID database, the protein-protein interaction (PPI) network was constructed using the Cytoscape software, and module analysis was performed using MCODE. Then, overall survival (OS), progression free interval (PFI) and disease specific survival (DSS) analysis of hub genes was performed by using TCGA database. The correlations between hub genes and clinical values were validated through CRN and immunohistochemistry (IHC) stain. RESULTS: A total of 64 DEGs were obtained, KEGG pathway analysis showed that the significant pathways included PPAR signaling pathway, Complement and coagulation cascades. Four hub genes (ITIH2, ALB, CPB2, HGFAC) and two biomarkers (CPB2, HGFAC) with significantly prognostic values were verified by Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) cohort. CONCLUSIONS: CPB2 and HGFAC may serve as new biomarkers in diagnosing liver metastasis of CRC or potential drug target.

SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach.

SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods. Supplementary Information: The online version contains supplementary material available at 10.1007/s42979-023-01703-6.

Prognostic Prediction Model for Glioblastoma: A Ferroptosis-Related Gene Prediction Model and Independent External Validation.

Glioblastoma (GBM) is the most common primary malignant intracranial tumor with a poor prognosis. Ferroptosis is a newly discovered, iron-dependent, regulated cell death, and recent studies suggest its close correlation to GBM. The transcriptome and clinical data were obtained for patients diagnosed with GBM from TCGA, GEO, and CGGA. Ferroptosis-related genes were identified, and a risk score model was constructed using Lasso regression analyses. Survival was evaluated by univariate or multivariate Cox regressions and Kaplan-Meier analyses, and further analyses were performed between the high- and low-risk groups. There were 45 ferroptosis-related different expressed genes between GBM and normal brain tissues. The prognostic risk score model was based on four favorable genes, CRYAB, ZEB1, ATP5MC3, and NCOA4, and four unfavorable genes, ALOX5, CHAC1, STEAP3, and MT1G. A significant difference in OS between high- and low-risk groups was observed in both the training cohort (p < 0.001) and the validation cohorts (p = 0.029 and 0.037). Enrichment analysis of pathways and immune cells and functioning was conducted between the two risk groups. A novel prognostic model for GBM patients was developed based on eight ferroptosis-related genes, suggesting a potential prediction effect of the risk score model in GBM.

Muscle transcriptome analysis reveal candidate genes and pathways related to fat and lipid metabolism in Yunling cattle.

Yunling cattle (YL) is a recently developed beef breed harboring a quarter of Yunnan ancestral cattle genome, spanning over past 30 years. Compared with Diqing cattle (DQ), a Yunnan native cattle breed, YL presents various advantages, including rapid growth and exquisite meat quality. However, the molecular mechanisms underlying these phenotypic differences are not clearly understood. To further identify the candidate genes responsible for the quality of the meat in the muscle, longissimus dorsi (LD) muscle was used for RNA-Seq analysis. A total of 508 differentially expressed genes (DEGs) were identified in YL (adjusted p-value <0.01 and log2FoldChange >1), of which 243 were up-regulated and 265 were down-regulated. Functional association analysis showed that the identified DEGs mainly enriched the lipid and fat metabolism pathways. Moreover, it was also observed that several fat-related genes were differentially expressed in both cattle breeds, including three up-regulated genes (MOGAT1, ACSM3, PLPP2) and two down-regulated genes (ADIG, GPAT3). In addition, alternative splice analysis was also performed revealing an important 9-11 exon skipping variation of GPAM gene (crucial for beef marbling) in YL, which is three times higher than that in DQ, suggesting that this variation might have played the central role in the 'snow beef' effect in YL. We believe that our results will help in understanding the mechanism of muscle development and promote the further breeding programs in YL cattle.

Insight into sulfamethoxazole effects on aerobic denitrification by strain Pseudomonas aeruginosa PCN-2: From simultaneous degradation performance to transcriptome analysis.

It is a well-established fact that aerobic denitrifying strains are profoundly affected by antibiotics, but bacterium performing simultaneous aerobic denitrification and antibiotic degradation is hardly reported. Here, a typical aerobic denitrifying bacterium Pseudomonas aeruginosa PCN-2 was discovered to be capable of sulfamethoxazole (SMX) degradation. The results showed that nitrate removal efficiency was decreased from 100% to 88.12%, but the resistance of strain PCN-2 to SMX stress was enhanced with the increment of SMX concentration from 0 to 100 mg/L. Transcriptome analysis revealed that the down-regulation of energy metabolism pathways rather than the denitrifying functional genes was responsible for the suppressed nitrogen removal, while the up-regulation of antibiotic resistance pathways (e.g., biofilm formation, multi-drug efflux system, and quorum sensing) ensured the survival of bacterium and the carrying out of aerobic denitrification. Intriguingly, strain PCN-2 could degrade SMX during aerobic denitrification. Seven metabolites were identified by the UHPLC-MS, and three degradation pathways (which includes a new pathway that has never been reported) was proposed combined with the expressions of drug metabolic genes (e.g., cytP450, FMN, ALDH and NAT). This work provides a mechanistic understanding of the metabolic adaption of strain PCN-2 under SMX stress, which provided a broader idea for the treatment of SMX-containing wastewater.

Progress in adverse effects of sleep deprivation on body fertility / 中国药理学与毒理学杂志

Sleep is essential for the maintenance of human normal functions.Nowadays,the occurrence of active sleep deprivation(ASD)or passive sleep depriva-tion(PSD)is becoming more and more common due to the inability of the body adapting to the rapid changes in the internal and external environment.SD is not only an action,a brief process or a result,but also a directly or indirectly sustained state,which is associated to sleep time,circadian rhythm or sleep quality.SD can lead to numerous adverse effects on the body,such as sleep-related acute and chronic diseases.Long-term SD increases the risk of neurological and cardiovascular dis-eases as well as immune system dysfunction.In addi-tion,SD may affect the reproductive health of the body,giving rise to a series of potential fertility problems.In recent years,the correlation research and mechanism between SD and the related diseases have become a focus of scholars' attention.Numerous lines of evidence suggest that pathological sleep,such as insomnia and sleep apnea syndrome,is associated with impaired repro-ductive function.Disruptions in the circadian rhythm can also lead to impaired hypothalamic-pituitary-gonadal(HPG)axis function and thereby interfere with the repro-ductive process.Our research team has demonstrated that SD significantly affects the fertility of male and female rats and has adverse effects on reproduction.By new generation sequencing(NGS)and RT-PCR verifica-tion,we have identified differently expressed genes that are involved in mediating the effect of SD on fertility.However,the mechanisms and biological macromolecules regulated by SD are worthy of being further explored.This paper provides a brief review of SD research and then focuses on the adverse impact of SD on fertility,conducting a literature review to sort out the ideas and pro-vide references for research in this field.

Integrated computational approaches to screen gene expression data to determine key genes and therapeutic targets for type-2 diabetes mellitus.

There is a rapid rise in cases of Type-2-diabetes mellitus (T2DM) globally, irrespective of the geography, ethnicity or any other variable factors. The molecular mechanisms that could cause the condition of T2DM need to be more thoroughly analysed to understand the clinical manifestations and to derive better therapeutic regimes. Tools in bioinformatics are used to trace out key gene elements and to identify the key causative gene elements and their possible therapeutic agents. Microarray datasets were retrieved from the Gene expression omnibus database and studied using R to derive different expressed gene (DEG) elements. With the comparison of the expressed genes with disease specific genes in DisGeNET, the final annotated genes were taken for analysis. Gene Ontology studies, Protein-protein interaction (PPI), Co-expression analysis, Gene-drug interactions were performed to scale down the hub genes and to identify the novelty across the genes analysed so far. In vivo and invitro analysis of key genes and the trace of interaction pathway is crucial to better understand the unique outcomes from the novel genes, forming the basis to understand the pathway that ends up causing T2DM. Afterwards, docking was executed enabling recognition of interacting residues involved in inhibition. The complex CCL5-265 and CD8A-40585 thus docked showed best results as is evident from its PCA analysis and MMGBSA calculation. There is now scope for deriving candidate drugs that could possibly detect personalized therapies for T2DM.

Changes in the Transcriptome Caused by Mutations in the Ribosomal Protein uS10 Associated with a Predisposition to Colorectal Cancer.

A number of mutations in the RPS20 gene encoding the ribosomal protein uS10 have been found to be associated with a predisposition to hereditary non-polyposis colorectal carcinoma (CRC). We transfected HEK293T cells with constructs carrying the uS10 minigene with mutations identical to those mentioned above and examined the effects of the produced proteins on the cellular transcriptome. We showed that uS10 with mutations p.V50SfsX23 or p.L61EfsX11 cannot be incorporated into 40S ribosomal subunits, while the protein with the missense mutation p.V54L functionally replaces the respective endogenous protein in the 40S subunit assembly and the translation process. The comparison of RNA-seq data obtained from cells producing aberrant forms of uS10 with data for those producing the wild-type protein revealed overlapping sets of upregulated and downregulated differently expressed genes (DEGs) related to several pathways. Among the limited number of upregulated DEGs, there were genes directly associated with the progression of CRC, e.g., PPM1D and PIGN. Our findings indicate that the accumulation of the mutant forms of uS10 triggers a cascade of cellular events, similar to that which is triggered when the cell responds to a large number of erroneous proteins, suggesting that this may increase the risk of cancer.

Corrigendum: Exploration of the Role of Serine Proteinase Inhibitor A3 in Alcohol Dependence Using Gene Expression Omnibus Database.

[This corrects the article DOI: 10.3389/fpsyt.2021.779143.].

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA).

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.

Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning.

Ulcerative colitis (UC) is an immune-related disorder with enhanced prevalence globally. Early diagnosis is critical for the effective treatment of UC. However, it still lacks specific diagnostic signatures. The aim of our study was to explore efficient signatures and construct the diagnostic model for UC. Microarray data of GSE87473 and GSE48634, which were obtained from tissue biopsy samples, were downloaded from the Gene Expression Omnibus (GEO), and differently expressed genes (DEGs), GO, and KEGG analyses were performed. We constructed the PPI network via STRING database. The immune infiltration of the samples was evaluated using CIBERSORT methods combined with the LM22 feature matrix. The logistic regression model was constructed, with the expression of selected genes as the predictor variable, and the UC occurrence as the responsive variable. As a result, a total of 126 DEGs between the UC patients and normal counterparts were identified. The GO and KEGG analysis revealed that multiple biological processes, such as antimicrobial humoral immune response mediated by antimicrobial peptide and IL-17 signaling pathway, were enriched. The infiltration of eight immune cell types (B cells naive, Dendritic.cells.activated, Macrophages.M0, Macrophages.M2, Mast.cells.resting, Neutrophils, Plasma.cells, and T.cells.follicular.helper) was significantly different between patients with UC and normal counterparts. The top 50 most significant DEGs were selected for the construction of the PPI network. The average AUC of the logistic regression model in the fivefold cross-validation was 0.8497 in the training set, GSE87473. The AUC of another independent verification set of GSE48634 from the GEO database was 0.7208. In conclusion, we identified potential hub genes, including REG3A, REG1A, DEFA6, REG1B, and DEFA5, which might be significantly associated with UC progression. The logistic regression model based on the five genes could reliably diagnose UC patients.

Identification of the key genes and immune infiltrating cells determined by sex differences in ischaemic stroke through co-expression network module.

Stroke is one of the leading causes of patients' death and long-term disability worldwide, and ischaemic stroke (IS) accounts for nearly 80% of all strokes. Differential genes and weighted gene co-expression network analysis (WGCNA) in male and female patients with IS were compared. The authors used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) to analyse the distribution pattern of immune subtypes between male and female patients. In this study, 141 up-regulated and 61 down-regulated genes were gathered and distributed into five modules in response to their correlation degree to clinical traits. The criterion for Gene Ontology (GO) term and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway indicated that detailed analysis had the potential to enhance clinical prediction and to identify the gender-related mechanism. After that, the expression levels of hub genes were measured via the quantitative real-time PCR (qRT-PCR) method. Finally, CCL20, ICAM1 and PTGS2 were identified and these may be some promising targets for sex differences in IS. Besides, the hub genes were further verified by rat experiments. Furthermore, these CIBERSORT results showed that T cells CD8 and Monocytes may be the target for the treatment of male and female patients, respectively.

Construction and analysis of the abnormal lncRNA-miRNA-mRNA network in hypoxic pulmonary hypertension.

The incidence of hypoxic pulmonary hypertension (HPH) is increasing. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) play an important role in HPH, but the functions and mechanism have yet to be fully elucidated. In the present study, we established a HPH rat model with 8 h of hypoxia exposure (10% O2) per day for 21 days. High-throughput sequencing identified 60 differentially expressed (DE) lncRNAs, 20 DE miRNAs and 695 DE mRNAs in rat lung tissue. qRT-PCR verified the accuracy of the results. The DE mRNAs were significantly enriched in immune response, inflammatory response, leukocyte migration, cell cycle, cellular response to interleukin-1, IL-17 signalling pathway, cytokine-cytokine receptor interaction and Toll-like receptor signalling pathway. According to the theory of competing endogenous RNA (ceRNA) networks, lncRNA-miRNA-mRNA network was constructed by Cytoscape software, 16 miRNAs and 144 mRNAs. The results suggested that seven DE lncRNAs (Ly6l, AABR07038849.2, AABR07069008.2, AABR07064873.1, AABR07001382.1, AABR07068161.1 and AABR07060341.2) may serve as molecular sponges of the corresponding miRNAs and play a major role in HPH.

Melatonin Regulates the Periodic Growth of Cashmere by Upregulating the Expression of Wnt10b and ß-catenin in Inner Mongolia Cashmere Goats.

Secondary hair follicle growth in cashmere goats has seasonal cycle changes, and melatonin (MT) has a regulatory effect on the cashmere growth cycle. In this study, the growth length of cashmere was measured by implanting MT in live cashmere goats. The results indicated that the continuous implantation of MT promoted cashmere to enter the anagen 2 months earlier and induce secondary hair follicle development. HE staining of skin tissues showed that the number of secondary hair follicles in the MT-implanted goats was significantly higher than that in the control goats (P < 0.05). Transcriptome sequencing of the skin tissue of cashmere goats was used to identify differentially expressed genes: 532 in February, 641 in October, and 305 in December. Fluorescence quantitative PCR and Western blotting results showed that MT had a significant effect on the expression of Wnt10b, ß-catenin, and proteins in the skin tissue of Inner Mongolia cashmere goats. This finding suggested that MT alters the cycle of secondary hair follicle development by changing the expression of related genes. This research lays the foundation for further study on the mechanism by which MT regulates cashmere growth.

Construction of Long Noncoding RNA-Associated ceRNA Networks Reveals Potential Biomarkers in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disease that seriously impairs both cognitive and memory functions mainly in the elderly, and its incidence increases with age. Recent studies demonstrated that long noncoding RNAs (lncRNAs) play important roles in AD by acting as competing endogenous RNAs (ceRNAs). OBJECTIVE: In this study, we aimed to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers in AD based on the ceRNA hypothesis. METHODS: A total of 20 genes (10 upregulated genes and 10 downregulated genes) were identified as the hub differentially expressed genes (DEGs). The functional enrichment analysis showed that the most significant pathways of DEGs involved include retrograde endocannabinoid signaling, synaptic vesicle circle, and AD. The upregulated hub genes were mainly enriched in the cytokine-cytokine receptor interaction pathway, whereas downregulated hub genes were involved in the neuroactive ligand-receptor interaction pathway. After convergent functional genomic (CFG) ranks and expression level analysis in different brain regions of hub genes, we found that CXCR4, GFAP, and GNG3 were significantly correlated with AD. We further identified crucial miRNAs and lncRNAs of targeted genes to construct lncRNA-associated ceRNA regulatory networks. RESULTS: The results showed that two lncRNAs (NEAT1, MIAT), three miRNAs (hsa-miR-551a, hsa-miR-133b and hsa-miR-206), and two mRNA (CXCR4 and GNG3), which are highly related to AD, were preliminarily identified as potential AD biomarkers. CONCLUSION: Our study provides new insights for understanding the pathogenic mechanism underlying AD, which may potentially contribute to the ceRNA mechanism in AD.

Transcriptome sequencing-based personalized analysis of hepatocellular carcinoma patients with portal vein tumor thrombus.

BACKGROUND: The mechanism of portal vein tumor thrombus (PVTT) in hepatocellular carcinoma (HCC) has been widely studied, and numerous diagnostic and prognostic biomarkers for HCC with PVTT have been identified. We aimed to evaluate the extent to which these biomarkers may aid the personalized precision therapy of HCC with PVTT. METHODS: Matched tissue specimens [primary HCC tumor (PT), adjacent normal (N) liver, and PVTT tissues] were acquired from 3 Chinese HCC patients who underwent surgery at Sun Yat-sen University Cancer Centre between 2019 and 2020. Ribonucleic acid (RNA) sequencing was performed on the 9 tissue samples. GFOLD (generalized fold change) algorithm was used to analyze the differently expressed genes (DEGs) between the PVTT, PT, and normal tissues from each patient. Genes with a P<0.01 and a |GFOLD value| >1 were identified as having significantly different expression. RESULTS: In total, 3,543, 32,472, and 12,901 tumorigenesis-associated genes, and 2,919, 17,679, and 14,825 metastasis-associated genes, were detected in Patient 1 (P1), Patient 2 (P2), and Patient 3 (P3), respectively. We analyzed the expression levels of genes associated with hypoxia, macrophage recruitment and cancer stem cells (CSCs). The results showed that hypoxia and CSCs may have contributed to tumorigenesis but not to metastasis in P1. We also found the hypoxia microenvironment played an important role in tumorigenesis and metastasis in P2, and CSCs may have contributed to metastasis. Additionally, we found that CSCs played critical roles in metastasis but not in tumorigenesis in P3. The results also showed that the long non-coding RNA (lncRNA) Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) was greatly overexpressed in the PTs and PVTT in all 3 patients, and Heart and Neural Crest Derivatives Expressed 2-antisense RNA 1 (HAND2-AS1) was downregulated in PVTT compared with PTs in all 3 patients. Thus, MALAT1 and HAND2-AS1 may be robust biomarkers for metastasis in HCC patients with PVTT. CONCLUSIONS: Tumor-associated macrophages (TAMs)-targeted immunotherapy is a promising therapy for HCC patients with PVTT. LncRNAs MALAT1, and HAND2-AS1 may be promising targets for HCC therapy.