Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method

Deciphering the SOX4/MAPK1 regulatory axis: a phosphoproteomic insight into IQGAP1 phosphorylation and pancreatic Cancer progression

OBJECTIVE

This study aims to elucidate the functional role of IQGAP1 phosphorylation modification mediated by the SOX4/MAPK1 regulatory axis in developing pancreatic cancer through phosphoproteomics analysis.

METHODS

Proteomics and phosphoproteomics data of pancreatic cancer were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Differential analysis, kinase-substrate enrichment analysis (KSEA), and independent prognosis analysis were performed on these datasets. Subtype analysis of pancreatic cancer patients was conducted based on the expression of prognostic-related proteins, and the prognosis of different subtypes was evaluated through prognosis analysis. Differential analysis of proteins in different subtypes was performed to identify differential proteins in the high-risk subtype. Clinical correlation analysis was conducted based on the expression of prognostic-related proteins, pancreatic cancer typing results, and clinical characteristics in the pancreatic cancer proteomics dataset. Functional pathway enrichment analysis was performed using GSEA/GO/KEGG, and most module proteins correlated with pancreatic cancer were selected using WGCNA analysis. In cell experiments, pancreatic cancer cells were grouped, and the expression levels of SOX4, MAPK1, and the phosphorylation level of IQGAP1 were detected by RT-qPCR and Western blot experiments. The effect of SOX4 on MAPK1 promoter transcriptional activity was assessed using a dual-luciferase assay, and the enrichment of SOX4 on the MAPK1 promoter was examined using a ChIP assay. The proliferation, migration, and invasion functions of grouped pancreatic cancer cells were assessed using CCK-8, colony formation, and Transwell assays. In animal experiments, the impact of SOX4 on tumor growth and metastasis through the regulation of MAPK1-IQGAP1 phosphorylation modification was studied by constructing subcutaneous and orthotopic pancreatic cancer xenograft models, as well as a liver metastasis model in nude mice.

RESULTS

Phosphoproteomics and proteomics data analysis revealed that the kinase MAPK1 may play an important role in pancreatic cancer progression by promoting IQGAP1 phosphorylation modification. Proteomics analysis classified pancreatic cancer patients into two subtypes, C1 and C2, where the high-risk C2 subtype was associated with poor prognosis, malignant tumor typing, and enriched tumor-related pathways. SOX4 may promote the occurrence of the high-risk C2 subtype of pancreatic cancer by regulating MAPK1-IQGAP1 phosphorylation modification. In vitro cell experiments confirmed that SOX4 promoted IQGAP1 phosphorylation modification by activating MAPK1 transcription while silencing SOX4 inhibited the proliferation, migration, and invasion of pancreatic cancer cells by reducing the phosphorylation level of MAPK1-IQGAP1. In vivo, animal experiments further confirmed that silencing SOX4 suppressed the growth and metastasis of pancreatic cancer by reducing the phosphorylation level of MAPK1-IQGAP1.

CONCLUSION

The findings of this study suggest that SOX4 promotes the phosphorylation modification of IQGAP1 by activating MAPK1 transcription, thereby facilitating the growth and metastasis of pancreatic cancer.

Melatonin alleviates endoplasmic reticulum stress to improve ovarian function by regulating the mTOR pathway in aged laying hens

Granular cell apoptosis is a key factor leading to follicular atresia and decreased laying rate in aged laying hens. Endoplasmic reticulum stress (ERS) induced cell apoptosis is a new type of apoptosis pathway. Previous studies have shown that the ERS pathway is involved in the regulation of follicular development and atresia, and can be regulated by mTOR. Melatonin (MEL) can protect the normal development of follicles, but the precise mechanism by which MEL regulates follicular development is not yet clear. So, we investigated the potential relationship between MEL and ERS and mTOR signaling pathway in vivo through intraperitoneal injection of MEL in aged laying hens. The results show that the laying rate, ovarian follicle number, plasma MEL, E2, LH, FSH concentrations, as well as the mRNA expression of mTOR signaling-associated genes TSC1, TSC2, mTOR, 4E-BP1, and S6K in old later-period chicken control (Old-CN) group was significantly decreased (P < 0.01). In contrast, the ERS-related of plasma and granular cell layer mRNA expression of Grp78, CHOP, and Caspase-3 was significantly increased (P < 0.01). While both of the effects were reversed by MEL. Then, aging granulosa cells were treated with MEL in vitro, followed by RNA seq analysis, and it was found that 259 and 322 genes were upregulated and downregulated. After performing GO enrichment analysis, it was found that DEGs significantly contribute to the biological processes including cell growth and apoptosis. Using pathway enrichment analysis, we found significant overrepresentation of cellular processes related to mTOR signaling and endoplasmic reticulum (ER) stress, involving genes such as GRB10, SGK1, PRKCA, RPS6KA2, RAF1, PIK3R3, FOXO1, DERL3, HMOX1, TLR7, VAMP7 and INSIG2. The obtained results of RT-PCR showed consistency with the RNA-Seq data. In summary, the underlined results revealed that MEL has significantly contributed to follicular development via activating the mTOR signaling pathway-related genes and alleviating ERS-related genes in laying hens. The current study provides a theoretical background for enhancing the egg-laying capability of hens and also providing a basis for elucidating the molecular mechanism of follicular selection.

Low-density lipoprotein receptor-related protein 5/6 promotes endometrial cancer progression and cancer cell immune escape

The study investigated the potential association of the low-density lipoprotein (LDL) genome with endometrial cancer progression based on the Gene Expression Omnibus data set and The Cancer Genome Atlas data set. Differential and weighted gene coexpression network analysis was performed on endometrial cancer transcriptome datasets GSE9750 and GSE106191. The protein-protein interaction network was built using LDL-receptor proteins and the top 50 tumor-associated genes. Low-density lipoprotein-related receptors 5/6 (LRP5/6) in endometrial cancer tissues were correlated with oncogenes, cell cycle-related genes, and immunological checkpoints using Spearman correlation. MethPrimer predicted the LRP5/6 promoter CpG island. LRP2, LRP6, LRP8, LRP12, low-density lipoprotein receptor-related protein-associated protein, and LRP5 were major LDL-receptor-related genes associated with endometrial cancer. LRP5/6 was enriched in various cancer-related pathways and may be a key LDL-receptor-related gene in cancer progression. LRP5/6 may be involved in the proliferation process of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may be involved in the proliferation of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may promote the immune escape of cancer cells by promoting the expression of immune checkpoints, promoting endometrial cancer progression. The MethPrimer database predicted that the LRP5/6 promoter region contained many CpG islands, suggesting that DNA methylation can occur in the LRP5/6 promoter region. LRP5/6 may aggravate endometrial cancer by activating the phosphoinositide 3-kinase/protein kinase B pathway.

Integrated analysis of miRNA, transcriptome, and degradome sequencing provides new insights into lipid metabolism in perilla seed

MicroRNAs (miRNA) are small noncoding RNAs that play a crucial as molecular regulators in lipid metabolism in various oil crops. Perilla (Perilla frutescens) is a specific oil crop known for its high alpha-linolenic acid (C18:3n3, ALA) content (>65 %) in their seed oils. In view of the regulatory mechanism of miRNAs in perilla remains unclear, we conducted miRNAs and transcriptome sequencing in two cultivars with distinct lipid compositions. A total of 525 unique miRNAs, including 142 differentially expressed miRNAs was identified in perilla seeds. The 318 miRNAs targeted 7,761 genes. Furthermore, we identified 112 regulated miRNAs and their 610 target genes involved in lipid metabolism. MiR159b and miR167a as the core nodes to regulate the expression of genes in oil biosynthesis (e.g., KAS, FATB, GPAT, FAD, DGK, LPAAT) and key regulatory TFs (e.g., MYB, ARF, DOF, SPL, NAC, TCP, and bHLH). The 1,219 miRNA-mRNA regulation modules were confirmed through degradome sequencing. Notably, pf-miR159b-MYBs and pf-miR167a-ARFs regulation modules were confirmed. They exhibited significantly different expression levels in two cultivars and believed to play important roles in oil biosynthesis in perilla seeds. This provides valuable insights into the functional analysis of miRNA-regulated lipid metabolism in perilla seeds.

Toxicity of ionic liquids against earthworms (Eisenia fetida)

Ionic liquids (ILs) are widely used in frontier fields because of their highly tunable properties. Although ILs may have adverse effects on organisms, few studies have focused on their effect on earthworm gene expression. Herein we investigated the toxicity mechanism of different ILs towards Eisenia fetida using transcriptomics. Earthworms were exposed to soil containing different concentrations and types of ILs, and behavior, weight, enzymatic activity and transcriptome were analyzed. Earthworms exhibited avoidance behavior towards ILs and growth was inhibited. ILs also affected antioxidant and detoxifying enzymatic activity. These effects were concentration and alkyl chain length-dependent. Analysis of intrasample expression levels and differences in transcriptome expression levels showed good parallelism within groups and large differences between groups. Based on functional classification analysis, we speculate that toxicity mainly occurs through translation and modification of proteins and intracellular transport functions, which affect protein-related binding functions and catalytic activity. KEGG pathway analysis revealed that ILs may damage the digestive system of earthworms, among other possible pathological effects. Transcriptome analysis reveals mechanisms that cannot be observed by conventional toxicity endpoints. This is useful to evaluate the potential environmental adverse effects of the industrial use of ILs.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods

Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.

miRNAs from Plasma Extracellular Vesicles Are Signatory Noninvasive Prognostic Biomarkers against Atherosclerosis in LDLr-/-Mice

Circular microRNAs (miRNAs) have become central in pathophysiological conditions of atherosclerosis (AS). However, the biomarkers for diagnosis and therapeutics against AS are still unclear. The atherosclerosis models in low-density lipoprotein receptor deficiency (LDLr^−/−) mice were established with a high-fat diet (HFD). The extraction kit isolated extracellular vesicles from plasma. Total RNAs were extracted from LDLr^−/− mice in plasma extracellular vesicles. Significantly varying miRNAs were detected by employing Illumina HiSeq 2000 deep sequencing technology. Target gene predictions of miRNAs were employed by related software that include RNAhybrid, TargetScan, miRanda, and PITA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) further analyzed the intersection points of predicted results. The results showed that the HFD group gradually formed atherosclerotic plaques in thoracic aorta compared with the control group. Out of 17, 8 upregulated and 9 downregulated miRNAs with a significant difference were found in the plasma extracellular vesicles that were further cross-examined by sequencing and bioinformatics analysis. Focal adhesion and Ras signaling pathway were found to be the most closely related pathways through GO and KEGG pathway analyses. The 8 most differentially expressed up- and downregulated miRNAs were further ascertained by TaqMan-based qRT-PCR. TaqMan-based qRT-PCR and in situ hybridization further validated the most differentially expressed miRNAs (miR-378d, miR-181b-5p, miR-146a-5p, miR-421-3p, miR-350-3p, and miR-184-3p) that were consistent with deep sequencing analysis suggesting a promising potential of utility to serve as diagnostic biomarkers against AS. The study gives a comprehensive profile of circular miRNAs in atherosclerosis and may pave the way for identifying biomarkers and novel targets for atherosclerosis.

Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features

Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.

Transcriptome analyses of potential regulators of pre- and post-ovulatory follicles in the pigeon (Columba livia)

To identify the dominant genes controlling follicular maturation, ovulation and regression for pigeon, we used RNA-seq to explore the gene expression profiles of pre- and post-ovulatory follicles of pigeon. We obtained total of 4.73million (96% of the raw data) high-quality clean reads, which could be aligned with 20282 genes. Gene expression profile analysis identified 1461 differentially expressed genes (DEGs) between the pre- (P4) and post-ovulatory follicles (P5). Of these, 843 genes were upregulated, and 618 genes were down-regulated. Furthermore, many DEGs were significantly enriched in some pathways closely related to follicle maturation, ovulation and regression, such as ECM-receptor interaction, vascular smooth muscle contraction, progesterone-mediated oocyte maturation, phagosome. Importantly, the DGEs in ECM-receptor interaction pathway included COL1A1 , COL1A2 , COL4A1 , COL4A2 , ITGA11 , ITGB3 and SDC3 , in the progesterone-mediated oocyte maturation pathway involved CDK1 , CDC25A , CCNB3 , CDC20 and Plk1 , and in the vascular smooth muscle contraction covered CALD1 , KCNMA1 , KCNMB1 , CACNA1 , ACTA2 , MYH10 , MYL3 , MYL6 , MYL9 , closely related to promoting follicular maturation and ovulation in pre-ovulatory follicles. Moreover, it seems that the lysosomal cathepsin family has a decisive role in the regression of early stage of post-ovulatory follicle. Taken together, these data enrich the research of molecular mechanisms of pigeon follicular activities at the transcriptional level and provide novel insight of breeding-related physiology for birds.

Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

The performance of aerobic granular sludge for simulated swine wastewater treatment and the removal mechanism of tetracycline

In this study, aerobic granular sludge (AGS) cultivated in a sequencing batch reactor (SBR) was employed to investigate its ability on the decontamination of tetracycline (TC) from swine wastewater (SWW). The removal mechanism of TC by AGS was studied. Results showed that the AGS process could effectively remove chemical oxygen demand (COD), ammonium nitrogen (NH_{+ 4}-N), total phosphorus (TP), and TC during operation. The removal of TC by AGS was mainly due to adsorption and biodegradation, and the contribution rate of biodegradation increased after AGS adaptation to TC. Twenty-two by-products were detected during biodegradation of TC, and accordingly the degradation pathway of TC was speculated. Compared to the control reactor, the microbe diversity in different levels of classification was richer in the TC fed reactor according to the LefSe analysis. The results revealed that enzymes that participated in the metabolic pathway of microbial biodegradation of polycyclic aromatic compounds were enriched and may have played a key role in the biodegradation of TC.

Identifying Discriminative Biological Function Features and Rules for Cancer-Related Long Non-coding RNAs

Cancer has been a major public health problem worldwide for many centuries. Cancer is a complex disease associated with accumulative genetic mutations, epigenetic aberrations, chromosomal instability, and expression alteration. Increasing lines of evidence suggest that many non-coding transcripts, which are termed as non-coding RNAs, have important regulatory roles in cancer. In particular, long non-coding RNAs (lncRNAs) play crucial roles in tumorigenesis. Cancer-related lncRNAs serve as oncogenic factors or tumor suppressors. Although many lncRNAs are identified as potential regulators in tumorigenesis by using traditional experimental methods, they are time consuming and expensive considering the tremendous amount of lncRNAs needed. Thus, effective and fast approaches to recognize tumor-related lncRNAs should be developed. The proposed approach should help us understand not only the mechanisms of lncRNAs that participate in tumorigenesis but also their satisfactory performance in distinguishing cancer-related lncRNAs. In this study, we utilized a decision tree (DT), a type of rule learning algorithm, to investigate cancer-related lncRNAs with functional annotation contents [gene ontology (GO) terms and KEGG pathways] of their co-expressed genes. Cancer-related and other lncRNAs encoded by the key enrichment features of GO and KEGG filtered by feature selection methods were used to build an informative DT, which further induced several decision rules. The rules provided not only a new tool for identifying cancer-related lncRNAs but also connected the lncRNAs and cancers with the combinations of GO terms. Results provided new directions for understanding cancer-related lncRNAs.

Four Immune-Related Long Non-coding RNAs for Prognosis Prediction in Patients With Hepatocellular Carcinoma

BACKGROUND

Long non-coding RNA (LncRNA) plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). This study aims to establish an immune-related LncRNA model for risk assessment and prognosis prediction in HCC patients.

METHODS

Hepatocellular carcinoma patient samples with complete clinical data and corresponding whole transcriptome expression were obtained from the Cancer Genome Atlas (TCGA). Immune-related genes were acquired from the Gene Set Enrichment Analysis (GSEA) website and matched with LncRNA in the TCGA to get immune-related LncRNA. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for screening the candidate LncRNAs and calculating the risk coefficient to establish the prognosis model. Patients were divided into a high-risk group and a low-risk group depending on the median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. GSEA and principal component analysis were used for function evaluation.

RESULTS

A total of 319 samples met the screening criteria and were randomly distributed across the training cohort and the validation cohort. After comparison with the IMMUNE_RESPONSE gene set and the IMMUNE_SYSTEM_PROCESS gene set, a total of 3094 immune-related LncRNAs were screened. Ultimately, four immune-related LncRNAs were used to construct a formula using LASSO regression. According to the formula, the low-risk group showed a higher survival rate than the high-risk group in the validation cohort and the whole cohort. The receiver operating characteristic curves data demonstrated that the risk score was more specific than other traditional clinical characteristics in predicting the 5-year survival rate for HCC.

CONCLUSION

The four-immune-related-LncRNA model can be used for survival prediction in HCC and guide clinical therapy.

A ceRNA network and a potential regulatory axis in gastric cancer with different degrees of immune cell infiltration

Immune cell infiltration is an important indicator of whether tumor patients will benefit from immunotherapy. Gastric cancer is one of the most common tumors in the world, and new indicators of immunotherapy are urgently needed. The aim of this study was to construct ceRNA networks in gastric cancer with different degrees of immune cell infiltration. We analyzed the expression profiles of different gastric cancer with different degrees of immune cell infiltration retrieved from The Cancer Genome Atlas (TCGA) database and found differentially expressed lncRNAs, mRNAs, and miRNAs. A ceRNA regulatory network of gastric cancer with different degrees of immune cell infiltration was constructed using functional annotation, RNA‐RNA interaction prediction, correlation analysis, survival analysis, and other comprehensive bioinformatics methods. The interaction and correlation between ceRNAs were verified using experiments on tumor tissues and cell lines. Cell line experiments showed a potential RP11‐1094M14.8/miR‐1269a/CXCL9 axis that was consistent with the ceRNA theory. qRT‐PCR results showed that RP11‐1094M14.8 knockdown significantly reduced the expression of CXCL9, and RP11‐1094M14.8 overexpression had the opposite effect. The results of clinical analysis of gastric cancer samples showed that RP11‐1094M14.8 and CXCL9 were highly expressed in hot tumors, and CXCL9 was positively correlated with a better prognosis for patients. The constructed novel ceRNA network and the potential regulatory axis may provide a comprehensive understanding of the potential mechanisms of development in gastric cancer with different degrees of immune cell infiltration. The RP11‐1094M14.8/miR‐1269a/CXCL9 axis may serve as a potential immune‐therapeutic target for gastric cancer with different degrees of immune cell infiltration.

Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches

BACKGROUND

About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques are tools that can improve the analysis and interpretation or extraction of knowledge from the data. These techniques may enhance the prognosis and diagnosis associated with reducing diseases such as T2DM. We applied four classification models, including K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and artificial neural networks (ANN) for diagnosing T2DM, and we compared the diagnostic power of these algorithms with each other. We performed the algorithms on six LncRNA variables (LINC00523, LINC00995, HCG27_201, TPT1-AS1, LY86-AS1, DKFZP) and demographic data.

RESULTS

To select the best performance, we considered the AUC, sensitivity, specificity, plotted the ROC curve, and showed the average curve and range. The mean AUC for the KNN algorithm was 91% with 0.09 standard deviation (SD); the mean sensitivity and specificity were 96 and 85%, respectively. After applying the SVM algorithm, the mean AUC obtained 95% after stratified 10-fold cross-validation, and the SD obtained 0.05. The mean sensitivity and specificity were 95 and 86%, respectively. The mean AUC for ANN and the SD were 93% and 0.03, also the mean sensitivity and specificity were 78 and 85%. At last, for the logistic regression algorithm, our results showed 95% of mean AUC, and the SD of 0.05, the mean sensitivity and specificity were 92 and 85%, respectively. According to the ROCs, the Logistic Regression and SVM had a better area under the curve compared to the others.

CONCLUSION

We aimed to find the best data mining approach for the prediction of T2DM using six lncRNA expression. According to the finding, the maximum AUC dedicated to SVM and logistic regression, among others, KNN and ANN also had the high mean AUC and small standard deviations of AUC scores among the approaches, KNN had the highest mean sensitivity and the highest specificity belonged to SVM. This study's result could improve our knowledge about the early detection and diagnosis of T2DM using the lncRNAs as biomarkers.

Identification of genes related to dexamethasone-induced immunosuppression in chicken thymus using transcriptome analysis

The molecular mechanism of stress-induced immunosuppression (SIS) in certain poultry immune organs is not completely clear. In this study, we constructed a stress immunosuppression model by selecting 180 healthy 7-day-old Gushi chickens and dividing them randomly into two groups: a D_T group and a B_T group. The D_T group was given dexamethasone, and the B_T group was given normal saline, according to the treatment method established and reported in our previous study. Thymus samples were subsequently taken from both groups. RNA-seq was used to sequence the transcriptomes of the thymus samples from both groups, and 1278 significant differentially expressed genes (DEGs) were obtained, of which 845 genes were up-regulated and 433 genes were down-regulated (padj<0.05, |FC| ≥ 2, FPKM>1). We identified immune-related gene ontology (GO) terms including immune system processes, immune system process regulation, and T cell activation. The results of KEGG (http: //www.kegg.jp) analysis showed that the DEGs are involved in a variety of immune-related pathways, such as cytokine-cytokine receptor interactions, Jak-STAT signaling pathways, and cell adhesion molecules (CAMs). The cytokine-cytokine receptor interaction pathway involves the DEGs CCR6, CCR5, CD40LG and FAS. The DEGs in the Jak-STAT signaling pathway were SPRY2, BCL2L1. These DEGS play an important role in cell apoptosis. CD40L, CD8, among other genes, are involved in the CAMs pathway. The results of this study add to existing data on the genomic study of stress affecting immune function, and provide a basis for further studies of the molecular mechanisms of stress-influenced immune function.

Microarray analysis of the expression profile of immune-related gene in rapid recurrence early-stage lung adenocarcinoma

Background

Although much progress has been made in the diagnosis of early-stage lung adenocarcinoma (ES-LUAD), the prognosis for ES-LUAD patients with rapid recurrence is still poor. Importantly, there is currently no effective and precise method to screen patients who may develop rapid recurrence. Therefore, it is necessary to identify potential differentially expressed genes (DEGs) in ES-LUAD patients with rapid recurrence and non-rapid recurrence.

Methods

Affymetrix GeneChip Human Transcriptome Array was used to identify DEGs between ES-LUAD patients with rapid recurrence and non-rapid recurrence. Rapid recurrence was defined as recurrence-free survival (RFS) ≦ 1 year and non-rapid recurrence was defined as RFS ≧ 3 years. The biological functions of the DEGs were analyzed by GO and KEGG pathway enrichment analyses. The protein–protein interaction (PPI) network of identified DEGs was conducted by STRING and Cytoscape software. The expression level of crucial hub genes and tumor-infiltrating lymphocytes (TILs) was verified by immunohistochemistry (IHC).

Results

A total of 416 DEGs were identified between ES-LUAD patients with and without rapid recurrence. The results of GO analysis revealed that 2 of the top 10 categories in the domain of cellular component, 2 of the top 10 in the domain of molecular function, and 9 of the top 10 in the domain of biological process were functionally related to immunity. The results of KEGG analysis showed that 6 of the top 8 pathways were functionally involved in immune regulation and inflammatory response. The PPI network analysis identified ten crucial nodal protein, including EGFR, MMP9, IL-1β, PTGS2, MMP1, and 5 histone proteins, which constituted 25 key interactions. IL-1β and PTGS2 expression were closely related to immunity and IHC analysis further revealed that low expression of IL-1β and PTGS2 is associated with rapid recurrence. Kaplan–Meier analysis further revealed that LUAD patients with lower IL-1β or PTGS2 expression had a worse RFS. When the TIL density of CD3⁺, CD4⁺, CD8⁺ and CD20⁺ subsets was less than 20%, ES-LUAD patients have a higher probability of rapid recurrence.

Conclusion

There were significant differences in the expression of immune-related genes between patients with rapid recurrence and patient with non-rapid recurrence. Immune-related genes such as IL-1β and PTGS2 and TIL density (20%) play important roles in rapid recurrence of ES-LUAD. This study provided a theoretical basis for distinguishing the two types of patients from an immunological perspective.

Electronic supplementary material

The online version of this article (10.1007/s00432-020-03287-7) contains supplementary material, which is available to authorized users.

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

iProEP: A Computational Predictor for Predicting Promoter

Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).