scholarly journals A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Zong-xiu Yin ◽  
Chun-yan Xing ◽  
Guan-hua Li ◽  
Long-bin Pang ◽  
Jing Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Scores ◽  
Biological Pathway ◽  
Training Cohort ◽  
Therapeutic Recommendations

Abstract Background Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy. Methods We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8). Results Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels. Conclusions In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

scholarly journals Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chen Han ◽  
Cong Zhang ◽  
Huixia Wang ◽  
Kexin Li ◽  
Lianmei Zhao
Keyword(s):  
High Risk ◽  
Correlation Analysis ◽  
Survival Time ◽  
Risk Score ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Prognostic Signature ◽  
Training Cohort ◽  
Stomach Adenocarcinoma

Abstract Background Stomach 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. Methods The 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. Results A 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. Conclusion Our 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.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals Prognostic Implications of Immune-Related Gene Pairs Signatures in Bladder Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Hui Xiong ◽  
Hui Gao ◽  
Jinding Hu ◽  
Yun Dai ◽  
Hanbo Wang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Related Gene ◽  
Gene Pairs ◽  
Immune Related Gene

Compelling evidence indicates that immune function is correlated with the prognosis of bladder cancer (BC). Here, we aimed to develop a clinically translatable immune-related gene pairs (IRGPs) prognostic signature to estimate the overall survival (OS) of bladder cancer. From the 251 prognostic-related IRGPs, 37 prognostic-related IRGPs were identified using LASSO regression. We identified IRGPs with the potential to be prognostic markers. The established risk scores divided BC patients into high and low risk score groups, and the survival analysis showed that risk score was related to OS in the TCGA-training set ( p < 0.001 ; HR = 7.5 [5.3, 10]). ROC curve analysis showed that the AUC for the 1-year, 3-year, and 5-year follow-up was 0.820, 0.883, and 0.879, respectively. The model was verified in the TCGA-testing set and external dataset GSE13507. Multivariate analysis showed that risk score was an independent prognostic predictor in patients with BC. In addition, significant differences were found in gene mutations, copy number variations, and gene expression levels in patients with BC between the high and low risk score groups. Gene set enrichment analysis showed that, in the high-risk score group, multiple immune-related pathways were inhibited, and multiple mesenchymal phenotype-related pathways were activated. Immune infiltration analysis revealed that immune cells associated with poor prognosis of BC were upregulated in the high-risk score group, whereas immune cells associated with a better prognosis of BC were downregulated in the high-risk score group. Other immunoregulatory genes were also differentially expressed between high and low risk score groups. A 37 IRGPs-based risk score signature is presented in this study. This signature can efficiently classify BC patients into high and low risk score groups. This signature can be exploited to select high-risk BC patients for more targeted treatment.

scholarly journals An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

2020 ◽  
Author(s):  
Minling Liu ◽  
Wei Dai ◽  
Mengyuan Zhu ◽  
Xueying Li ◽  
Shan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Validation Cohort ◽  
Functional Enrichment ◽  
Biological Behavior ◽  
Risk Scores ◽  
Training Cohort

Abstract Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

scholarly journals Identification and validation of a six-gene signature associated with glycolysis to predict the prognosis of patients with cervical cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Luya Cai ◽  
Chuan Hu ◽  
Shanshan Yu ◽  
Lixiao Liu ◽  
Xiaobo Yu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Validation Cohort ◽  
Patient Survival ◽  
Expression Profiles ◽  
Predictive Ability ◽  
Gene Signature ◽  
Training Cohort ◽  
Score Model

Abstract Background Cervical cancer (CC) is one of the most common gynaecological cancers. The gene signature is believed to be reliable for predicting cancer patient survival. However, there is no relevant study on the relationship between the glycolysis-related gene (GRG) signature and overall survival (OS) of patients with CC. Methods We extracted the mRNA expression profiles of 306 tumour and 13 normal tissues from the University of California Santa Cruz (UCSC) Database. Then, we screened out differentially expressed glycolysis-related genes (DEGRGs) among these mRNAs. All patients were randomly divided into training cohort and validation cohort according to the ratio of 7: 3. Next, univariate and multivariate Cox regression analyses were carried out to select the GRG with predictive ability for the prognosis of the training cohort. Additionally, risk score model was constructed and validated it in the validation cohort. Results Six mRNAs were obtained that were associated with patient survival. The filtered mRNAs were classified into the protective type (GOT1) and the risk type (HSPA5, ANGPTL4, PFKM, IER3 and PFKFB4). Additionally, by constructing the prognostic risk score model, we found that the OS of the high-risk group was notably poorer, which showed good predictive ability both in training cohort and validation cohort. And the six-gene signature is a prognostic indicator independent of clinicopathological features. Through the verification of PCR, the results showed that compared with the normal cervial tissuses, the expression level of six mRNAs were significantly higher in the CC tissue, which was consistent with our findings. Conclusions We constructed a glycolysis-related six-gene signature to predict the prognosis of patients with CC using bioinformatics methods. We provide a thorough comprehension of the effect of glycolysis in patients with CC and provide new targets and ideas for individualized treatment.

Identification and Validation of Autophaggen-based Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

2020 ◽  
Author(s):  
JinQun Jiang ◽  
HongYan Xu ◽  
PingShen Zhao ◽  
Hai Lu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Enrichment Score ◽  
Training Set ◽  
Autophagy Gene

Cervical cancer is a common malignancy in women and has a poor prognosis.More and more studies have shown that autophagy disorder is closely related to the occurrence of tumors. However, the prognostic role of autophagy gene in cervical cancer is still unclear. In this study, we constructed the risk signatures of autophagy related genes to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from the TCGA and GES52903 queues as training sets and validation sets. The cervical normal tissue expression profile data from UCSC XENA website is GTEx data as a supplement to TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes with the Consensus approach tumor samples from the TCGA is divided into six subtypes, and the clinical traits in the six subtypes have different distribution, with further then absolute shrinkage and selection operator (LASSO) and multiariable COX regression method finally got seven autophagy genetic risk model is constructed, in the training set, the survival rate of high risk group is lower than the low risk group (p &lt; 0.0001), the validation set,The AUC area of the receiver operating characteristic (ROC) curve, the training set is 0.894, and the verification set is 0.736. We find that the high and low risk score is closely related to the TMN stage (All P is less than 0.05).The nomogram shows that the risk score combined with other indicators such as age, G,T,M, and N better predicts 1-year, 2-year, 3-year survival, and the DCA curve shows that the risk model combined with other indicators produces better clinical efficacy.Then immune cells in 28 in the enrichment score, there were statistically significant differences, high and low risk most GSEA enrichment analysis, the main enrichment in G2 / M checkpoint high-risk score, Genes defining epithelial and mesenchymal transition, raised in response to the low oxygen levels (hypoxia) gene, gene is important to the mitotic spindle assembly, these are closely related with the occurrence of tumor . In conclusion, our constructed autophagy risk signature may be a prognostic tool for cervical cancer.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

scholarly journals Development and Validation of a Lncrnas-Based Nomogram for Survival Prediction in Neuroblastoma Patients

2021 ◽  
Author(s):  
Yong Lv ◽  
ShuGuang Jin ◽  
Bo Xiang
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment

Abstract BackgroundTreatment of neuroblastoma is evolving toward precision medicine. LncRNAs can be used as prognostic biomarkers in many types of cancer.MethodsBased on the RNA-seq data from GSE49710, we built a lncRNAs-based risk score using the least absolute shrinkage and selection operation (LASSO) regression. Cox regression, receiver operating characteristic curves were used to evaluate the association of the LASSO risk score with overall survival. Nomograms were created and then validated in an external cohort from TARGET database. Gene set enrichment analysis was performed to identify the significantly changed biological pathways. ResultsThe 16-lncRNAs-based LASSO risk score was used to separate patients into high-risk and low-risk groups. In GSE49710 cohort, the high-risk group exhibited a poorer OS than those in the low-risk group (P<0.001). Moreover, multivariate Cox regression analysis demonstrated that LASSO risk score was an independent risk factor (HR=6.201;95%CI:2.536-15.16). The similar prognostic powers of the 16-lncRNAs were also achieved in the external cohort and in stratified analysis. In addition, a nomogram was established and worked well both in the internal validation cohort (C-index=0.831) and external validation cohort (C-index=0.773). The calibration plot indicated the good clinical utility of the nomogram. Gene set enrichment analysis (GSEA) indicated that high-risk group was related with cancer recurrence, metastasis and inflammatory associated pathways.ConclusionThe lncRNA-based LASSO risk score is a promising and potential prognostic tool in predicting the survival of patients with neuroblastoma. The nomogram combined the lncRNAs and clinical parameters allows for accurate risk assessment in guiding clinical management.

scholarly journals Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
M2 Macrophages ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

scholarly journals Alternative Splicing Events in Tumor Immune Infiltration in Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian-yu Shi ◽  
Yan-yan Bi ◽  
Bian-fang Yu ◽  
Qing-feng Wang ◽  
Dan Teng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Alternative Splicing ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Cell Infiltration ◽  
Immune Infiltration

Despite extensive research, the exact mechanisms involved in colorectal cancer (CRC) etiology and pathogenesis remain unclear. This study aimed to examine the correlation between tumor-associated alternative splicing (AS) events and tumor immune infiltration (TII) in CRC. We analyzed transcriptome profiling and clinical CRC data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq and Innate databases, respectively to develop and validate a risk model of differential AS events and subsequently a TII risk model. We then conducted a two-factor survival analysis to study the association between TII and AS risk and evaluated the associations between immune signatures and six types of immune cells based on the TIMER database. Subsequently, we studied the distribution of six types of TII cells in high- and low-risk groups for seven AS events and in total. We obtained the profiles of AS events/genes for 484 patients, which included 473 CRC tumor samples and 41 corresponding normal samples, and detected 22581 AS events in 8122 genes. Exon Skip (ES) (8446) and Mutually Exclusive Exons (ME) (74) exhibited the most and fewest AS events, respectively. We then classified the 433 patients with CRC into low-risk (n = 217) and high-risk (n = 216) groups based on the median risk score in different AS events. Compared with patients with low-risk scores (mortality = 11.8%), patients with high-risk scores were associated with poor overall survival (mortality = 27.6%). The risk score, cancer stage, and pathological stage (T, M, and N) were closely correlated with prognosis in patients with CRC (P &lt; 0.001). We identified 6479 differentially expressed genes from the transcriptome profiles of CRC and intersected 468 differential immune-related signatures. High-AS-risk and high-TII-risk predicted a poor prognosis in CRC. Different AS types were associated with different TII risk characteristics. Alternate Acceptor site (AA) and Alternate Promoter (AP) events directly affected the concentration of CD4T cells, and the level of CD8T cells was closely correlated with Alternate Terminator (AT) and Exon Skip (ES) events. Thus, the concentration of CD4T and CD8T cells in the CRC immune microenvironment was not specifically modulated by AS. However, B cell, dendritic cell, macrophage, and neutrophilic cell levels were strongly correlated with AS events. These results indicate adverse associations between AS event risk levels and immune cell infiltration density. Taken together, our findings show a clear association between tumor-associated alternative splicing and immune cell infiltration events and patient outcome and could form a basis for the identification of novel markers and therapeutic targets for CRC and other cancers in the future.

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