A 5-lncRNA signature predicts clinical prognosis and demonstrates a different mRNA expression in adult soft tissue sarcoma.

Objective: To study the construction of a prognostic model for hepatocellular carcinoma (HCC) based on pyroptosis-related genes (PRGs). Methods: HCC patient datasets were obtained from the Cancer Genome Atlas (TCGA) database, and a prognostic model was constructed by applying univariate Cox and least absolute shrinkages and selection operator (LASSO) regression analysis. According to the median risk score, HCC patients in the TCGA dataset were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox analysis, and nomograms were used to evaluate the predictive ability of the prognostic models. Functional enrichment analysis and immune infiltration analysis were performed on differentially expressed genes between the two groups. Finally, two HCC datasets (GSE76427 and GSE54236) from the Gene Expression Omnibus database were used to externally validate the prognostic value of the model. Univariate and multivariate Cox regression analysis or Wilcoxon tests were performed on the data. Results: A total of 366 HCC patients were included after screening the HCC patient dataset obtained from the TCGA database. A prognostic model related to HCC was established using univariate Cox regression analysis, LASSO regression analysis, and seven genes (CASP8, GPX4, GSDME, NLRC4, NLRP6, NOD2, and SCAF11). 366 cases were evenly divided into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival analysis showed that there were statistically significant differences in the survival time between patients in the high-risk and low-risk groups in the TCGA, GSE76427, and GSE54236 datasets (median overall survival time was 1 149 d vs. 2 131 d, 4.8 years vs. 6.3 years, and 20 months vs. 28 months, with P = 0.000 8, 0.034 0, and 0.0018, respectively). ROC curves showed good survival predictive value in both the TCGA dataset and two externally validated datasets. The areas under the ROC curves of 1, 2, and 3 years were 0.719, 0.65, and 0.657, respectively. Multivariate Cox regression analysis showed that the risk score of the prognostic model was an independent predictor of overall survival time in HCC patients. The risk model score accurately predicted the survival probability of HCC patients according to the established nomogram. Functional enrichment analysis and immune infiltration analysis showed that the immune status of the high-risk group was significantly decreased. Conclusion: The prognostic model constructed in this study based on seven PRGs accurately predicts the prognosis of HCC patients.

Background: Lung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients. Methods: lncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Results: A total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669. Conclusions: The present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.

BACKGROUNDCuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated.AIMTo identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC).METHODSUsing RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups.RESULTSThree hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluorouracil, gemcitabine, paclitaxel, imatinib, sunitinib, rapamycin, and XL-184 (cabozantinib), were found to be more efficacious in the low-risk group; erlotinib, an exception, was more efficacious in the high-risk group.CONCLUSIONThe lncRNA signature, CupRLSig, constructed in this study is valuable in prognostic estimation of HCC. Importantly, CupRLSig also predicts the level of immune infiltration and potential efficacy of tumor immunotherapy.

BackgroundLung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients.MethodslncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model.ResultsA total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669.ConclusionsThe present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.

BackgroundStomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD.MethodsThe RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson’s correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT–qPCR in a clinical cohort.ResultsA prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort.ConclusionOur study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.

In acute aristolochic acid nephropathy (AAN), aristolochic acid (AA) induces renal injury and tubulointerstitial fibrosis. However, the roles of microRNAs (miRNAs/miRs) and mRNAs involved in AAN are not clearly understood. The aim of the present study was to examine AA-induced genome-wide differentially expressed (DE) miRNAs and DE mRNAs using deep sequencing in mouse kidneys, and to analyze their regulatory networks. In the present self-controlled study, mice were treated with 5 mg/kg/day AA for 5 days, following unilateral nephrectomy. AA-induced renal injury and tubulointerstitial fibrosis were detected using hematoxylin and eosin staining and Masson's trichrome staining in the mouse kidneys. A total of 82 DE miRNAs and 4,605 DE mRNAs were identified between the AA-treated group and the self-control group. Of these DE miRNAs and mRNAs, some were validated using reverse transcription-quantitative PCR. Expression levels of the profibrotic miR-21, miR-433 and miR-132 families were significantly increased, whereas expression levels of the anti-fibrotic miR-122-5p and let-7a-1-3p were significantly decreased. Functions and signaling pathways associated with the DE miRNAs and mRNAs were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 767 DE pairs (in opposing directions) of miRNAs and their mRNA targets were identified. Among these, regulatory networks of miRNAs and mRNAs were analyzed using KEGG to identify enriched signaling pathways and extracellular matrix-associated pathways. In conclusion, the present study identified genome-wide DE miRNAs and mRNAs in the kidneys of AA-treated mice, as well as their regulatory pairs and signaling networks. The present results may improve the understanding of the role of DE miRNAs and their mRNA targets in the pathophysiology of acute AAN.

Long non-coding RNAs (lncRNAs) have been reported to be involved in multiple biological processes. However, the roles of lncRNAs in the reproduction of half-smooth tongue sole (Cynoglossus semilaevis) are unclear, especially in the molecular regulatory mechanism driving ovarian development and ovulation. Thus, to explore the mRNA and lncRNA mechanisms regulating reproduction, we collected tongue sole ovaries in three stages for RNA sequencing. In stage IV vs. V, we identified 312 differentially expressed (DE) mRNAs and 58 DE lncRNAs. In stage V vs. VI, we identified 1,059 DE mRNAs and 187 DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DE mRNAs were enriched in ECM-receptor interaction, oocyte meiosis and steroid hormone biosynthesis pathways. Furthermore, we carried out gene set enrichment analysis (GSEA) to identify potential reproduction related-pathways additionally, such as fatty metabolism and retinol metabolism. Based on enrichment analysis, DE mRNAs with a potential role in reproduction were selected and classified into six categories, including signal transduction, cell growth and death, immune response, metabolism, transport and catabolism, and cell junction. The interactions of DE lncRNAs and mRNAs were predicted according to antisense, cis-, and trans-regulatory mechanisms. We constructed a competing endogenous RNA (ceRNA) network. Several lncRNAs were predicted to regulate genes related to reproduction including cyp17a1, cyp19a1, mmp14, pgr, and hsd17b1. The functional enrichment analysis of these target genes of lncRNAs revealed that they were involved in several signaling pathways, such as the TGF-beta, Wnt signaling, and MAPK signaling pathways and reproduction related-pathways such as the progesterone-mediated oocyte maturation, oocyte meiosis, and GnRH signaling pathway. RT-qPCR analysis showed that two lncRNAs (XR_522278.2 and XR_522171.2) were mainly expressed in the ovary. Dual-fluorescence in situ hybridization experiments showed that both XR_522278.2 and XR_522171.2 colocalized with their target genes cyp17a1 and cyp19a1, respectively, in the follicular cell layer. The results further demonstrated that lncRNAs might be involved in the biological processes by modulating gene expression. Taken together, this study provides lncRNA profiles in the ovary of tongue sole and further insight into the role of lncRNA involvement in regulating reproduction in tongue sole.

BackgroundVentilator-induced diaphragmatic dysfunction (VIDD) is associated with weaning difficulties, intensive care unit hospitalization (ICU), infant mortality, and poor long-term clinical outcomes. The expression patterns of long noncoding RNAs (lncRNAs) and mRNAs in the diaphragm in a rat controlled mechanical ventilation (CMV) model, however, remain to be investigated.ResultsThe diaphragms of five male Wistar rats in a CMV group and five control Wistar rats were used to explore lncRNA and mRNA expression profiles by RNA-sequencing (RNA-seq). Muscle force measurements and immunofluorescence (IF) staining were used to verify the successful establishment of the CMV model. A total of 906 differentially expressed (DE) lncRNAs and 2,139 DE mRNAs were found in the CMV group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological functions or pathways of these DE mRNAs. Our results revealed that these DE mRNAs were related mainly related to complement and coagulation cascades, the PPAR signaling pathway, cholesterol metabolism, cytokine-cytokine receptor interaction, and the AMPK signaling pathway. Some DE lncRNAs and DE mRNAs determined by RNA-seq were validated by quantitative real-time polymerase chain reaction (qRT-PCR), which exhibited trends similar to those observed by RNA-sEq. Co-expression network analysis indicated that three selected muscle atrophy-related mRNAs (Myog, Trim63, and Fbxo32) were coexpressed with relatively newly discovered DE lncRNAs.ConclusionsThis study provides a novel perspective on the molecular mechanism of DE lncRNAs and mRNAs in a CMV model, and indicates that the inflammatory signaling pathway and lipid metabolism may play important roles in the pathophysiological mechanism and progression of VIDD.

Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct the risk model and assess whether the risk score was an independent prognostic factor for patients with OS. Gene Set Enrichment Analysis (GSEA) was performed to analyze the functions of genes in high-risk and low-risk groups. StarBase and Cytoscape were used to construct a competing endogenous RNA (ceRNA) network based on m6A-related prognostic lncRNA signature. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of genes involved in the ceRNA network. Results: We extracted 122 common lncRNAs from TCGA and Gene Expression Omnibus (GEO) databases. Pearson correlation results revealed 59 significant m6A-related lncRNAs in The Cancer Genome Atlas (TCGA) database, from which 2 were screened to construct a risk signature in TCGA dataset, which was then validated in the GEO dataset. A corresponding risk score was calculated and shown to be an independent prognostic factor for patients with OS. Enrichment analysis indicated that cell proliferation-related biological processes were more common in the high-risk group, while immune-related biological processes were more common in the low-risk group. Moreover, we established a nomogram that had a good ability to predict the overall survival of patients with OS. Additionally, a ceRNA network based on small nucleolar RNA host gene 7 (SNHG7) and small nucleolar RNA host gene 12 (SNHG12) was constructed, with genes that were enriched in hepatocellular carcinoma, gastric cancer, and non-small-cell lung cancer pathways. Conclusion: Our study revealed the prognostic role of m6A-related lncRNAs in OS and identified SNHG7 and SNHG12 as potential biomarkers for predicting the prognosis of patients with OS. These findings have enriched our understanding of the role of m6A modification in the dysregulation of lncRNAs in OS.

Molecular analysis facilitates the prediction of overall survival (OS) of breast cancer and decision-making of the treatment plan. The current study was designed to identify new prognostic genes for breast cancer and construct an effective prognostic signature with integrated bioinformatics analysis. Differentially expressed genes in breast cancer samples from The Cancer Genome Atlas (TCGA) dataset were filtered by univariate Cox regression analysis. The prognostic model was optimized by the Akaike information criterion and further validated using the TCGA dataset (n = 1014) and Gene Expression Omnibus (GEO) dataset (n = 307). The correlation between the risk score and clinical information was assessed by univariate and multivariate Cox regression analyses. Functional pathways in relation to high-risk and low-risk groups were analyzed using gene set enrichment analysis (GSEA). Four prognostic genes (EXOC6, GPC6, PCK2, and NFATC2) were screened and used to construct a prognostic model, which showed robust performance in classifying the high-risk and low-risk groups. The risk score was significantly related to clinical features and OS. We identified 19 functional pathways significantly associated with the risk score. This study constructed a new prognostic model with a high prediction performance for breast cancer. The four-gene prognostic signature could serve as an effective tool to predict prognosis and assist the management of breast cancer patients.

PurposePyroptosis plays an important role in tumor progression. However, there is no pyroptosis-associated long noncoding RNA (lncRNA) signature to predict the prognosis of patients with colorectal cancer (CRC).Materials and MethodsThe RNA sequencing data (RNA-seq) and corresponding clinical information relating to CRC patients were obtained from the Cancer Genome Atlas (TCGA) database and the GSE39582 dataset. Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature. Finally, gene set enrichment analysis (GSEA) was performed to explore the potential function of the lncRNA signature.ResultsWe constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1). CRC patients were subdivided into high- and low-risk groups based on median risk scores. The results of the K-M, PCA, and ROC analyses showed that this signature could accurately predict the prognosis of CRC patients. Univariate and multivariate Cox regression analyses showed that the pyroptosis-associated signature was an independent prognostic factor. Functional analysis suggested that tumor-associated pathways were enriched for in the high-risk CRC patient group.ConclusionOur study established an effective prognostic signature for CRC patients that may represent a potential therapeutic target.

BackgroundProstate cancer has a high incidence and a low 5-year survival rate. We aimed to combine cholesterol- and immune-related genes to screen prostate cancer prognosis-related genes and construct a prognostic risk model.MethodsWe obtained publicly released clinical data of prostate cancer through The Cancer Genome Atlas. Cholesterol- and immune-related genes were separately collected from the mSigDB and ImmPort databases. The prognostic model based on the immune-cholesterol-related differentially expressed mRNAs (DEmRNAs) network was constructed by univariate and multivariate Cox regression methods. Gene set enrichment analysis (GSEA), mutation landscape analysis, and immune infiltration analysis were carried out to investigate the role of immune-cholesterol-related DEmRNAs in prostate cancer.ResultsWe identified 11 immune-cholesterogenic-related DEmRNAs (C2orf88, TRPM4, SAPCD2, RHPN1, RAC3, APOF, PTGS2, TSPAN1, KLK4, ENTPD5, and C1orf64) as risk factors that were related to the occurrence and development of prostate cancer by bioinformatics analysis. Immune infiltration analysis suggested immune-cholesterol-related DEmRNAs may act in an immunomodulatory role for treatment decisions. The proportion of plasma cells, memory resting CD4 T cells, and neutrophils in the low-risk group was significantly higher than that in the high-risk group (p < 0.05). The GSEA revealed DEmRNAs were enriched in 58 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, consisting of hematopoietic cell lineage, hypertrophic cardiomyopathy, and the JAK-STAT signaling pathway. The Gleason score of the high-risk group showed a significant difference from that of the low-risk group after clinical data analysis (P < 0.05).ConclusionThe prognostic risk model and nomogram constructed based on the immune-cholesterol-related genes had a great prognostic performance for prostate cancer.

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

The morbidity and mortality of patients with colorectal cancer, one of the most common malignant tumors worldwide, is steadily increasing. The aim of this study was to investigate the association between prognostic immune-related gene profile and the outcome of colorectal cancer in patients by analyzing datasets from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) further demonstrated that these genes were enriched in many immune-related biological processes. Univariate Cox regression analysis was applied to examine the association of immune-related genes with the prognosis in patients with colorectal cancer. The least absolute shrinkage and selection operation (LASSO) Cox regression model was then used to establish the immune-related signature for the prognostic evaluation of colorectal cancer in patients. Survival differences were assessed by the Kaplan-Meier method along with the log-rank test. A total of 133 prognostic immune-related signatures were identified by using the univariate Cox proportional hazards regression analysis. A 14-gene signature-based risk score was constructed using the LASSO Cox regression. According to the cut-off of the risk-score, patients were assigned to the low-risk and high-risk groups. The log-rank test suggested that the survival time of the low-risk group was significantly higher than that of the high-risk group. In the time-dependent ROC curve analysis, the AUC for 1-year, 3-year, and 5-year overall survival (OS) were 0.781, 0.742, and 0.791, respectively. GO and KEGG analysis further revealed that the gene sets were actively involved in immune and inflammatory response, as well as the cytokine-cytokine receptor interaction pathway. To summarize, we identified a novel 14-gene immune-related signature that may potentially serve as a prognostic predictor for colorectal cancer, thereby contributing to patient personalized treatment decisions. Further research needs to be conducted to validate the prognostic value of the selected genes.

Background: Colon adenocarcinoma (COAD), a malignant gastrointestinal tumor, has the characteristics of high mortality and poor prognosis. Even in the presence of oxygen, the Warburg effect, a major metabolic hallmark of almost all cancer cells, is characterized by increased glycolysis and lactate fermentation, which supports biosynthesis and provides energy to sustain tumor cell growth and proliferation. However, a thorough investigation into glycolysis- and lactate-related genes and their association with COAD prognosis, immune cell infiltration, and drug candidates is currently lacking.Methods: COAD patient data and glycolysis- and lactate-related genes were retrieved from The Cancer Genome Atlas (TCGA) and Gene Set Enrichment Analysis (GSEA) databases, respectively. After univariate Cox regression analysis, a nonnegative matrix factorization (NMF) algorithm was used to identify glycolysis- and lactate-related molecular subtypes. Least absolute shrinkage and selection operator (LASSO) Cox regression identified twelve glycolysis- and lactate-related genes (ADTRP, ALDOB, APOBEC1, ASCL2, CEACAM7, CLCA1, CTXN1, FLNA, NAT2, OLFM4, PTPRU, and SNCG) related to prognosis. The median risk score was employed to separate patients into high- and low-risk groups. The prognostic efficacy of the glycolysis- and lactate-related gene signature was assessed using Kaplan–Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. The nomogram, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were employed to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on differentially expressed genes (DEGs) from the high- and low-risk groups. Using CIBERSORT, ESTIMATE, and single-sample GSEA (ssGSEA) algorithms, the quantities and types of tumor-infiltrating immune cells were assessed. The tumor mutational burden (TMB) and cytolytic (CYT) activity scores were calculated between the high- and low-risk groups. Potential small-molecule agents were identified using the Connectivity Map (cMap) database and validated by molecular docking. To verify key core gene expression levels, quantitative real-time polymerase chain reaction (qRT–PCR) assays were conducted.Results: We identified four distinct molecular subtypes of COAD. Cluster 2 had the best prognosis, and clusters 1 and 3 had poor prognoses. High-risk COAD patients exhibited considerably poorer overall survival (OS) than low-risk COAD patients. The nomogram precisely predicted patient OS, with acceptable discrimination and excellent calibration. GO and KEGG pathway enrichment analyses of DEGs revealed enrichment mainly in the “glycosaminoglycan binding,” “extracellular matrix,” “pancreatic secretion,” and “focal adhesion” pathways. Patients in the low-risk group exhibited a larger infiltration of memory CD4+ T cells and dendritic cells and a better prognosis than those in the high-risk group. The chemotherapeutic agent sensitivity of patients categorized by risk score varied significantly. We predicted six potential small-molecule agents binding to the core target of the glycolysis- and lactate-related gene signature. ALDOB and APOBEC1 mRNA expression was increased in COAD tissues, whereas CLCA1 and OLFM4 mRNA expression was increased in normal tissues.Conclusion: In summary, we identified molecular subtypes of COAD and developed a glycolysis- and lactate-related gene signature with significant prognostic value, which benefits COAD patients by informing more precise and effective treatment decisions.