Development and validation of prognostic model for predicting mortality of COVID-19 patients in Wuhan, China

AbstractNovel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell’s C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.

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Prognostic Model for Mortality of Hospitalized Patients with COVID-19 Pneumonia in Wuhan, China: A Multi-center retrospective cohort study

10.21203/rs.3.rs-28186/v1 ◽

2020 ◽

Author(s):

Qi Mei ◽

Amanda Y. Wang ◽

Yang Yang ◽

Ming Li ◽

Fei Wang ◽

...

Keyword(s):

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Validation Cohort ◽

Risk Group ◽

Clinical Prognosis ◽

Training Cohort ◽

Risk Of Mortality ◽

Increased Risk

Abstract Background: Novel coronavirus (COVID-19) infection is a global public health issue and has now affected more than 70 countries worldwide. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with high risk of mortality. However, prognostic factors assessing poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain unclear.Methods: We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the of end of follow up period was the primary outcome. Prognostic factors for mortality were also assessed and a prognostic model was developed, calibrated and validated.Results: The study included 492 patients with SARS-CoV-2, which were divided into three cohorts, the training cohort (n=237), the validation cohort 1 (n=120), and the validation cohort 2 (n=135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including age, odds ratio (OR), 1.1 / years increase (p<0.001); neutrophil-to-lymphocyte ratio OR, 1.14 (p<0.001), body temperature on admission OR, 1.53 / °C increase (p=0.005), increase of aspartate transaminase OR, 2.47 (p=0.019), and decrease of total protein OR, 1.69 (p=0.018).Furthermore, the prognostic model drawn from the training cohort was validated with the validation cohort 1 and 2 with comparable area under curve (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell’s C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other.Conclusions: A validated prognostic model was developed to assist in determining the clinical prognosis for SARS- CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted in the low risk group had a high probability of survival.

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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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A radiomics nomogram for prediction of overall survival in hepatocellular carcinoma after hepatectomy

10.21203/rs.3.rs-28085/v1 ◽

2020 ◽

Author(s):

Qinqin Liu ◽

Jing Li ◽

Fei Liu ◽

Weilin Yang ◽

Jingjing Ding ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Factors ◽

Training Cohort ◽

Texture Parameters ◽

Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with dismal prognosis, and prediction of the prognosis of HCC can assist the therapeutic decisions. More and more studies showed that the texture parameters of images can reflect the heterogeneity of the tumor, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of the study was to investigate the prognostic value of computed tomography (CT) texture parameters for patients with HCC after hepatectomy, and try to develop a radiomics nomograms by combining clinicopathological factors with radiomics signature.Methods 544 eligible patients were enrolled in the retrospective study and randomly divided into training cohort (n=381) and validation cohort (n=163). The regions of interest (ROIs) of tumor is delineated, then the corresponding texture parameters are extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in training cohort, and the radiomics score (Rad-score) was generated. According to the cut-off value of the Rad-score calculated by the receiver operating characteristic (ROC) curve, the patients were divided into high-risk group and low-risk group. The prognosis of the two groups was compared and validated in the validation cohort. Univariate and multivariable analyses by COX proportional hazard regression model were used to select the prognostic factors of overall survival (OS). The radiomics nomogram for OS were established based on the radiomics signature and clinicopathological factors. The Concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.Result 7 texture parameters associated with OS were selected in the training, and the radiomics signature was formulated based on the texture parameters. The patients were divided into high-risk group and low-risk group by the cut-off values of the Rad-score of OS. The 1-, 3- and 5-year OS rate was 71.0%, 45.5% and 35.5% in the high-risk group, respectively, and 91.7%, 82.1% and 78.7%, in the low-risk group, respectively, with significant difference (P <0.001). COX regression model found that Rad-score was an independent prognostic factor of OS. In addition, the radiomics nomogram was developed based on five variables: α‐fetoprotein (AFP), platelet lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and Rad-score. The nomograms displayed good accuracy in predicting OS (C-index=0.747) in the training cohort and was confirmed in the validation cohort (C-index=0.777). The calibration plots also showed an excellent agreement between the actual and predicted survival probabilities. The DAC indicated that the radiomics nomogram showed better clinical usefulness than the clinicopathologic nomogram.Conclusion The radiomics signature is potential biomarkers of the prognosis of HCC after hepatectomy. Radiomics nomogram that integrated radiomics signature can provide more accurate estimate of OS for patients with HCC after hepatectomy.

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Thrombosis and Risk of Mortality in Newly Diagnosed High-Risk Polycythemia Vera: An Analysis of the Medicare Claims Database in the United States

Blood ◽

10.1182/blood-2020-141370 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 9-10

Author(s):

Naveen Pemmaraju ◽

Aaron T. Gerds ◽

Shreekant Parasuraman ◽

Jingbo Yu ◽

Anne Shah ◽

...

Keyword(s):

High Risk ◽

Median Time ◽

Research Funding ◽

Index Date ◽

Newly Diagnosed ◽

Publicly Traded ◽

Risk Of Mortality ◽

Increased Risk ◽

History Of

Background Polycythemia vera (PV) is a myeloproliferative neoplasm (MPN) associated with an increased risk of thrombotic events (TEs), a major cause of morbidity and mortality. Patients aged ≥60 years and/or with a history of thrombosis are considered to have high-risk PV. There is limited contemporary, real-world evidence exploring the effect of TEs on mortality in patients with PV. The aim of this analysis was to compare the risk of mortality in patients newly diagnosed with high-risk PV who experienced a TE vs those who did not experience a TE. Study Design and Methods All data from the Medicare Fee-for-Service (FFS) claims database (Parts A/B/D) from January 2010-December 2017 were used to identify patients with a PV diagnosis (all high risk based on cohort being ≥65 years of age) with ≥1 inpatient or ≥2 outpatient claims. The index date was the date of the first qualifying PV claim. Patients with a PV diagnosis or use of cytoreductive therapy within 12 months before the index date (pre-index period) were excluded; ≥12-months continuous medical and pharmacy enrollment pre-index dates was required. The study sample was categorized into TE and non-TE groups based on the occurrence of any of the following events during follow-up: deep vein thrombosis, pulmonary embolism, ischemic stroke, acute myocardial infarction, transient ischemic attack, peripheral arterial thrombosis, or superficial thrombophlebitis. TEs were evaluated from the index date to the end of follow-up. Cox regression analyses with time-varying effects were used to assess mortality risk among patients with PV, with post-index TE as a time-dependent variable, stratified by pre-index TE, and adjusting for patient demographic characteristics and comorbid conditions. Results A total of 56,176 Medicare FFS beneficiaries with PV diagnoses met inclusion criteria. The median age was 73 years, 51.9% were men, and 90.7% were white; 10,110 patients (18.0%) had a history of TE before diagnosis (ie, pre-index). In the follow-up period, 20,105 patients (35.8%) had a TE and 36,071 patients (64.2%) did not have a TE. In the comparison between the TE vs non-TE groups, the median (range) age (75.0 [65-104] vs 73.0 [65-106] years, respectively), mean (SD) Charlson comorbidity index score (3.1 [2.6] vs 2.2 [2.3]), and percentage of patients with a history of cardiovascular events (34.1% vs 23.8%), bleeding (13.3% vs 10.4%), or anemia (28.6% vs 23.4%) were higher (Table 1). Among all patients with PV, the median time from diagnosis to first post-index TE was 7.5 months. Among those with pre-index TE (n=10,093), median time from index to first post-index TE was 0.6 months, whereas patients without pre-index TE (n=46,083) had a median time to first post-index TE of 14.2 months. Among all patients with TE during follow-up, the most common TEs were ischemic stroke (47.5%), transient ischemic attack (30.9%), and acute myocardial infarction (30.5%). The risk of mortality was increased for patients who experienced a TE compared with those who did not (hazard ratio [HR; 95% CI], 9.3 [8.4-10.2]; P<0.0001). For patients who experienced a pre-index TE, the risk of mortality was increased for patients who experienced a subsequent TE during follow-up compared with patients who did not (HR [95% CI], 6.7 [5.8-7.8]; P<0.0001). Likewise, for patients who did not experience a pre-index TE, the risk of mortality was increased for patients who experienced a TE during follow-up compared with patients who did not (HR [95% CI], 13.1 [11.4-15.0]; P<0.0001). Conclusions In this real-world study, approximately one-third of patients with newly diagnosed high-risk PV experienced a TE during follow-up and had a 9-fold increased risk of mortality vs those who did not experience a TE. TE risk mitigation remains an important management goal in patients with PV, particularly in those with prior TE. Disclosures Pemmaraju: Samus Therapeutics: Research Funding; Celgene: Honoraria; SagerStrong Foundation: Other: Grant Support; Affymetrix: Other: Grant Support, Research Funding; MustangBio: Honoraria; Blueprint Medicines: Honoraria; LFB Biotechnologies: Honoraria; Plexxikon: Research Funding; Novartis: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Stemline Therapeutics: Honoraria, Research Funding; Pacylex Pharmaceuticals: Consultancy; Daiichi Sankyo: Research Funding; Incyte Corporation: Honoraria; Roche Diagnostics: Honoraria; Cellectis: Research Funding; DAVA Oncology: Honoraria. Gerds:Sierra Oncology: Research Funding; Celgene: Consultancy, Research Funding; Gilead Sciences: Research Funding; Imago Biosciences: Research Funding; Pfizer: Research Funding; CTI Biopharma: Consultancy, Research Funding; Roche/Genentech: Research Funding; Apexx Oncology: Consultancy; AstraZeneca/MedImmune: Consultancy; Incyte Corporation: Consultancy, Research Funding. Parasuraman:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Yu:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Shah:Avalere Health: Current Employment. Xi:Incyte Corporation: Other: Avalere Health is a paid consultant of Incyte Corporation; Avalere Health: Current Employment. Kumar:Avalere Health: Current Employment; Incyte Corporation: Other: Avalere Health is a paid consultant of Incyte Corporation. Scherber:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Verstovsek:Gilead: Research Funding; Incyte Corporation: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; CTI Biopharma Corp: Research Funding; Promedior: Research Funding; Roche: Research Funding; AstraZeneca: Research Funding; Blueprint Medicines Corp: Research Funding; Genentech: Research Funding; Sierra Oncology: Consultancy, Research Funding; Protagonist Therapeutics: Research Funding; ItalPharma: Research Funding; PharmaEssentia: Research Funding; NS Pharma: Research Funding; Celgene: Consultancy, Research Funding.

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PROgnostic Model for Advanced Cancer (PRO-MAC)

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2018-001702 ◽

2019 ◽

Vol 10 (4) ◽

pp. e34-e34 ◽

Cited By ~ 1

Author(s):

Allyn Hum ◽

Yoko Kin Yoke Wong ◽

Choon Meng Yee ◽

Chung Seng Lee ◽

Huei Yaw Wu ◽

...

Keyword(s):

High Risk ◽

Advanced Cancer ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Tertiary Care ◽

Assessment System ◽

Organ Systems ◽

Tertiary Teaching

ObjectiveTo develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting.DesignProspective cohort study with 2 years’ follow-up.SettingSingle tertiary teaching hospital in Singapore.ParticipantsThe study includes consecutive patients diagnosed with advanced cancer who were referred to a palliative care unit between 2013 and 2015 (N=840). Data were randomly split into training (n=560) and validation (n=280) sets.Results743 (88.5%) patients died with a mean follow-up of 97.0 days (SD 174.0). Cox regression modelling was used to build a prognostic model, cross-validating with six randomly split dataset pairs. Predictor variables for the model included functional status (Palliative Performance Scale, PPS V.2), symptoms (Edmonton Symptom Assessment System, ESASr), clinical assessment (eg, the number of organ systems with metastasis, serum albumin and total white cell count level) and patient demographics. The area under the receiver operating characteristic curve using the final averaged prognostic model was between 0.69 and 0.75. Our model classified patients into three prognostic groups, with a median survival of 79.0 days (IQR 175.0) for the low-risk group (0–1.5 points), 42.0 days (IQR 75.0) for the medium-risk group (2.0–5.5 points), and 15.0 days (IQR 28.0) for the high-risk group (6.0–10.5 points).ConclusionsPROgnostic Model for Advanced Cancer (PRO-MAC) takes into account patient and disease-related factors and identify high-risk patients with 90-day mortality. PPS V.2 and ESASr are important predictors. PRO-MAC will help physicians identify patients earlier for supportive care, facilitating multidisciplinary, shared decision-making.

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Circulating Plasma Cells as a Biomarker to Predict Newly Diagnosed Multiple Myeloma Prognosis: Developing Nomogram Prognostic Models

Frontiers in Oncology ◽

10.3389/fonc.2021.639528 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qianwen Cheng ◽

Li Cai ◽

Yuyang Zhang ◽

Lei Chen ◽

Yu Hu ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Plasma Cells ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Newly Diagnosed ◽

Training Cohort ◽

Entire Cohort

Background: To investigate the prognostic value of circulating plasma cells (CPC) and establish novel nomograms to predict individual progression-free survival (PFS) as well as overall survival (OS) of patients with newly diagnosed multiple myeloma (NDMM).Methods: One hundred ninetyone NDMM patients in Wuhan Union Hospital from 2017.10 to 2020.8 were included in the study. The entire cohort was randomly divided into a training (n = 130) and a validation cohort (n = 61). Univariate and multivariate analyses were performed on the training cohort to establish nomograms for the prediction of survival outcomes, and the nomograms were validated by calibration curves.Results: When the cut-off value was 0.038%, CPC could well distinguish patients with higher tumor burden and lower response rates (P < 0.05), and could be used as an independent predictor of PFS and OS. Nomograms predicting PFS and OS were developed according to CPC, lactate dehydrogenase (LDH) and creatinine. The C-index and the area under receiver operating characteristic curves (AUC) of the nomograms showed excellent individually predictive effects in training cohort, validation cohort or entire cohort. Patients with total points of the nomograms ≤ 60.7 for PFS and 75.8 for OS could be defined as low-risk group and the remaining as high-risk group. The 2-year PFS and OS rates of patients in low-risk group was significantly higher than those in high-risk group (p < 0.001).Conclusions: CPC is an independent prognostic factor for NDMM patients. The proposed nomograms could provide individualized PFS and OS prediction and risk stratification.

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A Risk Signature with Immune and Inflammatory Cells Infiltration in Gastric Cancer Predicts Survival and Efficiency of Chemotherapy

10.21203/rs.3.rs-30024/v1 ◽

2020 ◽

Author(s):

Sen Li ◽

Wenpeng Wang ◽

Pengfei Ma ◽

Junli Zhang ◽

Yanghui Cao ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Adjuvant Chemotherapy ◽

T Lymphocytes ◽

Risk Stratification ◽

Validation Cohort ◽

Risk Group ◽

Inflammatory Cells ◽

High Risk Group ◽

Training Cohort

Abstract Background In order to accurately predict outcomes of gastric cancer (GC), we developed a risk signature with tumor infiltration immune and inflammatory cells for prognosis.Methods A risk signature model in combination with CD66b + neutrophils, CD3 + T, CD8 + T lymphocytes, and FOXP3 + regulatory T cells was developed in a training cohort of 327 GC patients undergoing surgical resection between 2011 and 2012, and validated in a validation cohort of 285 patients from 2012 to 2013.Results High CD66b expression predicted poor disease-special survival (DSS) as well as inversely correlated with CD8 (P < 0.05) and FOXP3 expression (P < 0.05) in the training cohort, comparable disease-free survival (DFS) findings were observed in the validation cohort.Furthermore, a risk stratification was developed from integration of CD66b + neutrophils and T immune cells. In both DFS and DSS, the high-risk group all demonstrated worse prognosis than low-risk group in both the training cohort and the validation cohort (all P < 0.05). In addition, the high-risk group was associated with post-operative relapses. Furthermore, this risk signature model increase the predictive accuracy and efficiency for post-operative relapses. At last, the high-risk group identified a subgroup of GC patients who tend to not benefit from adjuvant chemotherapy.Conclusions Incorporation of neutrophils into T lymphocytes could provide more accurate prognostic information in GC, and this risk stratification predicted survival benefit from post-operative adjuvant chemotherapy in GC.

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A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.625452 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zijun Xu ◽

Lijuan Xu ◽

Liping Liu ◽

Hai Li ◽

Jiewen Jin ◽

...

Keyword(s):

High Risk ◽

Biochemical Recurrence ◽

Risk Group ◽

Predictive Biomarkers ◽

Gene Signature ◽

Low Risk ◽

Free Survival ◽

Training Cohort ◽

Increased Risk ◽

Robust Likelihood

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

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Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma

BMC Cancer ◽

10.1186/s12885-021-08987-y ◽

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.

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Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.621232 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiwen Wu ◽

Tian Lan ◽

Muqi Li ◽

Junfeng Liu ◽

Xukun Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Score ◽

Validation Cohort ◽

Risk Group ◽

Cancer Genome ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Training Cohort

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients.Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. A high-risk group and low-risk group were separated for further study depending on their median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort.Results: A total of 548 differential mRNAs were identified from HCC samples (n = 374) and normal controls (n = 50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender.Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.

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