Long non-coding RNA profile study identifies a metabolism-related signature for colorectal cancer

Abstract Background Heterogeneity in colorectal cancer (CRC) patients provides novel strategies in clinical decision-making. Identifying distinctive subgroups in patients can improve the screening of CRC and reduce the cost of tests. Metabolism-related long non-coding RNA (lncRNA) can help detection of tumorigenesis and development for CRC patients. Methods RNA sequencing and clinical data of CRC patients which extracted and integrated from public databases including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were set as training cohort and validation cohort. Metabolism-related genes were acquired from Kyoto Encyclopedia of Genes and Genomes (KEGG) and the metabolism-related lncRNAs were filtered using correlation analysis. The risk score was calculated based on lncRNAs with prognostic value and verified through survival curve, receiver operating characteristic (ROC) curve and risk curve. Prognostic factors of CRC patients were also analyzed. Nomogram was constructed based on the results of cox regression analyses. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA). Results The training cohort and the validation cohort enrolled 432 and 547 CRC patients respectively. A total of 23 metabolism-related lncRNAs with prognostic value were screened out and 10 of which were significantly differentially expressed between tumour and normal tissues. Finally, 8 lncRNAs were used to establish a risk score (DICER1-AS1, PCAT6, GAS5, PRR7-AS1, MCM3AP-AS1, GAS6-AS1, LINC01082 and ADIRF-AS1). Patients were divided into high-risk and low-risk groups according to the median of risk scores in training cohort and the survival curves indicated that the survival prognosis was significantly different. The area under curve (AUC) of the ROC curve in two cohorts were both greater than 0.6. The age, tumour stage and risk score were selected as independent factors and used to construct a nomogram to predict CRC patients' survival rate with the c-index of 0.806. The ssGSEA indicated that the risk score was associated with immune cells and functions. Conclusions Our systematic study established a metabolism-related lncRNA signature to predict outcomes of CRC patients which may contribute to individual prevention and treatment.

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Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e15565 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e15565-e15565

Author(s):

Qiqi Zhu ◽

Du Cai ◽

Wei Wang ◽

Min-Er Zhong ◽

Dejun Fan ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Prognostic Value ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Training Cohort ◽

Gene Pairs ◽

Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

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An Immune Risk Score Predicts Survival of Patients with Myelodysplastic Syndrome

Blood ◽

10.1182/blood-2020-137349 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 33-34

Author(s):

Yang Liang ◽

Fang Hu ◽

Yu-Jun Dai ◽

Yun Wang ◽

Huan Li

Keyword(s):

Regression Analysis ◽

Myelodysplastic Syndrome ◽

Risk Score ◽

Prognostic Model ◽

Validation Cohort ◽

Gene Set Enrichment Analysis ◽

Type I ◽

Immune Gene ◽

Training Cohort ◽

Immune Score

Background: Myelodysplastic syndrome (MDS) was characterized as ineffective hematopoiesis, increased transformation to acute myeloid leukemia (AML), and accompanied by immune system dysfunction. However, the immune signature of MDS remains elusive. Methods: The clinical data (age, sex, international prognostic score system (IPSS), hemoglobin, blast, RBC transfusion dependence, and corresponding subject-level survival) as well as expression profiles of MDS (CD34+ cells) were obtained from Gene Expression Omnibus (GEO: GSE 58831; GSE 114922). A robust prognosis model of immune genes was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis. Survival analysis for prognostic model was carried out through the Kaplan-Meier curve and Log-rank test. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the accuracy of prognostic models. Immune score for different subtype were calculated further by single sample gene set enrichment analysis (ssGSEA). Result: A novel robust immune gene prognostic model indicate that subtype with lower risk score were longer overall survival (OS) than subtype with higher risk score in training cohort (Figure1 A, C). The model was further verified by the validation cohort (Figure1 B, D). The multivariate Cox regression analysis demonstrated the model was an independent prognostic factor for OS prediction with hazard ratios of 56.694 (95% CIs: 9.038−355.648), 3.009 (95% CIs: 1.042−8.692) both in train cohort and external validation cohort respectively (Figure1 G, H). The AUC of 5- year were 0.92 (95% CIs: 0.86 - 0.97) and 0.7 (95% CIs: 0.51 - 0.89) for OS respectively in training cohort and validation cohort (Figure1 E, F). Furthermore, ssGSEA showed higher risk score subtype was significantly associated with higher immune score of check point, human leukocyte antigen (HLA), T cell co-inhibition and type I interferon (IFN) response (Figure1 K-N), which indicating that the poor outcome might be caused by tumor-associated immune response dysfunction partly. Conclusion: We constructed a robust immune gene prognostic model, which have a potential prognostic value for MDS patients and may provide evidence for personalized immunotherapy. Figure Disclosures No relevant conflicts of interest to declare.

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The Prognostic Significance of Tumor Deposit Count for Colorectal Cancer Patients after Radical Surgery

Gastroenterology Research and Practice ◽

10.1155/2020/2052561 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Kuo Zheng ◽

Nanxin Zheng ◽

Cheng Xin ◽

Leqi Zhou ◽

Ge Sun ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Value ◽

Validation Cohort ◽

Cutoff Point ◽

Disease Specific Survival ◽

Optimal Cutoff ◽

Training Cohort ◽

Tumor Deposit ◽

Optimal Cutoff Point ◽

Disease Specific

Background. The prognostic value of tumor deposit (TD) count in colorectal cancer (CRC) patients has been rarely evaluated. This study is aimed at exploring the prognostic value of TD count and finding out the optimal cutoff point of TD count to differentiate the prognoses of TD-positive CRC patients. Method. Patients diagnosed with CRC from Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2010, to December 31, 2012, were analyzed. X-tile program was used to identify the optimal cutoff point of TD count in training cohort, and a validation cohort was used to test this cutoff point after propensity score matching (PSM). Univariate and multivariate Cox proportional hazard models were used to assess the risk factors of survival. Results. X-tile plots identified 3 (P<0.001) as the optimal cutoff point of TD count to divide the patients of training cohort into high and low risk subsets in terms of disease-specific survival (DSS). This cutoff point was validated in validation cohort before and after PSM (P<0.001, P=0.002). More TD count, which was defined as more than 3, was validated as an independent risk prognostic factor in univariate and multivariate analysis (P<0.001). Conclusion. More TD count (TD count≥4) was significantly associated with poor disease-specific survival in CRC patients.

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Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

Precision Clinical Medicine ◽

10.1093/pcmedi/pbab002 ◽

2021 ◽

Author(s):

Ke Zhao ◽

Lin Wu ◽

Yanqi Huang ◽

Su Yao ◽

Zeyan Xu ◽

...

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Prognostic Value ◽

Validation Cohort ◽

Survival Rates ◽

Training Cohort ◽

Mucinous Component ◽

Hematoxylin And Eosin ◽

High Consistency ◽

Whole Slide Images

Abstract Background In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large amount of adenocarcinoma, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patients cohorts. Methods Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed by the Cox proportional hazard model. Result Patients were stratified to mucus-low and mucus-high groups by 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18-2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21-3.60, 0.008; 79.8% vs. 62.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. Conclusion The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development.

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The Prognostic Value of Preoperative Serum Lactate Dehydrogenase (LDH) Levels in Patients Underwent Curative-intent Hepatectomy For Colorectal Liver Metastases: A Two-Center Cohort Study

10.21203/rs.3.rs-236654/v1 ◽

2021 ◽

Author(s):

Long Bai ◽

Ze-Yu Lin ◽

Yun-Xin Lu ◽

Qin Chen ◽

Han Zhou ◽

...

Keyword(s):

Colorectal Cancer ◽

Lactate Dehydrogenase ◽

Cancer Patients ◽

Prognostic Value ◽

Colorectal Liver Metastases ◽

Validation Cohort ◽

Curative Intent ◽

Training Cohort ◽

Preoperative Serum ◽

Colorectal Cancer Patients

Abstract Background: The prognostic value of lactate dehydrogenase (LDH) in colorectal cancer patients has remained inconsistent between non-metastatic and metastatic settings. So far very few studies have included LDH in prognostic analysis for patients with colorectal liver metastases (CRLM) who underwent curative-intent hepatectomy. Patients and Methods: Consecutive metastatic colorectal cancer patients who underwent curative-intent resection for CRLM from two Chinese medical centers treated in 2000-2019 were enrolled in the training cohort (434 patients) and the validation cohort (146 patients). Overall survival (OS) was the primary endpoint. Cox regression model was performed to identify the prognostic values of LDH and other clinicopathology variables. A modification of the established Fong scoring system comprising LDH was developed within this Chinese population.Results: In the training cohort, preoperative LDH > upper limit of normal (ULN) was the strongest independent prognostic factor both for RFS (HR 2.11, 95% confidence intervals [CIs], 1.54-2.89; P < .001) and OS (HR 2.41, 95% CI, 1.72-3.39; P < .001) in multivariate analysis. 5-year survival rates were 23.7% and 52.9% in the LDH > ULN group and LDH < ULN group, respectively. These data were also confirmed in the validation cohort and then in pooled cohort. Replacing carcinoembryonic antigen (CEA) with LDH in the Fong score contributed to an improvement in the predictive value.Conclusions: Preoperative serum LDH is a reliable and independent predictor for curative-intent CRLM resection. Composite of LDH and Fong score is a potential stratification tool for CRLM resection.

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A Prognostic Model Constructed Based on Rna Binding Protein to Predict the Prognosis of Patients With Rectal Cancer

10.21203/rs.3.rs-370155/v1 ◽

2021 ◽

Author(s):

Liqiang Zhou ◽

You Wu ◽

Shihao Li ◽

Dengzhong Wu ◽

Jinliang Wang ◽

...

Keyword(s):

Rectal Cancer ◽

Roc Curve ◽

Prognostic Model ◽

Clinical Decision Making ◽

Rna Binding ◽

Validation Cohort ◽

Rna Binding Protein ◽

Clinical Decision ◽

Clinical Samples ◽

Training Cohort

Abstract Background: The incidence of rectal cancer in young people is increasing, and there has been a problem of poor prognosis in recent years. Many studies have shown that RNA binding protein (RBP) is related to the progression of various malignant tumors. However, the role of RBPs in rectal cancer is poorly understood. New prognostic models are urgently needed.Materials and methods: In the study, we used the RBPTD database, The Cancer Genome Atlas (TCGA) database and the transcription data information and corresponding clinical information of rectal cancer patients in the Gene Expression Omnibus (GEO) database to screen out RBPs that are differentially expressed in tumor tissues and normal tissues. Subsequently, we analyzed the prognostic value of these RBPs using bioinformatics methods. In order to screen the key RBP in the occurrence of rectal tumors and establish a prognostic risk score model. The use of survival analysis shows that assessing the relationship between key RBPs and the patient's overall survival rate. In the TCGA cohort, the prognostic model was further tested. At the same time, the nomogram of the 6 RBP mRNAs in the TCGA cohort was constructed, and the ROC curve was used for verification. Finally, q-PCR was performed on clinical samples to verify the expression of hub genes.Results: The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good results in the training cohort and validation cohort. The new 6RBP (EXO1, TOP2A, RUVBL1, NXT1, PACSIN2, WDR4) prognostic model was established to predict the prognosis of rectal cancer. The ROC curve showed good survival prediction in both the training cohort and the validation cohort. The constructed nomogram has certain guiding significance for clinical decision-making. In addition, GSEA analysis revealed potential biological functions. The q-PCR verification results showed the consistency with the construction of the prognostic model.Conclusions: We constructed a six RBPs prognostic model and a nomogram to predict the prognosis of patients with rectal cancer, and performed q-PCR expression testing through clinical samples, which may help clinical decision-making.

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