scholarly journals Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer

Author(s):  
Xu Wang ◽  
Ke Chen ◽  
Zhenglin Wang ◽  
Yuanmin Xu ◽  
Longfei Dai ◽  
...  

Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs).Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer.Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues.Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.


2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.


2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.


2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and AGRs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.


2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.


2020 ◽  
Author(s):  
Qian Xu ◽  
Yugang Guo ◽  
Jintao Fang ◽  
Jiawei Zhou ◽  
Guohui Ma ◽  
...  

Abstract Background: In the clinical decision-making among patients with colon cancer (COAD), making an accurate prognosis of the patients plays a central role. The effects of autophagy on the clinical outcomes of cancer, including COAD, have been widely reported in numerous studies. Here, weaim to build a novel autophagy-associated, risk-stratification scoring system to predict the overall survival(OS)of patients with COAD. Methods: In this study, the candidate autophagy-related prognostic genes correlated with the survival of COAD patients from The Cancer Genome Atlas (TCGA) public RNA microarray and clinical data sets were selected as training data set. A cohort of 67 patients from TCGA and a cohort of 124 patients from GEO were used for the external validation. The autophagy-related mRNAs(ARGs) were analyzed by multivariate Cox regression analyses. Spearman correlation analysis were used to construct autophagy-related mRNAs and lncRNAs coexpression network. Results: 6 autophagy-related mRNAs and 14 lncRNAs with prognostic value were extracted for constructing two novel autophagy-related RNAs signatures, respectively. Univariate and multivariate Cox regression analyses were then demonstrated that the two signature could act as independent prognostic predictor for OS. Additionally, a prognostic nomogram incorporating the clinicopathological characteristics(patient’s age, tumor stage) and autophagy-related lncRNA risk score was constructed to predict the OS, which was used in the training and validation sets (5-year C-index: 0.826 and 0.895, respectively), demonstrating better discrimination ability and clinical net benefit than the risk score model. Further gene set enrichment analysis revealed that autophagy-associated lncRNAs were significantly enriched in cancer-related pathways.Conclusions: The identified autophagy-related mRNAs and lncRNAs signature had important clinical implications in prognosis prediction and the user-friendly nomogram may offer an extra insight for individualized therapy of COAD.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mingchao Hu ◽  
Jianchun Gu ◽  
Wenzhao Su ◽  
Zhenjie Zhang ◽  
Baosong Zhu ◽  
...  

Aim. The aim of our work was to determine the utility of DNM1 as a biomarker for the diagnosis and prognosis of colon cancer (CC). Methods. DNM1 expression variations in CC vs. normal tissues were investigated using The Cancer Genome Atlas (TCGA) database. The association of DNM1 expression levels with the clinicopathological variables in CC prognosis was investigated using logistic regression analyses. Independent prognostic factors for CC were evaluated using univariate and multivariate Cox regression analyses. The correlation between DNM1 expression and immune cell infiltration was estimated using single-sample Gene Set Enrichment Analysis (ssGSEA). Results. DNM1 expression in CC tissues was significantly higher than that in normal tissues. High DNM1 expression was significantly correlated with M stage, N stage, perineural invasion and lymphatic invasion and predicted poor prognosis. The univariate analysis highlighted that DNM1 was an independent CC risk factor. Results of ssGSEA showed that DNM1 was linked to several cancer-related pathways, including the neuroactive ligand-receptor interaction, hypertrophic cardiomyopathy, ECM-receptor interaction, dilated cardiomyopathy, and calcium signaling pathway. Moreover, DNM1 expression was positively correlated with the level of infiltration by Neutrophils, Tregs, NK cells, and Macrophages. Conclusion. DNM1 has a significant function and has diagnostic and prognostic potential for CC.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1738-1738 ◽  
Author(s):  
Ya Zhang ◽  
Xiaosheng Fang ◽  
Na Chen ◽  
Xiao Lv ◽  
Xueling Ge ◽  
...  

Introduction N6-methyladenosine (m6A) RNA methylation is the most abundant epitranscriptomic modification, dynamically installed by the m6A methyltransferases (termed as "writers"), reverted by the demethylases (termed as "erasers"), and recognized by m6A binding proteins (termed as "readers"). Emerging evidence suggests that m6A RNA methylation regulates RNA stability, and participates in the pathogenesis of multiple diseases including cancers. Nevertheless, the role of m6A RNA methylation in chronic lymphocytic leukemia (CLL) remains to be unveiled. Herein, we hypothesized that m6A RNA methylation contributed to the tumorigenesis and maintenance of CLL. Moreover, the risk-prediction model integrated with the m6A regulators could serve as a novel and effective prognostic indicator in CLL. This study aimed to identify robust m6A RNA methylation-associated fingerprints for risk stratification in patients with CLL. Methods A total of 714 de novo CLL patients from 4 cohorts (China, Spain, Germany and Italy) were enrolled with informed consents. EpiQuik m6A RNA methylation colorimetric quantification assay was utilized to assess m6A RNA methylation levels. LASSO Cox regression algorithm was performed to calculate m6A RNA methylation-associated risk score (short for "m6A risk score") in R software. Besides, Kaplan-Meier survival analysis with log-rank test, univariate and multivariate Cox regression analyses and ROC curve analysis of overall survival (OS) were conduct to explore the prognostic value of m6A signature in CLL. Furthermore, RNA-seq, MeRIP-seq, Ribo-seq, functional enrichment analyses in silico and preclinical experiments ex vivo were applied to confirm the biological mechanism of the m6A regulators in CLL. Results In the present study, we performed a comprehensive analysis to dissect the role of m6A RNA methylation regulators in CLL. Compared with normal B cells from healthy donors, obvious decreased level of m6A RNA methylation was observed in primary CLL cells (p<0.01; Figure 1A). In addition, down-regulated m6A RNA methylation was also detected in CLL cell lines MEC1 and EHEB (p<0.05; Figure 1A). Then, we further investigated the association of the m6A RNA methylation regulators with clinical outcomes of CLL patients. By LASSO Cox regression analysis in 486 CLL patients, the m6A risk score was established with the coefficients of fourteen m6A regulators at the minimum lambda value of 0.00892 (Figure 1B-C). Based on the median risk score as the cut-off value, a clear distribution pattern was delineated in CLL patients (Figure 1D). Kaplan-Meier curves showed stratified high-risk patients presented significantly shorter OS versus the low-risk group (HR=4.477, p<0.001; Figure 2A). Besides, m6A risk score also predicts inferior prognosis in stable subgroup (HR=3.097, p=0.037; Figure 2B), and progressed/ relapsed subgroup (HR=3.325, p=0.001; Figure 2C). Moreover, univariate, multivariate cox regression analyses and ROC curve confirmed high m6A risk score as an independent survival predictor in CLL patients (p<0.001; Figure 2D-E). Thereafter, the clinicopathological relevance and underlying mechanism of m6A risk score were explored. Significant elevated m6A risk score was detected in patients with unfavorable treatment responses compared with stable status (p<0.001; Figure 3A). Furthermore, CLL patients with advanced Binet stage, positive ZAP-70 and unmutated IGHV present increased m6A risk score (p<0.05; Figure 3B-C). Intriguingly, we also observed the significantly negative correlation between highrisk score and 13q14 deletion, in accordance with patients' inferior outcome (p=0.047; Figure 3D). Moreover, Pearson correlation analysis, STRING interactive network and functional enrichment analyses deciphered that the m6A regulators exerted crucial roles in CLL progression potentially via modulating RNA metabolism and oncogenic pathways (Figure 4A-C). Conclusion To date, our study provides evidence for the first time that reduced m6A RNA methylation contributes to the tumorigenesis of CLL. Distinct m6A risk scoreis demonstrated as an efficient tool facilitating prognosis evaluation in CLL patients. However, validation of the signature in more independent cohorts are warranted. Further interrogations will be elucidated on the biological mechanism of m6A regulators, highlighting insights into pathogenesis and therapy strategy of CLL. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 23-24
Author(s):  
Xiaomin Chen ◽  
Shuai Ren ◽  
Mengfei Ding ◽  
Yiqing Cai ◽  
Shunfeng Hu ◽  
...  

Introduction N6-methyladenosine (m6A) is the most abundant form of internal modifications in eukaryotic cells. m6A methylation is dynamically modulated by diverse types of regulators, including methyltransferases ('writers'), RNA binding proteins ('readers'), and demethylases ('erasers'). Growing evidence has shown that m6A methylation plays essential role in the development and progression of multiple cancers. However, the functions of m6A methylation in diffuse large B-cell lymphoma (DLBCL) remains undefined. Herein, we aimed to identify novel prognostic biomarker by m6A methylation regulators and explore its underlying mechanism in DLBCL. Methods The available expression data of 48 DLBCL samples and 337 normal samples and the clinical information from 928 DLBCL samples were separately extracted from three databases. The expression level of the m6A methylation regulators was analyzed and the LASSO Cox regression was employed to calculate risk score. Kaplan-Meier survival analysis, univariate and multivariate Cox regression analyses, and ROC curve analysis were conducted. GO and KEGG enrichement were applied to explore the potential function of KIAA1429 in DLBCL. The lymph node biopsies of DLBCL patients and reactive hyperplasia cases were collected with informed consent to detect the expression of KIAA1429. Results We firstly assessed the expression of m6A methylation regulators in DLBCL and found that most of them were dysregulated (p&lt;0.001; Figure 1A-B). Subsequently, we conjectured that the alteration of m6A methylation regulators ratio may be an inherent feature representing individual differences, and discovered that the proportion of diverse m6A RNA methylation regulators was weakly to strongly relevant (p&lt;0.05; Figure 1C). To evaluate the clinical prognostic value of m6A methylation regulators in DLBCL patients, 475 GCB DLBCL patients, which are intimately associated with double-hit lymphoma (DHL) were selected for further analysis. The univariate Cox regression analysis indicated that six genes were high-risk and were significantly associated with OS (p&lt;0.05, HR&gt;1; Figure 2A), including KIAA1429 (p=0.043, HR=1.743). Six genes were selected based on the minimum criteria of LASSO Cox regression to establish the risk signature (Figure 2B-C). The high-risk group had a significantly shorter OS in DLBCL patients (p&lt;0.001; Figure 2D). Furthermore, ROC curve, univariate, and multivariate Cox regression analyses showed that high m6A risk score acted as an independent indicator in DLBCL patients (p&lt;0.001; Figure 2E-G). We further evaluated the correlation between m6A methylation regulators and clinicopathological feature of DHL patients. KIAA1429 was found to be significantly associated with the IPI and DHL (p&lt;0.05; Figure 3A-B). High expression of KIAA1429 resulted in a negative correlation with OS in DHL patients (p=0.018; Figure 3C). However, no significant difference was found in the OS of non-DHL patients (Figure 3D). Univariate analyses indicated that KIAA1429 was an independent indicator in DLBCL patients (p=0.04; Figure 3E). Enhanced expression levels of KIAA1429 mRNA and protein were verified in DLBCL cell lines (Figure 3F-G). Additionally, samples from DLBCL patients also showed significantly high expression of KIAA1429 compared to the reactive hyperplasia group, with the positive rate of 93% (50 of 54) and 25% (5 of 20), respectively (Figure 3H). To investigate the underlying mechanism of KIAA1429, WGCNA was used to divide genes into different modules (Figure 4A-B). Subsequently, We selected the blue module including KIAA1429 to analyze their functions. A significant association between KIAA1429 expression and its module genes was identified (Figure 4C). GO and KEGG enrichment illuminated that KIAA1429 may act as a potential prognostic biomarker by regulating the mRNA processing and MAPK signaling pathways (Figure 4D-F). Conclusions In summary, we identified for the first time that m6A methylation regulators were dysregulated in DLBCL, and its risk score could exert as an independent prognostic indicator in GCB-DLBCL. More importantly, our study demonstrated the prognostic value of KIAA1429 in DHL patients. Further investigations on the mechanism of KIAA1429 in DLBCL may assist clinicians in achieving individualized treatment for this patient population. Keywords: m6A, KIAA1429, prognosis, mechanism, DLBCL Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  

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.


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