Differentially Expressed Genes Research Articles

A hypoxia–glycolysis–lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

Abstract Background Liver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC. Methods This study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes. Results We classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1. Conclusion The hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Transcriptome sequencing reveals novel biomarkers and immune cell infiltration in esophageal tumorigenesis

BACKGROUND Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies worldwide, and its development comprises a multistep process from intraepithelial neoplasia (IN) to carcinoma (CA). However, the critical regulators and underlying molecular mechanisms remain largely unknown. AIM To explore the genes and infiltrating immune cells in the microenvironment that are associated with the multistage progression of ESCC to facilitate diagnosis and early intervention. METHODS A mouse model mimicking the multistage development of ESCC was established by providing warter containing 4-nitroquinoline 1-oxide (4NQO) to C57BL/6 mice. Moreover, we established a control group without 4NQO treatment of mice. Then, transcriptome sequencing was performed for esophageal tissues from patients with different pathological statuses, including low-grade IN (LGIN), high-grade IN (HGIN), and CA, and controlled normal tissue (NOR) samples. Differentially expressed genes (DEGs) were identified in the LGIN, HGIN, and CA groups, and the biological functions of the DEGs were analyzed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The CIBERSORT algorithm was used to detect the pattern of immune cell infiltration. Immunohistochemistry (IHC) was also conducted to validate our results. Finally, the Luminex multiplex cytokine analysis was utilized to measure the serum cytokine levels in the mice. RESULTS Compared with those in the NOR group, a total of 681541, and 840 DEGs were obtained in the LGIN, HGIN, and CA groups, respectively. Using the intersection of the three sets of DEGs, we identified 86 genes as key genes involved in the development of ESCC. Enrichment analysis revealed that these genes were enriched mainly in the keratinization, epidermal cell differentiation, and interleukin (IL)-17 signaling pathways. CIBERSORT analysis revealed that, compared with those in the NOR group, M0 and M1 macrophages in the 4NQO group showed stronger infiltration, which was validated by IHC. Serum cytokine analysis revealed that, compared with those in the NOR group, IL-1β and IL-6 were upregulated, while IL-10 was downregulated in the LGIN, HGIN, and CA groups. Moreover, the expression of the representative key genes, such as S100a8 and Krt6b, was verified in external human samples, and the results of immunohistochemical staining were consistent with the findings in mice. CONCLUSION We identified a set of key genes represented by S100a8 and Krt6b and investigated their potential biological functions. In addition, we found that macrophage infiltration and abnormal alterations in the levels of inflammation-associated cytokines, such as IL-1β, IL-6, and IL-10, in the peripheral blood may be closely associated with the development of ESCC.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

Identification of Key Genes and Imbalanced SNAREs Assembly in the Comorbidity of Polycystic Ovary Syndrome and Depression

Background: Women with polycystic ovary syndrome (PCOS) have increased odds of concurrent depression, indicating that the relationship between PCOS and depression is more likely to be comorbid. However, the underlying mechanism remains unclear. Here, we aimed to use bioinformatic analysis to screen for the genetic elements shared between PCOS and depression. Methods: Differentially expressed genes (DEGs) were screened out through GEO2R using the PCOS and depression datasets in NCBI. Protein–protein interaction (PPI) network analysis and enrichment analysis were performed to identify the potential hub genes. After verification using other PCOS and depression datasets, the associations between key gene polymorphism and comorbidity were further studied using data from the UK biobank (UKB) database. Results: In this study, three key genes, namely, SNAP23, VTI1A, and PRKAR1A, and their related SNARE interactions in the vesicular transport pathway were identified in the comorbidity of PCOS and depression. The rs112568544 at SNAP23, rs11077579 and rs4458066 at PRKAR1A, and rs10885349 at VTI1A might be the genetic basis of this comorbidity. Conclusions: Our study suggests that the SNAP23, PRKAR1A, and VTI1A genes can directly or indirectly participate in the imbalanced assembly of SNAREs in the pathogenesis of the comorbidity of PCOS and depression. These findings may provide new strategies in diagnosis and therapy for this comorbidity.

Cryoprotectant-Mediated Cold Stress Mitigation in Litchi Flower Development: Transcriptomic and Metabolomic Perspectives

Temperature is vital in plant growth and agricultural fruit production. Litchi chinensis Sonn, commonly known as litchi, is appreciated for its delicious fruit and fragrant blossoms and is susceptible to stress when exposed to low temperatures. This study investigates the effect of two cryoprotectants that counteract cold stress during litchi flowering, identifies the genes that generate the cold resistance induced by the treatments, and hypothesizes the roles of these genes in cold resistance. Whole plants were treated with Bihu and Liangli cryoprotectant solutions to protect inflorescences below 10 °C. The soluble protein, sugar, fructose, sucrose, glucose, and proline contents were measured during inflorescence. Sucrose synthetase, sucrose phosphate synthetase, antioxidant enzymes (SOD, POD, CAT), and MDA were also monitored throughout the flowering stage. Differentially expressed genes (DEGs), gene ontology, and associated KEGG pathways in the transcriptomics study were investigated. There were 1243 DEGs expressed after Bihu treatment and 1340 in the control samples. Signal transduction pathways were associated with 39 genes in the control group and 43 genes in the Bihu treatment group. The discovery of these genes may contribute to further research on cold resistance mechanisms in litchi. The Bihu treatment was related to 422 low-temperature-sensitive differentially accumulated metabolites (DAMs), as opposed to 408 DAMs in the control, mostly associated with lipid metabolism, organic oxidants, and alcohols. Among them, the most significant differentially accumulated metabolites were involved in pathways such as β-alanine metabolism, polycyclic aromatic hydrocarbon biosynthesis, linoleic acid metabolism, and histidine metabolism. These results showed that Bihu treatment could potentially promote these favorable traits and increase fruit productivity compared to the Liangli and control treatments. More genomic research into cold stress is needed to support the findings of this study.

Identification of potential biomarkers of gout through weighted gene correlation network analysis

BackgroundAlthough hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity.MethodsUsing the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT−qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments.ResultsAccording to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein–protein interaction (PPI) network. Using RT−qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins.ConclusionIn this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells.

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Inhibition of the galactosyltransferase C1GALT1 reduces osteosarcoma cell proliferation by interfering with ERK signaling and cell cycle progression.

Novel therapeutic strategies are urgently required for osteosarcoma, given the early age at onset and persistently high mortality rate. Modern transcriptomics techniques can identify differentially expressed genes (DEGs) that may serve as biomarkers and therapeutic targets, so we screened for DEGs in osteosarcoma. We found that osteosarcoma cases could be divided into fair and poor survival groups based on gene expression profiles. Among the genes upregulated in the poor survival group, siRNA-mediated knockdown of the glycosylation-related gene C1GALT1 suppressed osteosarcoma cell proliferation in culture. Gene expression, phosphorylation, and glycome array analyses also demonstrated that C1GALT1 is required to maintain ERK signaling and cell cycle progression. Moreover, the C1GALT1 inhibitor itraconazole suppressed osteosarcoma cell proliferation in culture, while doxycycline-induced shRNA-mediated knockdown reduced xenograft osteosarcoma growth in mice. Elevated C1GALT1 expression is a potential early predictor of poor prognosis, while pharmacological inhibition may be a feasible treatment strategy for osteosarcoma.

Sex Differences in Cochlear Transcriptomes in Horseshoe Bats

Sexual dimorphism of calls is common in animals, whereas studies on the molecular basis underlying this phenotypic variation are still scarce. In this study, we used comparative transcriptomics of cochlea to investigate the sex-related difference in gene expression and alternative splicing in four Rhinolophus taxa. Based on 31 cochlear transcriptomes, we performed differential gene expression (DGE) and alternative splicing (AS) analyses between the sexes in each taxon. Consistent with the degree of difference in the echolocation pulse frequency between the sexes across the four taxa, we identified the largest number of differentially expressed genes (DEGs) and alternatively spliced genes (ASGs) in R. sinicus. However, we also detected multiple DEGs and ASGs in taxa without sexual differences in echolocation pulse frequency, suggesting that these genes might be related to other parameters of echolocation pulse rather than the frequency component. Some DEGs and ASGs are related to hearing loss or deafness genes in human or mice and they can be considered to be candidates associated with the sexual differences of echolocation pulse in bats. We also detected more than the expected overlap of DEGs and ASGs in two taxa. Overall, our current study supports the important roles of both DGE and AS in generating or maintaining sexual differences in animals.

SOX6 AU controls myogenesis by cis-modulation of SOX6 in cattle

ABSTRACT Long non-coding RNAs (lncRNAs) have been shown to be involved in the regulation of skeletal muscle development through multiple mechanisms. The present study revealed that the lncRNA SOX6 AU (SRY-box transcription factor 6 antisense upstream) is reverse transcribed from upstream of the bovine sex-determining region Y (SRY)-related high-mobility-group box 6 (SOX6) gene. SOX6 AU was significantly differentially expressed in muscle tissue among different developmental stages in Xianan cattle. Subsequently, knockdown and overexpression experiments discovered that SOX6 AU promoted primary skeletal muscle cells proliferation, apoptosis, and differentiation in bovine. The overexpression of SOX6 AU in bovine primary skeletal muscle cells resulted in 483 differentially expressed genes (DEGs), including 224 upregulated DEGs and 259 downregulated DEGs. GO functional annotation analysis showed that muscle development-related biological processes such as muscle structure development and muscle cell proliferation were significantly enriched. KEGG pathway analysis revealed that the PI3K/AKT and MAPK signaling pathways were important pathways for DEG enrichment. Notably, we found that SOX6 AU inhibited the mRNA and protein expression levels of the SOX6 gene. Moreover, knockdown of the SOX6 gene promoted the proliferation and apoptosis of bovine primary skeletal muscle cells. Finally, we showed that SOX6 AU promoted the proliferation and apoptosis of bovine primary skeletal muscle cells by cis-modulation of SOX6 in cattle. This work illustrates our discovery of the molecular mechanisms underlying the regulation of SOX6 AU in the development of beef.

Altered Expression of B Cell Receptor Signaling Pathway Genes in Peripheral Blood of Patients with Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease and has adverse implications. The exact mechanism of its pathogenesis is not fully understood and remains to be elucidated. In the current study we aimed to identify key genes that can serve as potential biomarkers and therapeutic targets for MS and shed light on pathogenesis mechanisms involved in MS.
 We analyzed a gene expression dataset (GES21942) and found 266 differentially expressed genes (DEGs) including 183 upregulated and 83 downregulated genes in MS patients compared to controls. Then we conducted pathway enrichment on DEGs and selected the top enriched pathway i.e., B cell receptor signaling pathway, and 5 genes of this pathway (CR2, BLK, BLNK, RASGRP3, and KRAS) for further investigation in our clinical samples. We recruited 50 MS patients and 50 controls and assessed the expression of selected genes in the circulation of patients versus controls.
 Expression of CR2, BLK, BLNK, and RASGRP3 were significantly higher in MS cases compared with controls. There was no significant difference in expression of KRAS between patients and controls. All of the selected genes with differential expression had noticeable diagnostic power and CR2 was the most robust gene in differentiating MS cases from controls. Additionally, a combination of genes resulted in enhanced diagnostic power.
 Collectively our results suggest that the B cell receptor signaling pathway and the selected genes from this pathway may be implicated in the pathogenesis of MS and each of these genes can be considered as potential diagnostic biomarkers and therapeutic targets.

The Characterization of G-Quadruplexes in Tobacco Genome and Their Function under Abiotic Stress

Tobacco is an ideal model plant in scientific research. G-quadruplex is a guanine-rich DNA structure, which regulates transcription and translation. In this study, the prevalence and potential function of G-quadruplexes in tobacco were systematically analyzed. In tobacco genomes, there were 2,924,271,002 G-quadruplexes in the nuclear genome, 430,597 in the mitochondrial genome, and 155,943 in the chloroplast genome. The density of the G-quadruplex in the organelle genome was higher than that in the nuclear genome. G-quadruplexes were abundant in the transcription regulatory region of the genome, and a difference in G-quadruplex density in two DNA strands was also observed. The promoter of 60.4% genes contained at least one G-quadruplex. Compared with up-regulated differentially expressed genes (DEGs), the G-quadruplex density in down-regulated DEGs was generally higher under drought stress and salt stress. The G-quadruplex formed by simple sequence repeat (SSR) and its flanking sequence in the promoter region of the NtBBX (Nitab4.5_0002943g0010) gene might enhance the drought tolerance of tobacco. This study lays a solid foundation for further research on G-quadruplex function in tobacco and other plants.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.MethodsWe first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.ResultsThe datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.ConclusionThe new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Cotyledonary somatic embryo is one kind of intermediate material similar to callus in the process of in vitro tissue culture from Rosa hybrida ‘John F. Kennedy’

BackgroundRose is recognized as an important ornamental plant worldwide, and it is also one of the most widely used flowers in gardens. At present, the improvement of rose traits is still difficult and uncertain, and molecular breeding can provide new ideas for the improvement of modern rose varieties. Somatic embryos are quite good receptors for genetic transformation. However, little is known about the molecular mechanisms underlying during the regeneration process of rose somatic embryos. To elucidate the molecular regulation mechanism of somatic embryo plantlet regeneration, the relationship between the differences in traits of the two different regenerated materials and the significantly differentially expressed genes (DEGs) related to phytohormone pathways in the process of regeneration were be investigated.ResultsThese representative two regenerated samples from single-piece cotyledonary somatic embryo (SPC) culture of Rosa hybrida ‘John F. Kennedy’, were harvested for transcriptome analysis, with the SPC explants at the initial culture (Day 0) as the control. The differentially expressed genes (DEGs) in the materials from two different types for regeneration approach (SBF type: the regeneration approach type of single bud formed from SPC explants; MBF type: the regeneration approach type of multiple buds formed from SPC explants) were be screened by means of the transcriptome sequencing technology. In this study, a total of about 396.24 million clean reads were obtained, of which 78.95–82.92% were localized to the reference genome, compared with the initial material (CK sample), there were 5594 specific genes in the material of SBF type and 6142 specific genes in the MBF type. The DEGs from the SBF type material were mainly concentrated in the biological processes of GO terms such as phytohormones, substance transport, cell differentiation, and redox reaction. The KEGG enrichment analysis revealed these DEGs were more active in ubiquinone and other terpenoid-quinone biosynthesis, fatty acid elongation, steroid biosynthesis, and glycosphingolipid biosynthesis-globo and isoglobo series. In contrast, the DEGs induced by the MBF type material were mainly associated with the biological processes such as phytohormones, phosphorylation, photosynthesis and signal transduction. According to KEGG analysis, these DEGs of MBF type were significantly enriched in the porphyrin and chlorophyll metabolism, brassinosteroid biosynthesis, carotenoid biosynthesis, and peroxisome. Furthermore, the results from the phytohormone pathways analysis showed that the auxin-responsive factor SAUR and the cell wall modifying enzyme gene XTH were upregulated for expression but the protein phosphatase gene PP2C was downregulated for expression in SBF type; the higher expression of the ethylene receptor ETR, the ethylene transduction genes EBF1/2, the transcription factor EIN3, and the ethylene-responsive transcription factor ERF1/2 were induced by MBF type.ConclusionsAccording to the GO and KEGG analysis, it indicated the DEGs between two different regenerated materials from somatic embryos were significantly different which might be causing morphological differences. That was somatic embryos from Rosa hybrida ‘John F. Kennedy’ could regenerate plantlet via both classic somatic embryogenesis (seed-like germination) and organogenesis, cotyledonary somatic embryos should be considered as one kind of intermediate materials similiar to callus, rather than the indicator materials for somatic embryogenesis.

Investigating the Role of SNAI1 and ZEB1 Expression in Prostate Cancer Progression and Immune Modulation of the Tumor Microenvironment

Prostate cancer (PCa) is an immunologically cold tumor and the molecular processes that underlie this behavior are poorly understood. In this study, we investigated a primary cohort of intermediate-risk PCa (n = 51) using two NanoString profiling panels designed to study cancer progression and immune response. We identified differentially expressed genes (DEGs) and pathways associated with biochemical recurrence (BCR) and clinical risk. Confirmatory analysis was performed using the TCGA-PRAD cohort. Noteworthy DEGs included collagens such as COL1A1, COL1A2, and COL3A1. Changes in the distribution of collagens may influence the immune activity in the tumor microenvironment (TME). In addition, immune-related DEGs such as THY1, IRF5, and HLA-DRA were also identified. Enrichment analysis highlighted pathways such as those associated with angiogenesis, TGF-beta, UV response, and EMT. Among the 39 significant DEGs, 11 (28%) were identified as EMT target genes for ZEB1 using the Harmonizome database. Elevated ZEB1 expression correlated with reduced BCR risk. Immune landscape analysis revealed that ZEB1 was associated with increased immunosuppressive cell types in the TME, such as naïve B cells and M2 macrophages. Increased expression of both ZEB1 and SNAI1 was associated with elevated immune checkpoint expression. In the future, modulation of EMT could be beneficial for overcoming immunotherapy resistance in a cold tumor, such as PCa.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis.Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies.Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM.Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

A prognostic model of idiopathic pulmonary fibrosis constructed based on macrophage and mitochondria-related genes

BackgroundStudies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited.MethodsTo construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups.ResultsThere were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2.ConclusionWe achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

Transcriptomic and targeted metabolomic analyses provide insights into the flavonoids biosynthesis in the flowers of Lonicera macranthoides

BackgroundFlavonoids are one of the bioactive ingredients of Lonicera macranthoides (L. macranthoides), however, their biosynthesis in the flower is still unclear. In this study, combined transcriptomic and targeted metabolomic analyses were performed to clarify the flavonoids biosynthesis during flowering of L. macranthoides.ResultsIn the three sample groups, GB_vs_WB, GB_vs_WF and GB_vs_GF, there were 25, 22 and 18 differentially expressed genes (DEGs) in flavonoids biosynthetic pathway respectively. A total of 339 flavonoids were detected and quantified at four developmental stages of flower in L. macranthoides. In the three sample groups, 113, 155 and 163 differentially accumulated flavonoids (DAFs) were detected respectively. Among the DAFs, most apigenin derivatives in flavones and most kaempferol derivatives in flavonols were up-regulated. Correlation analysis between DEGs and DAFs showed that the down-regulated expressions of the CHS, DFR, C4H, F3’H, CCoAOMT_32 and the up-regulated expressions of the two HCTs resulted in down-regulated levels of dihydroquercetin, epigallocatechin and up-regulated level of kaempferol-3-O-(6’’-O-acetyl)-glucoside, cosmosiin and apigenin-4’-O-glucoside. The down-regulated expressions of F3H and FLS decreased the contents of 7 metabolites, including naringenin chalcone, proanthocyanidin B2, B3, B4, C1, limocitrin-3,7-di-O-glucoside and limocitrin-3-O-sophoroside.ConclusionThe findings are helpful for genetic improvement of varieties in L.macranthoides.