RNA-seq analyses of multiple meristems of soybean: novel and alternative transcripts, evolutionary and functional implications

Identification of diagnostic signature and immune infiltration for ischemic cardiomyopathy based on cuproptosis-related genes through bioinformatics analysis and experimental validation

BACKGROUND

Ischemic cardiomyopathy (IC) is primarily due to long-term ischemia/hypoxia of the coronary arteries, leading to impaired cardiac contractile or diastolic function. A new form of cell death induced by copper, called "cuproptosis" is related to the development and progression of multiple diseases. The cuproptosis-related gene (CuGs) plays an important role in acute myocardial infarction, while the specific mechanisms of CuGs in ischemic cardiomyopathy remain unclear.

METHODS

The expressions of CuGs and their immune characteristics were analyzed with the IC datasets obtained from the Gene Expression Omnibus, namely GSE5406 and GSE57338, identifying core genes associated with IC development. By comparing RF, SVM, GLM and XGB models, the optimal machine learning model was selected. The expression of marker genes was validated based on the GSE57345, GSE48166 and GSE42955 datasets. Construct a CeRNA network based on core genes. Therapeutic chemiacals targeting core genes were acquired using the CTD database, and molecular docking was performed using Autodock vina software. By ligating the left anterior descending (LAD) coronary artery, an IC mouse model is established, and core genes were experimentally validated using Western blot (WB) and immunohistochemistry (IHC) methods.

RESULTS

We identified 14 CuGs closely associated with the onset of IC. The SVM model exhibited superior discriminative power (AUC = 0.914), with core genes being DLST, ATP7B, FDX1, SLC31A1 and DLAT. Core genes were validated on the GSE42955, GSE48166 and GSE57345 datasets, showing excellent performance (AUC = 0.943, AUC = 0.800, and AUC = 0.932). The CeRNA network consists of 218 nodes and 264 lines, including 5 core diagnostic genes, 52 miRNAs, and 161 lncRNAs. Chemicals predictions indicated 8 chemicals have therapeutic effects on the core diagnostic genes, with benzo(a)pyrene molecular docking showing the highest affinity (-11.3 kcal/mol). Compared to the normal group, the IC group,which was established by LAD ligation, showed a significant decrease in LVEF as indicated by cardiac ultrasound, and increased fibrosis as shown by MASSON staining, WB results suggest increased expression of DLST and ATP7B, and decreased expression of FDX1, SLC31A1 and DLAT in the myocardial ischemic area (p < 0.05), which was also confirmed by IHC in tissue sections.

CONCLUSION

In summary, this study comprehensively revealed that DLST, ATP7B, FDX1, SLC31A1 and DLAT could be identified as potential immunological biomarkers in IC, and validated through an IC mouse model, providing valuable insights for future research into the mechanisms of CuGs and its diagnostic value to IC.

m6A- and m5C- modified lncRNAs orchestrate the prognosis in cutaneous melanoma and m6A- modified LINC00893 regulates cutaneous melanoma cell metastasis

BACKGROUND

As the most important modifications on the RNA level, N6-methyladenosine (m6A-) and 5-methylcytosine (m5C-) modification could have a direct influence on the RNAs. Long non-coding RNAs (lncRNAs) could also be modified by methylcytosine modification. Compared with mRNAs, the function of lncRNAs could be more potent to some extent in biological processes like tumorigenesis. Until now, rare reports have been done associated with cutaneous melanoma. Herein, we wonder if the m6A- and m5C- modified lncRNAs could influence the immune landscape and prognosis in melanoma, and we also want to find some lncRNAs which could directly affect the malignant behaviors of melanoma.

METHODS

Systematically, we explored the expression pattern of m6A- and m5C- modified lncRNAs in melanoma from datasets including UCSC Xena and NCBI GEO, and the prognostic lncRNAs were selected. Then, according to the expression pattern of lncRNAs, melanoma samples from these datasets were divided into several subtypes. Prognostic model, nomogram survival model, drug sensitivity, GO, and KEGG pathway analysis were performed. Furthermore, among several selected lncRNAs, we identified one lncRNA named LINC00893 and investigated its expression pattern and its biological function in melanoma cell lines.

RESULTS

We identified 27 m6A- and m5C- related lncRNAs which were significantly associated with survival, and we made a subtype analysis of melanoma samples based on these 27 lncRNAs. Among the two subtypes, we found differences of immune cells infiltration between these two subtypes. Then, LASSO algorithm was used to screen the optimized lncRNAs combination including ZNF252P-AS1, MIAT, FAM13A-AS1, LINC-PINT, LINC00893, AGAP2-AS1, OIP5-AS1, and SEMA6A-AS1. We also found that there was a significant correlation between the different risk groups predicted based on RS model and the actual prognosis. The nomogram survival model based on independent survival prognostic factors was also constructed. Besides, sensitivity to chemotherapeutic agents, GO and KEGG analysis were performed. In different risk groups, a total of 14 drug molecules with different distributions were obtained, which included AZD6482, AZD7762, AZD8055, camptothecin, dasatinib, erlotinib, gefitinib, gemcitabine, GSK269962A, nilotinib, rapamycin, and sorafenib. A total of 55 significantly related biological processes and 17 KEGG signaling pathways were screened. At last, we noticed that LINC00893 had a relatively lower expression in melanoma tissue and cell lines compared with adjacent tissues and epidermal melanocyte, and down-regulation of LINC00893 could promote the malignant behavior of melanoma cells in A875 and MV3. In these two melanoma cell lines, down-regulation of m6A-related molecules like YTHDF3 and METTL3 could promote the expression of LINC00893.

CONCLUSION

We made an analysis of m6A- and m5C- related lncRNAs in melanoma samples and a prediction of these lncRNAs' role in prognosis, tumor microenvironment, immune infiltration, and clinicopathological features. We also found that LINC00893, which is potentially regulated by m6A modification, could serve as a tumor-suppressor in melanoma and play an inhibitory role in melanoma metastasis.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Identification and validation of the TRHDE-AS1/hsa-miR-449a/ADAMTS5 axis as a novel prognostic biomarker in prostate cancer

Despite advancements in cancer research, the prognostic implications of competing endogenous RNA (ceRNA) networks in prostate cancer (PCa) remain incompletely understood. This study aimed to elucidate the prognostic relevance of ceRNA networks in PCa, utilizing a comprehensive bioinformatics approach alongside experimental validation. After searching The Cancer Genome Atlas (TCGA) database, RNA sequencing (RNA-Seq) data were extracted to identify differentially expressed RNAs (DERs) between 491 PCa samples and 51 normal prostate tissues, following which a comprehensive bioinformatics strategy was implemented to construct a ceRNA network. An optimal prognostic signature comprising these DERs was then established and validated using TCGA data. In addition, functional validation was performed through RNA pull-down, dual-luciferase reporter assays, quantitative real-time PCR, and western blot analysis conducted in PC-3 and DU145 cell lines, thereby complementing the bioinformatics analysis. A total of 613 DERs, comprising 103 long noncoding RNAs (lncRNAs), 60 microRNAs (miRNAs), and 450 messenger RNAs (mRNAs), were identified and utilized in constructing a ceRNA network, which encompassed 23 lncRNAs, 9 miRNAs, and 52 mRNAs. An optimal prognostic signature was established, including VPS9D1 antisense RNA 1 (VPS9D1-AS1), miR-449a, cyclin-dependent kinase 5 regulatory subunit 1 (CDK5R1), targeting protein for Xklp2 (TPX2), solute carrier family 7 member 11 (SLC7A11), copine7 (CPNE7), and maternal embryonic leucine zipper kinase (MELK), yielding area under the curve (AUC) values exceeding 0.8 across training, validation, and entire datasets. Our experiments results revealed an interaction between lncRNA TRHDE antisense RNA 1 (TRHDE-AS1) and miR-449a and that miR-449a could target the ADAM metallopeptidase with thrombospondin type 1 motif 5 (ADAMTS5) mRNA. Knockdown of miR-449a significantly impeded cell proliferation, G1/S transition, migration and invasion, and promoted apoptosis in PC-3 and DU145 cells. Furthermore, knockdown of miR-449a notably downregulated protein expression of CDK4, cyclin D1, N-cadherin and vimentin, while upregulating protein expression of cleaved caspase-3 and E-cadherin. This study contributes to a deeper understanding of the prognostic-linked ceRNA network in PCa, providing fundamental insights that could improve diagnostic and therapeutic approaches for PCa management.

Predictive Role of a Novel Ferroptosis-Related lncRNA Pairs Model in the Prognosis of Papillary Thyroid Carcinoma

This study aimed to evaluate the potential prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid carcinoma (PTC). Based on The TCGA database, lncRNAs and ferroptosis-related genes with differential expression levels in PTC tumors vs. normal tissues were screened. After the co-expression network construction, ferroptosis-related lncRNAs (FRLs) were screened. Kaplan-Meier analysis was conducted to compare the survival performance of patients with PTC in the high- and low-risk groups. Furthermore, a nomogram was created to enhance PTC prognosis. CIBERSORT was used to investigate the infiltration of various immune cells in high- and low-risk groups. In total, 10 lncRNA pairs with differential expression levels were obtained. There were significant differences in the histological subtype and pathological stage between the high- and low-risk groups, and age (P = 7.39E-13) and FRLM model status (P = 1.09E-04) were identified as independent prognostic factors. Subsequently, the nomogram survival model showed that the predicted one-, three-, and five-year survival rates were similar to the actual one- (c-index = 0.8475), three- (c-index = 0.7964), and five-year (c-index = 0.7555) survival rates. Subjects in the low-risk group had significantly more CD4 + memory T cells and resting myeloid dendritic cells, and subjects in the high-risk group had more plasma B cells and monocytes. The risk assessment model constructed using FRLs showed good predictive value for the prognosis of patients with PTC.

Construction and validation of m6A-related diagnostic model for psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory disease. N6-methyladenosine (m6A) is involved in numerous biological processes in both normal and diseased states. Herein, we aimed to explore the potential role of m6A regulators in the diagnosis of psoriasis and predict molecular mechanisms by which m6A regulators impact psoriasis.

Methods

GSE30999 (170 human skin tissue samples) and GSE13355 (180 human skin tissue samples) were downloaded as the training analysis dataset and validation dataset respectively. M6A-related genes were obtained from the literature and their expression levels in GSE30999 samples were measured to identify M6A-related DEGs between psoriasis lesions (LS) and non-lesional lesions (NL). We identified m6A-related DEGs using differential expression analysis and assessed their interactions through correlation analysis and network construction. A logistic regression analysis followed by LASSO optimization was employed to select m6A-related DEGs for the construction of a diagnostic model. The performance of the model was validated using support vector machine (SVM) methodology with sigmoid kernel function and extensive cross-validation. Additionally, the correlation between m6A-related DEGs and immune cell infiltration was analyzed, as well as the association of these DEGs with psoriasis subtypes. Functional analysis of the m6A-related DEGs included the construction of regulatory networks involving miRNAs, transcription factors (TFs), and small-molecule drugs. The m6A modification patterns were also explored by examining the gene expression differences between psoriasis subtypes and their enriched biological pathways. Finally, the expression of significant m6A regulators involved in the diagnostic model was examined by RT-qPCR.

Results

In this study, ten optimal m6A-related DEGs were identified, including FTO, IGF2BP2, METTL3, YTHDC1, ZC3H13, HNRNPC, IGF2BP3, LRPPRC, YTHDC2, and HNRNPA2B1. A diagnostic model based on these m6A-related DEGs was constructed, demonstrating high diagnostic accuracy with an area under the curve (AUC) in GSE30999 and GSE13355 of 0.974 and 0.730, respectively. Meanwhile, the expression level of m6A regulators verified by RT-qPCR was consistent with the results in GSE30999. The infiltration of activated mast cells and NK cells was significantly associated with all ten m6A-related DEGs in psoriasis. Among them, YTHDC1, HNRNPC, and FTO were targeted by most miRNAs and were regulated by nine related TFs. Therefore, patients may benefit from dorsomorphin and cyclosporine therapy. Between the two subgroups, 1,592 DEGs were identified, including LRPPRC and METTL3. These DEGs were predicted to be involved in neutrophil activation, cytokine-cytokine receptor interactions, and chemokine signaling pathways.

Conclusions

A diagnostic model based on ten m6A-related DEGs in patients with psoriasis was constructed, which may provide early diagnostic biomarkers and therapeutic targets for psoriasis.

Establishment of a diagnostic model based on immune-related genes in children with asthma

Objective

Allergic asthma is driven by an antigen-specific immune response. This study aimed to identify immune-related differentially expressed genes in childhood asthma and establish a classification diagnostic model based on these genes.

Methods

GSE65204 and GSE19187 were downloaded and served as training set and validation set. The immune cell composition was evaluated with ssGSEA algorithm based on the immune-related gene set. Modules that significantly related to the asthma were selected by WGCNA algorithm. The immune-related differentially expressed genes (DE-IRGs) were screened, the protein-protein interaction network and diagnostic model of DE-IRGs was constructed. The pathway and immune correlation analysis of hub DE-IRGs was analyzed.

Results

Eight immune cell types exhibited varying levels of abundance between the asthma and control groups. A total of 112 differentially expressed immune-related genes (DE-IRGs) was identified. Through the application of four ranking methods (MCC, MNC, DEGREE, and EPC), 17 hub DE-IRGs with overlapping significance were further selected. Subsequently, 8 optimized were identified using univariate logistic regression analysis and the LASSO regression algorithm, based on which a robust diagnostic model was constructed. Notably, TNF and CD40LG emerged as direct participants in asthma-related signaling pathways, displaying a positive correlation with the immune cell types of immature B cells, activated B cells, activated CD8 T cells, activated CD4 T cells, and myeloid-derived suppressor cells.

Conclusion

The diagnostic model constructed using the DE-IRGs (CCL5, CCR5, CD40LG, CD8A, IL2RB, PDCD1, TNF, and ZAP70) exhibited high and specific diagnostic value for childhood asthma. The diagnostic model may contribute to the diagnosis of childhood asthma.

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

PURPOSE

This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD).

METHODS

PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan-Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes.

RESULTS

The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine-cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration.

CONCLUSIONS

This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients

BACKGROUND/AIMS

The purpose of this study is to screen the feature genes related to gut microflora and explore the role of the genes in predicting the prognosis of patients with gastric cancer.

MATERIALS AND METHODS

We downloaded the gene profile of gastric cancer from the University of California Santa Cruz, the gut microflora related to gastric cancer from The Cancer Microbiome Atlas. The GSE62254 dataset was downloaded from National Center for Biotechnology Information Gene Expression Omnibus as a validation dataset. A correlation network between differentially expressed genes and gut microflora was constructed using Cytoscape. The optimized prognostic differentially expressed genes were identified through least absolute shrinkage and selection operator (LASSO) algorithm and univariate Cox regression analysis. The risk score model was established and then measured via Kaplan-Meier and area under the curve. Finally, the nomogram model was constructed according to the independent clinical factors, which was evaluated using C-index.

RESULTS

A total of 754 differentially expressed genes and 8 gut microflora were screened, based on which we successfully constructed the correlation network. We obtained 9 optimized prognostic differentially expressed genes, including HSD17B3, GNG7, CHAD, ARHGAP8, NOX1, YY2, GOLGA8A, DNASE1L3, and ABCA8. Moreover, Kaplan-Meier curves indicated the risk score model correctly predicted the prognosis of gastric cancer in both University of California Santa Cruz and GSE62254 dataset (area under the curve >0.8; area under the curve >0.7). Finally, we constructed the nomogram, in which the C index of 1, 3, and 5 years was 0.824, 0.772, and 0.735 representing that the nomogram was consistent with the actual situation.

CONCLUSIONS

These results indicate the 9 differentially expressed genes related to gut microflora might predict the survival time of patients with gastric cancer. Both risk signature and nomogram could effectively predict the prognosis for patients with gastric cancer.

Construction of a diagnostic model for osteoarthritis based on transcriptomic immune-related genes

Background

Osteoarthritis (OA) is a leading cause of disability globally, affecting over 500 million individuals worldwide. However, accurate and early diagnosis of OA is challenging to achieve. Immune-related genes play an essential role in OA development. Therefore, the objective of this study was to develop a diagnostic model for OA based on immune-related genes identified in synovial membrane.

Methods

The gene expression profile of OA were downloaded based on four datasets. The significantly differentially expressed genes (DEGs) between OA and control groups were selected. The differential immune cells were analyzed, followed by immune-related DEGs screening. WGCNA was used to screen module genes and these genes were further selected through optimization algorithm. Then, nomogram model was constructed. Chemical drug small molecule related to OA was predicted. Finally, expression levels of several key genes were validated by qRT-PCR through construction of OA rat models.

Results

The total 656 DEGs were obtained. Eight immune cells were significantly differential between two groups, and 317 immune-related DEGs were obtained. WGCNA identified three modules. The genes in modules were significantly involved in 15 pathways, involving in 65 genes. Then 12 DEGs were screened as the final optimal combination of DEGs, such as CEBPB, CXCL1, JUND, GABARAPL2 and PDGFC. The Nomogram model was also constructed. Furthermore, the chemical small molecules, such as acetaminophen, aspirin, and caffeine were predicted. The expression levels of CEBPB, CXCL1, GABARAPL2 and PDGFC were validated in OA rat models.

Conclusion

A diagnostic model based on twelve immune related genes was constructed. These model genes, such as CEBPB, CXCL1, GABARAPL2, and PDGFC, may serve as diagnostic biomarkers and immunotherapeutic targets.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer

OBJECTIVES

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

METHODS

We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

RESULTS

Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

CONCLUSIONS

The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

Pyroptosis is involved in ischemic cardiomyopathy (ICM). The study aimed to investigate the pyroptosis-related genes and clarify their diagnostic value in ICM. The bioinformatics method identified the differential pyroptosis genes between the normal control and ICM samples from online datasets. Then, protein-protein interaction (PPI) and function analysis were carried out to explore the function of these genes. Following, subtype analysis was performed using ConsensusClusterPlus, functions, immune score, stromal score, immune cell proportion and human leukocyte antigen (HLA) genes between subtypes were investigated. Moreover, optimal pyroptosis genes were selected using the least absolute shrinkage and selection operator (LASSO) analysis to construct a diagnostic model and evaluate its effectiveness using receiver operator characteristic (ROC) analysis. Twenty-one differential expressed pyroptosis genes were identified, and these genes were related to immune and pyroptosis. Subtype analysis identified two obvious subtypes: sub-1 and sub-2. And LASSO identified 13 optimal genes used to construct the diagnostic model. The diagnostic model in ICM diagnosis with the area under ROC (AUC) was 0.965. Our results suggested that pyroptosis was tightly associated with ICM.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

Genome-wide association study and RNA-seq identifies GmWRI1-like transcription factor related to the seed weight in soybean

The cultivated soybean (Glycine max (L.) Merrill) is domesticated from wild soybean (Glycine soja) and has heavier seeds with a higher oil content than the wild soybean. In this study, we identified a novel candidate gene associated with SW using a genome-wide association study (GWAS). The candidate gene GmWRI14-like was detected by GWAS analysis in three consecutive years. By constructing transgenic soybeans overexpressing the GmWRI14-like gene and gmwri14-like soybean mutants, we found that overexpression of GmWRI14-like increased the SW and increased total fatty acid content. We then used RNA-seq and qRT-PCR to identify the target genes directly or indirectly regulated by GmWRI14-like. Transgenic soyabeans overexpressing GmWRI14-like showed increased accumulation of GmCYP78A50 and GmCYP78A69 than non-transgenic soybean lines. Interestingly, we also found that GmWRI14-like proteins could interact with GmCYP78A69/GmCYP78A50 using yeast two-hybrid and bimolecular fluorescence complementation. Our results not only shed light on the genetic architecture of cultivated soybean SW, but also lays a theoretical foundation for improving the SW and oil content of soybeans.

Identification of potential Auxin Response Candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis

Glycine max, soybean, is an abundantly cultivated crop worldwide. Efforts have been made over the past decades to improve soybean production in traditional and organic agriculture, driven by growing demand for soybean-based products. Rapid canopy cover development (RCC) increases soybean yields and suppresses early-season weeds. Genome-wide association studies have found natural variants associated with RCC, however causal mechanisms are unclear. Auxin modulates plant growth and development and has been implicated in RCC traits. Therefore, modulation of auxin regulatory genes may enhance RCC. Here, we focus on the use of genomic tools and existing datasets to identify auxin signaling pathway RCC candidate genes, using a comparative phylogenetics and expression analysis approach. We identified genes encoding 14 TIR1/AFB auxin receptors, 61 Aux/IAA auxin co-receptors and transcriptional co-repressors, and 55 ARF auxin response factors in the soybean genome. We used Bayesian phylogenetic inference to identify soybean orthologs of Arabidopsis thaliana genes, and defined an ortholog naming system for these genes. To further define potential auxin signaling candidate genes for RCC, we examined tissue-level expression of these genes in existing datasets and identified highly expressed auxin signaling genes in apical tissues early in development. We identified at least 4 TIR1/AFB, 8 Aux/IAA, and 8 ARF genes with highly specific expression in one or more RCC-associated tissues. We hypothesize that modulating the function of these genes through gene editing or traditional breeding will have the highest likelihood of affecting RCC while minimizing pleiotropic effects.

Molecular characterization of ferroptosis in soft tissue sarcoma constructs a prognostic and immunotherapeutic signature through experimental and bioinformatics analyses

Ferroptosis regulators have been found to affect tumor progression. However, studies focusing on ferroptosis and soft tissue sarcoma (STS) are rare. Somatic mutation, copy number variation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, consensus clustering, differentially expressed genes analysis (DEGs), principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used to identify and explore different ferroptosis modifications in STS. A nomogram was constructed to predict the prognosis of STS. Moreover, three immunotherapy datasets were used to assess the Fescore. Western blotting, siRNA transfection, EdU assay and reactive oxygen species (ROS) measurement were performed. 16 prognostic ferroptosis regulators were screened and significant differences were observed in somatic mutation, copy number variation (CNV) and RT-qPCR among these ferroptosis regulators. 2 different ferroptosis modification patterns were found (Fe cluster A and B). Fe cluster A with higher Fescore was correlated with p53 pathway and had better prognosis of STS (p = 0.002) while Fe cluster B with lower Fescore was correlated with angiogenesis and MYC pathway and showed a poorer outcome. Besides, the nomogram effectively predicted the outcome of STS and the Fescore could also well predict the prognosis of other 16 tumors and immunotherapy response. Downregulation of LOX also inhibited growth and increased ROS production in sarcoma cells. The molecular characterization of ferroptosis regulators in STS was explored and an Fescore was constructed. The Fescore quantified ferroptosis modification in STS patients and effectively predicted the prognosis of a variety of tumors, providing novel insights for precision medicine.

Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease

AIMS

Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD.

METHODS AND RESULTS

Twelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD-HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme-linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL-23, and FGF-21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme-linked immunosorbent assay.

CONCLUSIONS

The combination of VEGFR2, FLRG, IL-23, and FGF-21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD.

A potential prognostic prediction model for metastatic osteosarcoma based on bioinformatics analysis

Osteosarcoma (OS) is a malignant primary bone tumor with a high incidence. This study aims to construct a prognostic prediction model by screening the prognostic mRNA of metastatic OS. Data on four eligible expression profiles from the National Center for Biotechnology Information Gene Expression Omnibus repository were obtained based on inclusion criteria and defined as the training set or the validation set. The differentially expressed genres (DEGs) between meta- static and non-metastatic OS samples in the training set were first identified, and DEGs related to prognosis were screened by univariate Cox regression analysis. In total, 107 DEGs related to the prognosis of metastatic OS were identified. Then, 46 DEGs were isolated as the optimized prognostic gene signature, and a metastatic-OS discriminating classifier was constructed, which had a high accuracy in distinguishing metastatic from non-metastatic OS samples. Furthermore, four optimized prognostic gene signatures (ALOX5AP, COL21A1, HLA-DQB1, and LDHB) were further screened, and the prognostic prediction model for metastatic OS was constructed. This model possesses a relatively satisfying prediction ability both in the training set and validation set. The prognostic prediction model that was constructed based on the four prognostic mRNA signatures has a high predictive ability for the prognosis of metastatic OS.

Uncovering transcriptional reprogramming during callus development in soybean: insights and implications

Callus, a valuable tool in plant genetic engineering, originates from dedifferentiated cells. While transcriptional reprogramming during callus formation has been extensively studied in Arabidopsis thaliana, our knowledge of this process in other species, such as Glycine max, remains limited. To bridge this gap, our study focused on conducting a time-series transcriptome analysis of soybean callus cultured for various durations (0, 1, 7, 14, 28, and 42 days) on a callus induction medium following wounding with the attempt of identifying genes that play key roles during callus formation. As the result, we detected a total of 27,639 alterations in gene expression during callus formation, which could be categorized into eight distinct clusters. Gene ontology analysis revealed that genes associated with hormones, cell wall modification, and cell cycle underwent transcriptional reprogramming throughout callus formation. Furthermore, by scrutinizing the expression patterns of genes related to hormones, cell cycle, cell wall, and transcription factors, we discovered that auxin, cytokinin, and brassinosteroid signaling pathways activate genes involved in both root and shoot meristem development during callus formation. In summary, our transcriptome analysis provides significant insights into the molecular mechanisms governing callus formation in soybean. The information obtained from this study contributes to a deeper understanding of this intricate process and paves the way for further investigation in the field.

Effects of Helicobacter pylori Infection on the Development of Chronic Gastritis

BACKGROUND/AIMS

Based on the gene expression profiles of gastric epithelial tissue at different stages of Helicobacter pylori-infected gastritis, key long noncoding RNAs and genes in the development of Helicobacter pylori infection-induced gastritis were screened to provide a basis for early diagnosis and treatment.

MATERIALS AND METHODS

We downloaded 2 sets of sample data from the database, including gastric epithelial tissue samples from gastritis patients from Bhutan and Dominican, and screened mRNAs in the differentially expressed RNAs of the 2 regions. Mfuzz clustering algorithm was used to screen RNAs related to the 3 stages of chronic gastritis. The competing endogenous RNA (ceRNA) regulation network was constructed, and the selected key RNAs were verified. Samples from Bhutan and Dominican were subdivided into the chronic gastritis/ normal comparison groups, and the differentially expressed RNAs were screened to obtain 1067 overlapping RNAs, containing 21 long noncoding RNAs and 1046 mRNAs.

RESULTS

Thirty-eight significant gene ontology functional nodes and 6 expression pattern clusters were obtained. Two ceRNA regulatory networks were constructed, and 4 shared miRNAs (hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-155-5p) were obtained. Eleven important long noncoding RNAs (AFAP1-AS1, MIR155HG, LINC00472, and FAM201A) and mRNAs (CASP10, SLC26A2, TRIB1, BMP2K, SCAMP1, TNKS1BP1, and MBOAT2) regulated by these 4 miRNAs were obtained. These results indicated that Helicobacter pylori infection had a certain influence on the development of gastritis.

CONCLUSIONS

The 11 key RNAs can provide a target for the early diagnosis and treatment of chronic gastritis following Helicobacter pylori infection.

Bioinformatics analysis identifies potential ferroptosis key genes in the pathogenesis of diabetic peripheral neuropathy

Background

Diabetic peripheral neuropathy (DPN) is a serious complication in Diabetes Mellitus (DM) patients and the underlying mechanism is yet unclear. Ferroptosis has been recently intensively researched as a key process in the pathogenesis of diabetes but there yet has been no related bioinformatics-based studies in the context of DPN.

Methods

We used data mining and data analysis techniques to screen differentially expressed genes (DEGs) and immune cell content in patients with DPN, DM patients and healthy participants (dataset GSE95849). These DEGs were then intersected with the ferroptosis dataset (FerrDb) to obtain ferroptosis DEGs and the associated key molecules and miRNAs interactions were predicted.

Results

A total of 33 ferroptosis DEGs were obtained. Functional pathway enrichment analysis revealed 127 significantly related biological processes, 10 cellular components, 3 molecular functions and 30 KEGG signal pathways. The biological processes that were significantly enriched were in response to extracellular stimulus and oxidative stress. Key modules constructed by the protein-protein interaction network analysis led to the confirmation of the following genes of interest: DCAF7, GABARAPL1, ACSL4, SESN2 and RB1. Further miRNA interaction prediction revealed the possible involvement of miRNAs such as miR108b-8p, miR34a-5p, mir15b-5p, miR-5838-5p, miR-192-5p, miR-222-3p and miR-23c. Immune-environment content of samples between DM and DPN patients revealed significant difference in the levels of endothelial cells and fibroblasts, which further speculates their possible involvement in the pathogenesis of DPN.

Conclusion

Our findings could provide insight for investigations about the role of ferroptosis in the development of DPN.

Screening of feature genes related to immune and inflammatory responses in periodontitis

BACKGROUND

Immune and inflammatory responses are important in the occurrence and development of periodontitis. The aim of this study was to screen for immune-related genes and construct a disease diagnostic model to further investigate the underlying molecular mechanisms of periodontitis.

METHODS

GSE16134 and GSE10334 datasets were used in this study. Differentially expressed genes (DEGs) between the periodontitis and control groups were selected. Immune-related genes were identified, and functional analysis and construction of an interaction network were conducted. Immune characteristics were evaluated using gene set variation analysis GSVA. Immunity-related modules were analyzed using weighted gene co-expression network analysis (WGCNA). The LASSO algorithm was applied to optimize the module genes. Correlation between optimized immune-related DEGs and immune cells was analyzed.

RESULTS

A total of 324 immune-related DEGs enriched in immune- and inflammation-related functions and pathways were identified. Of which, 23 immune cells were significantly different between the periodontitis and control groups. Nine optimal immune-related genes were selected using the WGCNA and LASSO algorithms to construct a diagnostic model. Except for CXCL1, the other eight genes were significantly positively correlated with regulatory T cells, immature B cells, activated B cells, and myeloid-derived suppressor cells.

CONCLUSION

This study identified nine immune-related genes and developed a diagnostic model for periodontitis.

Metabolic-related gene signatures for survival prediction and immune cell subtypes associated with prognosis in intrahepatic cholangiocarcinoma

OBJECTIVES

Our study aimed to reveal the metabolic-related gene signatures for survival prediction and immune cell subtypes associated with IHCC prognosis.

METHODS

Differentially expressed metabolic genes were identified between survival group and dead group which were divided according to survival at discharge. Recursive feature elimination (RFE) and randomForest (RF) algorithms were applied to optimize the combination of feature metabolic genes, which were used to generate SVM classifier. Performance of SVM classifier was evaluated by receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was conducted to uncover the activated pathways in high risk group, and differences in immune cell distributions were revealed.

RESULTS

There were 143 differentially expressed metabolic gens. RFE and RF identified 21 overlapping differentially expressed metabolic genes, and the constructed SVM classifier had excellent accuracy in training and validation dataset. RS survival prediction model was consisted of 10 metabolic genes. RS model had reliable predictive capability in the training and validation dataset. GSEA revealed 15 significant KEGG pathways that were relatively activated in the high risk group. High risk group had obviously lower counts of B cell naive and T cell CD4+ memory resting, while higher counts of B cell plasma and macrophage M2.

CONCLUSION

Prognostic prediction model of 10 metabolic genes could accurately predict the prognosis of IHCC patients.

Analysis of transcriptome expression profiling data in oral leukoplakia and early and late‑stage oral squamous cell carcinoma

The present study screened, potential prognostic biomarkers for oral carcinogenesis. The GSE85195 dataset, which consisted of oral leukoplakia (OL) and early and late-stage oral squamous cell carcinoma (OSCC) samples, was used. The differentially expressed genes (DEGs) in early OSCC vs. OL, late OSCC vs. OL and late OSCC vs. early OSCC groups were screened using the limma package in R. The Short Time-series Expression Miner software package was used to cluster DEGs with similar expression patterns in the course of disease progression (from OL to early and then late-stage OSCC). Moreover, the Database for Annotation, Visualization and Integrated Discovery online analysis tool was used to perform Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Reverse transcription-quantitative PCR was performed to assess the mRNA expression levels of hub node genes in clinical samples, and receiver operating characteristic curve analysis was performed to assess the prognostic value of the hub genes. A total of 4,595, 6,042 and 2,738 DEGs were screened in the early OSCC vs. OL, late OSCC vs. OL and late OSCC vs. early OSCC groups, respectively. A total of 665 overlapping genes were identified when the screened DEGs were compared. Cluster 1 and cluster 7 were identified as the significant clusters, which contained 496 and 341 DEGs, respectively. A PPI network was constructed with 440 interaction pairs. There were five differentially expressed hub nodes identified in different stages from OL to OSCC. The results of the present study indicated that fibronectin 1, signal transducer and activator of transcription 1, collagen type II α1 chain, collagen type X α1 chain and collagen type IV α6 chain might serve as independent diagnostic factors for OL and OSCC, and as prognostic biomarkers for OL carcinogenesis.

Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma

This study aimed to investigate Hippo pathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas and European Bioinformatics Institute array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P < 0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic value with an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.

Integrated whole transcriptome analysis for the crucial regulators and functional pathways related to cardiac fibrosis in rats

BACKGROUND

Cardiac fibrosis has gradually gained significance in the field of cardiovascular disease; however, its specific pathogenesis remains unclear. This study aims to establish the regulatory networks based on whole-transcriptome RNA sequencing analyses and reveal the underlying mechanisms of cardiac fibrosis.

METHODS

An experimental model of myocardial fibrosis was induced using the chronic intermittent hypoxia (CIH) method. Expression profiles of long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) were acquired from right atrial tissue samples of rats. Differentially expressed RNAs (DERs) were identified, and functional enrichment analysis was performed. Moreover, a protein-protein interaction (PPI) network and competitive endogenous RNA (ceRNA) regulatory network that are related to cardiac fibrosis were constructed, and the relevant regulatory factors and functional pathways were identified. Finally, the crucial regulators were validated using qRT-PCR.

RESULTS

DERs, including 268 lncRNAs, 20 miRNAs, and 436 mRNAs, were screened. Further, 18 relevant biological processes, such as "chromosome segregation, " and 6 KEGG signaling pathways, such as "cell cycle, " were significantly enriched. The regulatory relationship of miRNA-mRNA-KEGG pathways showed eight overlapping disease pathways, including "pathways in cancer." In addition, crucial regulatory factors, such as Arnt2, WNT2B, GNG7, LOC100909750, Cyp1a1, E2F1, BIRC5, and LPAR4, were identified and verified to be closely related to cardiac fibrosis.

CONCLUSION

This study identified the crucial regulators and related functional pathways in cardiac fibrosis by integrating the whole transcriptome analysis in rats, which might provide novel insights into the pathogenesis of cardiac fibrosis.

Isolation of Meiocytes and Cytological Analyses of Male Meiotic Chromosomes in Soybean, Lettuce, and Maize

Meiosis is a specialized cell division that halves the number of chromosomes following a single round of DNA replication, thus leading to the generation of haploid gametes. It is essential for sexual reproduction in eukaryotes. Over the past several decades, with the well-developed molecular and cytogenetic methods, there have been great advances in understanding meiosis in plants such as Arabidopsis thaliana and maize, providing excellent references to study meiosis in other plants. A chapter in the previous edition described molecular cytological methods for studying Arabidopsis meiosis in detail. In this chapter, we focus on methods for studying meiosis in soybean (Glycine max), lettuce (Lactuca sativa), and maize (Zea mays). Moreover, we include the method that was recently developed for examination of epigenetic modifications, such as DNA methylation and histone modifications on meiotic chromosomes in plants.

Time-Series Expression Profile Analysis of Post-Traumatic Joint Contracture in Rats at the Early Stages of the Healing Process

Objective

This study aimed to characterize the gene expression profile at the early stages of the healing process of post-traumatic joint contracture (PTJC).

Methods

Twelve rats were used for PTJC model establishment and were divided into four groups according to the sampling time: S0d, S3d, S7d and S2w. Transcriptome sequencing was performed on fibrotic joint capsule samples in four groups followed by bioinformatics analyses including differentially expressed genes (DEGs) screening, Short Time-series Expression Miner (STEM) analysis, network construction, and pathway analysis. Five important genes were validated by qRT-PCR.

Results

A total of 1171, 1052 and 793 DEGs were screened in S3d vs S0d, S7d vs S0d, and S2w vs S0d comparison groups, respectively. A total of 383 overlapping genes were screened out, which were significantly enriched in some inflammatory functions and pathways. Through STEM analysis, three clusters were identified, including 105, 57 and 57 DEGs, respectively. Then, based on the cluster genes, 10 genes, such as Il6, Timp1, Cxcl1, Cxcr4 and Mmp3, were further selected after PPI and pathway analyses. The expression levels of Il6, Timp1, Cxcl1, Cxcr4 and Mmp3 were validated by qRT-PCR.

Conclusion

The present study screened out several genes with significant changes in expression levels at the early stages of the healing process in PTJC, such as Il6, Timp1, Cxcl1, Cxcr4 and Mmp3. Our study offers a valuable contribution to the understanding pathomechanism of PTJC.

Transcriptome profiling reveals the roles of pigment formation mechanisms in yellow Paeonia delavayi flowers

The yellow colour of ornamental varieties of tree peony originated from Paeonia delavayi. However, but P. delavayi and Paeonia suffruticosa belong to different subgroups, so hybridization is difficult and results in a long breeding cycle. However, no comprehensive transcriptomic profiling has focused on the colour formation mechanisms of yellow tree peony petals. Analysing the colour formation mechanism of yellow petals in P. delavayi is very important for directional molecular breeding. In this study, the transcriptional map of yellow pigment development in petals was used to analyse the mechanism of petal colour formation. We analysed the genes related to the metabolism of flavonoids and carotenoids and the transcription factors (TFs) involved in P. delavayi var. lutea (pure yellow individual) yellow pigment development using transcriptome sequence profiling. Transcriptome sequence profiles revealed three and four differentially expressed transcripts (DETs) involved in flavonoid biosynthesis and carotenoid biosynthesis, respectively. An analysis of DETs in the flavonoid pathway showed that chalcone synthase (CHS) and chalcone 2´-glucosyltransferases (THC2'GT) act in synergy to synthesize isosalipurposide (ISP). CHS and flavonol synthase (FLS) synergistically synthesize quercetin and kaempferol. DEG analysis of the carotenoid pathway revealed that phytoene synthase (PSY), carotenoid isomerase (CRTISO) and β-carotene hydroxylases (CHYB) play a key role in regulating lutein formation, and carotenoid cleavage dioxygenase (CCD) plays an important role in the degradation of carotenoids. These two pathways may be regulated by TF families such as bHLH, ARF, and MYB. The results of the transient overexpression of genes showed that CHS and CHI are regulated by PdMYB2. In this study, the molecular mechanism of ISP synthesis was analysed in depth, and the complete metabolic pathway of carotenoids in Paeonia L. was reported for the first time. By studying the formation mechanism of yellow pigment in P. delavayi petals, a breeding strategy for improving flavonol and carotenoid contents and reducing anthocyanin synthesis by genetic engineering was suggested.

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

BACKGROUND

To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).

METHODS

The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.

RESULTS

Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05).

CONCLUSION

Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine-cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

Development and validation of a prognostic 9-gene signature for colorectal cancer

Introduction

Colorectal cancer (CRC) is one of the most prevalent cancers globally with a high mortality rate. Predicting prognosis using disease progression and cancer pathologic stage is insufficient, and a prognostic factor that can accurately evaluate patient prognosis needs to be developed. In this study, we aimed to infer a prognostic gene signature to identify a functional signature associated with the prognosis of CRC patients.

Methods

First, we used univariate Cox regression, least absolute shrinkage and selection operator (lasso) regression, and multivariate Cox regression analyses to screen genes significantly associated with CRC patient prognosis, from colorectal cancer RNA sequencing data in The Cancer Genome Atlas (TCGA) database. We then calculated the risk score (RS) for each patient based on the expression of the nine candidate genes and developed a prognostic signature.

Results

Based on the optimal cut-off on the receiver operating characteristic (ROC) curve, patients were separated into high- and low-risk groups, and the difference in overall survival between the two groups was examined. Patients in the low-risk group had a better overall survival rate than those in the high-risk group. The results were validated using the GSE72970, GSE39582, and GSE17536 Gene Expression Omnibus (GEO) datasets, and the same conclusions were reached. ROC curve test of the RS signature also indicated that it had excellent accuracy. The RS signature was then compared with traditional clinical factors as a prognostic indicator, and we discovered that the RS signature had superior predictive ability.

Conclusion

The RS signature developed in this study has excellent predictive power for the prognosis of patients with CRC and broad applicability as a prognostic indicator for patients.

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

BACKGROUND

Pancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.

METHODS

We obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.

RESULTS

Eight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.

CONCLUSIONS

An eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Prognostic signature related to the immune environment of oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) prognosis remains poor. Here we aimed to identify an effective prognostic signature for predicting the survival of patients with OSCC. Gene-expression and clinical data were obtained from the Cancer Genome Atlas database. Immune microenvironment-associated genes were identified using bioinformatics. Subtype and risk-score analyses were performed for these genes. Kaplan–Meier analysis and immune cell infiltration level were explored in different subtypes and risk-score groups. The prognostic ability, independent prognosis, and clinical features of the risk score were assessed. Furthermore, immunotherapy response based on the risk score was explored. Finally, a conjoint analysis of the subtype and risk-score groups was performed to determine the best prognostic combination. We found 11 potential prognostic genes and constructed a risk-score model. The subtype cluster 2 and a high-risk group showed the worst overall survival; differences in survival status might be due to the different immune cell infiltration levels. The risk score showed good performance, independent prognostic value, and valuable clinical application. Higher risk scores showed higher Tumor Immune Dysfunction and Exclusion scores, indicating that patients with a high-risk score were less likely to benefit from immunotherapy. Finally, conjoint analysis for the subgroups and risk groups showed the best predictive ability.

Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r2 = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern.

Transcriptome Meta-Analysis Identifies Candidate Hub Genes and Pathways of Pathogen Stress Responses in Arabidopsis thaliana

Simple Summary

Meta-analysis and systems-biology analysis revealed molecular plant defense responses in Arabidopsis thaliana when attacked by various pathogens. Differentially expressed genes were involved in several biosynthetic metabolic pathways, including those responsible for the biosynthesis of secondary metabolites and pathways central to photosynthesis and plant–pathogen interactions. In addition, WRKY40, WRKY46, and STZ transcription factors served as major points in protein–protein interactions. Overall, the findings highlighted genes that are commonly expressed during plant–pathogen interactions and will be useful in the development of novel genetic resistance strategies.

Abstract

Following a pathogen attack, plants defend themselves using multiple defense mechanisms to prevent infections. We used a meta-analysis and systems-biology analysis to search for general molecular plant defense responses from transcriptomic data reported from different pathogen attacks in Arabidopsis thaliana. Data from seven studies were subjected to meta-analysis, which revealed a total of 3694 differentially expressed genes (DEGs), where both healthy and infected plants were considered. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis further suggested that the DEGs were involved in several biosynthetic metabolic pathways, including those responsible for the biosynthesis of secondary metabolites and pathways central to photosynthesis and plant–pathogen interactions. Using network analysis, we highlight the importance of WRKY40, WRKY46 and STZ, and suggest that they serve as major points in protein–protein interactions. This is especially true regarding networks of composite-metabolic responses by pathogens. In summary, this research provides a new approach that illuminates how different mechanisms of transcriptome responses can be activated in plants under pathogen infection and indicates that common genes vary in their ability to regulate plant responses to the pathogens studied herein.

Discovery of immune-related diagnostic biomarkers and construction of diagnostic model in varies polycystic ovary syndrome

AIMS

The various diagnostic criteria for polycystic ovary syndrome (PCOS) raised problem for PCOS research worldwide. PCOS has been demonstrated to be significantly associated with immune response. We aimed to identify several immune-related biomarkers and construct a nomogram model for diagnosis in PCOS.

METHODS

The mRNA expression data were downloaded from Gene Expression Omnibus (GEO) database. Significant immune-related genes were identified to be the biomarkers for the diagnosis of PCOS using random forest model (RF), support vector machine model (SVM) and generalized linear model (GLM). The key biomarkers were selected from the optimal model and were utilized to construct a diagnostic nomogram. Receiver operating characteristic (ROC) curves was used to evaluate diagnostic ability of nomogram. Moreover, the relative proportion of 22 immune cell types was calculated by CIBERSORT algorithm.

RESULTS

Four immune-related biomarkers (cAMP, S100A9, TLR8 and IL6R) were demonstrated to be highly expressed in PCOS. The nomogram constructed on the ground of the four key biomarkers showed perfect performance in diagnosis of PCOS, whose AUC were greater than 0.7. Higher infiltrating abundance of neutrophils, resting NK cells and activated dendritic cells were observed in PCOS and were tightly associated with the four key biomarkers.

CONCLUSIONS

This study identified several immune-related diagnostic biomarkers for PCOS patients. The diagnostic nomogram constructed based the biomarkers provide a theory foundation for clinical application. Multiple immune cells were associated with the expression of these four biomarkers and might played a vital role in the procession of PCOS.

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Backgrounds

Pyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear.

Methods

Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs.

Results

A total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated.

Conclusion

PRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.

Characterization and function of biomarkers in sunitinib-resistant renal carcinoma cells

OBJECTIVE

Sunitinib is a first-line drug in the treatment of metastatic renal cell carcinoma, but patients will inevitably develop drug resistance after 6-15 months of systematic treatment, which seriously affects the prognosis in KIRC.

METHODS

During the study, the Gene Expression Omnibus (GEO) database was used to perform a systematic bioinformatics analysis,so that we could determine the genes (DEGs) which are differentially expressed between sunitinib-sensitive and sunitinib-resistant RCC (SRRC) cells.

RESULTS

A total of 31 DEGs were identified. Gene ontology (GO) was used to analyze the function of DEGS. These DEGs were found mainly enriched in organic aniontransmembrane transporter. The Cytohubba plug-in, STRING database and Cytoscape software were involved to construct a protein-protein interaction (PPI) network, and the pivot genes were identified by single-gene and multi-gene Cox regression analysis. Finally, DDX58 and MX2 were identified as prognostic genes. Survival analysis was performed by using prognostic nomogram, prognostic histogram and GEPIA database to verify the relationship between DDX58 and MX2 expression and survival. The relationship between the two pivot genes and the prognosis of patients was further verified by using the KM survival analyses and Time Dependency ROC curve analyses from TCGA database. Immunohistochemical analyses confirmed that, in tumor tissues and normal tissues, DDX58 and MX2 were differentially expressed. The expression of these two genes have relationship with the immune checkpoint.

CONCLUSIONS

This study provides insights into the molecular mechanisms of SRRC, as well as the selection of therapeutic and prognostic biomarkers for SRRC.

Construction of a circular RNA-based competing endogenous RNA network to screen biomarkers related to intervertebral disc degeneration

Background

Intervertebral disc degeneration (IDD) is a leading cause of disability with limited treatment strategies. A better understanding of the mechanism of IDD might enable less invasive and more targeted treatments. This study aimed to identify the circular RNA (circRNA)–microRNA (miRNA)–messenger RNA (mRNA) competing endogenous RNA (ceRNA) regulatory mechanisms in IDD.

Methods 

The GSE67567 microarray dataset was downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed circRNAs, miRNAs and mRNAs between IDD and controls were identified. A ceRNA network was constructed on the basis of the interaction between circRNAs and miRNAs, and miRNAs and mRNAs. Pathway enrichment analysis was performed on the mRNAs in the ceRNA network. Then, with ‘intervertebral disc degeneration’ as keywords, IDD-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were searched for in the Comparative Toxicogenomics Database.

Results

A total of 105 differentially expressed circRNAs, 84 miRNAs and 967 mRNAs were identified. After analysis, 86 circRNA–miRNA, and 126 miRNA–mRNA regulatory relationship pairs were obtained to construct a ceRNA network. The mRNAs were enriched in six KEGG signalling pathways, and four were associated with IDD: the hsa04350: TGF-beta signalling pathway, hsa04068: FoxO signalling pathway, hsa05142: Chagas disease (American trypanosomiasis) and hsa04380: Osteoclast differentiation. An IDD-related ceRNA network was constructed involving four circRNAs, three miRNAs and 11 mRNAs. Auxiliary validation showed that the expression levels of miR-185-5p, miR-486-5p, ACVR1B, FOXO1, SMAD2 and TGFB1 were consistent in different databases.

Conclusions

Our study identified some circRNA–miRNA–mRNA interaction axes potentially associated with the progression of IDD, viz.: circRNA_100086–miR-509-3p–MAPK1, circRNA_000200–miR-185-5p–TGFB1, circRNA_104308–miR-185-5p–TGFB1, circRNA_400090–miR-486-5p–FOXO1 and circRNA_400090–miR-486-5p–SMAD2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05579-0.

1. An IDD-related ceRNA network involving four circRNAs, three miRNAs and 11 mRNAs was constructed.

2. The expression levels of miR-185-5p, miR-486-5p, FOXO1, SMAD2 and TGFB1 were consistent in different databases.

3. Our study identified IDD-related circRNA–miRNA–mRNA interaction axes, including circRNA_100086–miR-509-3p–MAPK1.

Identification of potential gene markers in gestational diabetes mellitus

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub‐modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real‐time polymerase chain reaction (qRT‐PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up‐regulated and 684 down‐regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune‐related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down‐regulated HLA‐DQA1, HLA‐DRB1, HLA‐DPA1, and HLA‐DQB1 served as hub genes. In addition, six significant sub‐clusters were extracted and functional analysis suggested that these four genes in sub‐module 1 were also associated with immune and T1DM‐related pathways. Finally, they were also confirmed by qRT‐PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

Analysis of Whole-Transcriptome RNA-Seq Data Reveals the Involvement of Alternative Splicing in the Drought Response of Glycyrrhiza uralensis

Alternative splicing (AS) is a post-transcriptional regulatory mechanism that increases protein diversity. There is growing evidence that AS plays an important role in regulating plant stress responses. However, the mechanism by which AS coordinates with transcriptional regulation to regulate the drought response in Glycyrrhiza uralensis remains unclear. In this study, we performed a genome-wide analysis of AS events in G. uralensis at different time points under drought stress using a high-throughput RNA sequencing approach. We detected 2,479 and 2,764 AS events in the aerial parts (AP) and underground parts (UP), respectively, of drought-stressed G. uralensis. Of these, last exon AS and exon skipping were the main types of AS. Overall, 2,653 genes undergoing significant AS regulation were identified from the AP and UP of G. uralensis exposed to drought for 2, 6, 12, and 24 h. Gene Ontology analyses indicated that AS plays an important role in the regulation of nitrogen and protein metabolism in the drought response of G. uralensis. Notably, the spliceosomal pathway and basal transcription factor pathway were significantly enriched with differentially spliced genes under drought stress. Genes related to splicing regulators in the AP and UP of G. uralensis responded to drought stress and underwent AS under drought conditions. In summary, our data suggest that drought-responsive AS directly and indirectly regulates the drought response of G. uralensis. Further in-depth studies on the functions and mechanisms of AS during abiotic stresses will provide new strategies for improving plant stress resistance.

Identification of Key Target Genes and Pathway Analysis in Nonalcoholic Fatty Liver Disease Via Integrated Bioinformatics Analysis

Purpose

This study aimed at exploring the mechanisms underlying nonalcoholic fatty liver disease (NAFLD) and developing new diagnostic biomarkers for nonalcoholic steatohepatitis (NASH).

Methods

The microarray dataset GES83452 was downloaded from the NCBI-GEO database, and the differentially expressed RNAs (DERs) were screened between the NAFLD and non-NAFLD samples of the baseline and 1-year follow-up time point group based on the Limma package.

Results

A total of 561 DERs (268 downregulated and 293 upregulated) were screened in the baseline time point group, and 1163 DERs (522 downregulated and 641 upregulated) were screened in the 1-year follow-up time point group. A total of 74 lncRNA-miRNA pairs and 523 miRNA-mRNA pairs were obtained in order to construct a lncRNA-miRNA-mRNA regulatory network. Subsequently, functional enrichment analysis revealed 28 GO and 9 KEGG pathways in the ceRNA regulatory network. LEPR and CXCL10 are involved in the Cytokine-cytokine receptor interaction (P = 1.86E-02), and the FOXO1 is involved in both the insulin signaling pathway (P = 1.79E-02) and the pathways in cancer (P = 2.87E-02).

Conclusion

LEPR, CXCL10, and FOXO1 were the characteristic target genes for NAFLD.

Comprehensive circular RNA expression profiling with associated ceRNA network in orbital venous malformation

Purpose

Orbital venous malformation (OVM), the most common type of vascular malformation in adults, has a great impact on both visual and cosmetic factors. Circular RNAs (circRNAs) play important roles in various ophthalmological diseases; however, little is known about their function in the pathogenesis of OVM.

Methods

We obtained differentially expressed circRNAs, mRNAs, and miRNAs based on RNA sequencing of four OVM tissues and four normal orbital vascular tissues. The circRNA-mRNA coexpression network and circRNA-miRNA-mRNA and competing endogenous RNA (ceRNA) networks were constructed using miRanda software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the up- and downregulated mRNAs in the circRNA-miRNA-mRNA ceRNA network.

Results

Overall, we identified 45 upregulated and 144 downregulated circRNAs, as well as 2,175 upregulated and 1,274 downregulated mRNAs and 156 upregulated and 168 downregulated miRNAs in OVM samples compared with normal orbital vascular tissues. The expression changes of mRNAs and circRNAs detected by quantitative real-time PCR (qRT-PCR) were in line with the RNA-seq results. Then, a ceRNA regulatory network was constructed with these differentially expressed circRNAs, mRNAs, and miRNAs. GO functional analysis revealed that most related biological processes involved extracellular matrix organization, positive regulation of actin nucleation, and so on, which were thought to be involved in the evolution of OVM. KEGG pathway analysis of upregulated mRNAs showed that mucin-type O-glycan biosynthesis, glycosaminoglycan degradation, and the PI3K (Gene ID: 5290; OMIM: 613089)-AKT (Gene ID: 207; OMIM: 114500) signaling pathway were all enriched in OVM samples.

Conclusions

Our study provides novel insight into the regulatory mechanism of circRNAs, miRNAs, and mRNAs in the pathogenesis of OVM.

Identification and validation of an eight-lncRNA signature that predicts prognosis in patients with esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is correlated with worse clinical prognosis and lacks available targeted therapy. Thus, identification of reliable biomarkers is required for the diagnosis and treatment of ESCC.

Methods

We downloaded the GSE53625 dataset as a training dataset to screen differentially expressed RNAs (DERs) with the criterion of false discovery rate (FDR) < 0.05 and |log₂fold change (FC)| > 1. A support vector machine classifier was used to find the optimal feature gene set that could conclusively distinguish different samples. An eight-lncRNA signature was identified by random survival forest algorithm and multivariate Cox regression analysis. The RNA sequencing data from The Cancer Genome Atlas (TCGA) database were used for external validation. The predictive value of the signature was assessed using Kaplan–Meier test, time-dependent receiver operating characteristic (ROC) curves, and dynamic area under the curve (AUC). Furthermore, a nomogram to predict patients’ 3-year and 5-year prognosis was constructed. CCK-8 assay, flow cytometry, and transwell assay were conducted in ESCC cells.

Results

A total of 1136 DERs, including 689 downregulated mRNAs, 318 upregulated mRNAs, 74 downregulated lncRNAs and 55 upregulated lncRNAs, were obtained in the GES53625 dataset. From the training dataset, we identified an eight-lncRNA signature, (ADAMTS9-AS1, DLX6-AS1, LINC00470, LINC00520, LINC01497, LINC01749, MAMDC2-AS1, and SSTR5-AS1). A nomogram based on the eight-lncRNA signature, age, and pathologic stage was developed and showed good accuracy for predicting 3-year and 5-year survival probability of patients with ESCC. Functionally, knockdown of LINC00470 significantly suppressed cell proliferation, G1/S transition, and migration in two ESCC cell lines (EC9706 and TE-9). Moreover, knockdown of LINC00470 downregulated the protein levels of PCNA, CDK4, and N-cadherin, while upregulating E-cadherin protein level in EC9706 and TE-9 cells.

Conclusion

Our eight-lncRNA signature and nomogram can provide theoretical guidance for further research on the molecular mechanism of ESCC and the screening of molecular markers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-022-00331-x.

Molecular Characteristics of m6A Regulators and Tumor Microenvironment Infiltration in Soft Tissue Sarcoma: A Gene-Based Study

Background: N6-methyladenosine (m6A) methylation played a key role in tumor growth. However, the relationship between m6A and soft tissue sarcoma (STS) was still unclear.

Methods: The characterization and patterns of m6A modification in STS (TCGA-SARC and GSE17674) were analyzed comprehensively through bioinformatics and real-time polymerase chain reaction (RT-PCR). The effects of different m6A modification patterns on prognosis and immune infiltration of STS were further explored. Differentially expressed gene (DEG) analysis was performed. Moreover, an m6Ascore was constructed by principal component analysis (PCA). In addition, two immunotherapy datasets (IMvigor210 and GSE78220) and a sarcoma dataset (GSE17618) were used to evaluate the m6Ascore.

Results: Huge differences were found in somatic mutation, CNV, and expression of 25 m6A regulators in STS. Two modification patterns (A and B) in STS were further identified and the m6A cluster A showed a better clinical outcome with a lower immune/stromal score compared with the m6A cluster B (p < 0.050).In addition to , most STS samples from m6A cluster A showed a high m6Ascore, which was related to mismatch repair and a better prognosis of STS (p < 0.001). In contrast, the m6A cluster B, characterized by a low m6Ascore, was related to the MYC signaling pathway, which led to a poor prognosis of STS. A high m6Ascore also contributed to a better outcome of PD-1/PD-L1 blockade immunotherapy.

Conclusion: The modification patterns of 25 m6A regulators in the STS microenvironment were explored comprehensively. The novel m6Ascore effectively predicted the characteristics of the tumor microenvironment (TME) and outcome in STS and provided novel insights for future immunotherapy.

Development of a prognostic metabolic signature in stomach adenocarcinoma

PURPOSE

The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD.

METHODS

An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping.

RESULTS

A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA.

CONCLUSION

We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.

Genome-Wide Profiling of Alternative Splicing and Gene Fusion during Rice Black-Streaked Dwarf Virus Stress in Maize (Zea mays L.)

Rice black-streaked dwarf virus (RBSDV) causes maize rough dwarf disease (MRDD), which is a viral disease that significantly affects maize yields worldwide. Plants tolerate stress through transcriptional reprogramming at the alternative splicing (AS), transcriptional, and fusion gene (FG) levels. However, it is unclear whether and how AS and FG interfere with transcriptional reprogramming in MRDD. In this study, we performed global profiling of AS and FG on maize response to RBSDV and compared it with transcriptional changes. There are approximately 1.43 to 2.25 AS events per gene in maize infected with RBSDV. GRMZM2G438622 was only detected in four AS modes (A3SS, A5SS, RI, and SE), whereas GRMZM2G059392 showed downregulated expression and four AS events. A total of 106 and 176 FGs were detected at two time points, respectively, including six differentially expressed genes and five differentially spliced genes. The gene GRMZM2G076798 was the only FG that occurred at two time points and was involved in two FG events. Among these, 104 GOs were enriched, indicating that nodulin-, disease resistance-, and chloroplastic-related genes respond to RBSDV stress in maize. These results provide new insights into the mechanisms underlying post-transcriptional and transcriptional regulation of maize response to RBSDV stress.

Mining GEO and TCGA Database for Immune Microenvironment of Lung Squamous Cell Carcinoma Patients With or Without Chemotherapy

Background

Chemotherapy is the main treatment for patients with lung squamous cell carcinoma (LUSC). However, how chemotherapy affects their immune system is rarely reported. This study was aimed to compare the differences in the immune microenvironment of LUSC patients with or without chemotherapy.

Methods

A total of 494 LUSC samples were obtained from The Cancer Genome Atlas (TCGA) database. The immune cell infiltration was evaluated by the ssGSEA algorithm, and the tumor subtype was assayed by ConsensusClusterPlus. The differences in tumor mutation burden (TMB) and clinical information between the two types were then compared. Additionally, the differentially expressed genes (DEGs) between two types were analyzed and hub genes were validated in the GEO database.

Results

LSCC samples in TCGA were divided into three subtypes. Then, combining the tumor subtype and immune scores, the samples were divided into hot and cold tumors. Regardless of whether LUSC patients received chemotherapy, the survival of the hot tumor group was not significantly prolonged compared with that of the cold tumor group. For LUSC patients who received chemotherapy, the TMB value in hot tumor group was significantly higher. Total 501 DEGs were identified between two groups. The high expressions of hub genes CD19, CTLA4, FCGR3B, CD80, IL-10, etc. were also validated in the GSE37745 dataset.

Conclusion

Chemotherapy does not affect the survival and prognosis of LUSC patients, but it significantly increases the TMB value of patients with hot tumor. The DEGs, especially hub genes, such as CD19, CTLA4, and FCGR3B, may serve as biomarkers to distinguish cold and hot tumors in LUSC.