Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

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Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

Frontiers in Neurology ◽

10.3389/fneur.2021.724571 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yajie Qi ◽

Yingqi Xing ◽

Lijuan Wang ◽

Jie Zhang ◽

Yanting Cao ◽

...

Keyword(s):

Transcranial Doppler ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Survival Duration ◽

Quantitative Electroencephalography ◽

Prognosis Prediction ◽

Prognosis Prediction Model ◽

Gcs Score ◽

Hemispheric Infarction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.

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Screening and discrimination of optimal prognostic genes for pancreatic cancer based on a prognostic prediction model

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab296 ◽

2021 ◽

Author(s):

Zhiqin Chen ◽

Haifei Song ◽

Xiaochen Zeng ◽

Ming Quan ◽

Yong Gao

Keyword(s):

Pancreatic Cancer ◽

Prediction Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Molecular Therapy ◽

Clinical Factors ◽

Cox Regression Analysis ◽

Gene Set ◽

Prognosis Prediction ◽

Prognosis Prediction Model

Abstract The prognosis of pancreatic cancer is poor because patients are usually asymptomatic in the early stage and the early diagnostic rate is low. Therefore, in this study, we aimed to identify potential prognosis-related genes in pancreatic cancer to improve diagnosis and the outcome of patients. The mRNA expression profile data from The Cancer Genome Atlas database and GSE79668, GSE62452, and GSE28735 datasets from Gene Expression Omnibus were downloaded. The prognosis-relevant genes and clinical factors were analyzed using Cox regression analysis and the optimal gene sets were screened using the Cox proportional model. Next, the Kaplan-Meier survival analysis was used to evaluate the relationship between risk grouping and patient prognosis. Finally, an optimal gene-based prognosis prediction model was constructed and validated using a test dataset to discriminate the model accuracy and reliability. The results showed that 325 expression variable genes were identified, and 48 prognosis-relevant genes and three clinical factors, including lymph node stage (pathologic N), new tumor, and targeted molecular therapy were preliminarily obtained. In addition, a gene set containing 16 optimal genes was identified and included FABP6, MAL, KIF19, and REG4, which were significantly associated with the prognosis of pancreatic cancer. Moreover, a prognosis prediction model was constructed and validated to be relatively accurate and reliable. In conclusion, a gene set consisting of 16 prognosis-related genes was identified and a prognosis prediction model was constructed, which is expected to be applicable in the clinical diagnosis and treatment guidance of pancreatic cancer in the future.

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Identification of prognostic alternative splicing events in sarcoma

Scientific Reports ◽

10.1038/s41598-021-94485-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hongshuai Li ◽

Jie Yang ◽

Guohui Yang ◽

Jia Ren ◽

Yu Meng ◽

...

Keyword(s):

Alternative Splicing ◽

Cox Regression ◽

Univariate Analysis ◽

Area Under The Curve ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Functional Roles ◽

Cancer Pathogenesis ◽

Rare Malignancy

AbstractSarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

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Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Frontiers in Surgery ◽

10.3389/fsurg.2021.665115 ◽

2021 ◽

Vol 8 ◽

Author(s):

Daojun Lv ◽

Zanfeng Cao ◽

Wenjie Li ◽

Haige Zheng ◽

Xiangkun Wu ◽

...

Keyword(s):

Prostate Cancer ◽

Survival Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

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Identiﬁcation and Validation of Two Prognostic Autophagy-related Models in Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-78750/v1 ◽

2020 ◽

Author(s):

Xiong-Lin Sun ◽

Jun-Ming Zhu ◽

Fei Lin ◽

Zhi-Bin Ke ◽

Xiao-Dong Li ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Regression Models ◽

Risk Model ◽

Cox Regression ◽

Clear Cell ◽

Risk Group ◽

Gene Model ◽

Cell Renal Cell Carcinoma ◽

Training Cohort ◽

Model Training

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is one of the most frequent malignancies. Increasing evidence has highlighted the critical roles of autophagy-related genes and autophagy-related long non-coding RNA (lncRNA) in ccRCC development and progression. Therefore, it is necessary to identify novel biomarkers associated with autophagy in ccRCC.Methods: A total of 507 ccRCC patients were included in our study and then randomly divided into a training cohort (n=255) and testing cohort (n=252). Univariate Cox regression models, Lasso regression analyses and multivariate Cox regression models were successively used for constructing gene model and lncRNA model. Receiver-operating characteristic (ROC) curve analysis, Kaplan-Meier (K-M) analysis and more functional analyses were applied for verifying the accuracy of the two models.Results: The autophagy-related genes (ARGs) prognostic model was constructed based on the six ARGs (EIF4EBP1, IFNG, BID, BIRC5, CX3CL1 and RAB24) and five autophagy-related lncRNAs (AC093278.2, AC010326.3, AC099850.3, AC016773.1 and AC009549.1) and then ccRCC patients were significantly stratified into high- and low-risk groups in terms of overall survival (OS). K-M survival analyses indicated that low-risk group had a lower mortality rate than high-risk group in the six-gene prognostic risk model (training cohort: P=4.138e-07; testing cohort: P=1.125e-03) and the same results were obtained in the case of the five-lncRNA prognostic risk model (training cohort: P=4.564e-09; testing cohort: P=2.485e-03). The results of time-dependent ROC curves revealed six-gene prognostic risk model had a higher area under curve (AUC) of 0.765 than the five-lncRNA prognostic risk model at an AUC of 0.759. Therefore, the gene model is an indicator as good as the model constructed by lncRNAs. In addition, further functional analysis indicated these genes were functionally involved in regulation of endopeptidase activity, regulation of peptidase activity, autophagy, human cytomegalovirus infection, shigellosis, autophagy-animal and HIF-1 signaling pathway.Conclusions: A total of six OS-related ARGs and five autophagy-related lncRNA were identified in our current study. The two autophagy-related prediction models including genes and lncRNAs are reliable prognostic and predictive biomarkers for ccRCC.

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Inflammatory markers in cerebrospinal fluid: independent prognostic biomarkers in amyotrophic lateral sclerosis?

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2018-319586 ◽

2019 ◽

pp. jnnp-2018-319586 ◽

Cited By ~ 7

Author(s):

Benjamin Gille ◽

Maxim De Schaepdryver ◽

Lieselot Dedeene ◽

Janne Goossens ◽

Kristl G Claeys ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Cerebrospinal Fluid ◽

Disease Progression ◽

Inflammatory Markers ◽

Cox Regression ◽

Characteristic Curve ◽

Progression Rate ◽

Cox Regression Analysis ◽

Lateral Sclerosis ◽

Discriminatory Performance

ObjectiveInflammation is a key pathological hallmark in amyotrophic lateral sclerosis (ALS), which seems to be linked to the disease progression. It is not clear what the added diagnostic and prognostic value are of inflammatory markers in the cerebrospinal fluid (CSF) of patients with ALS.MethodsChitotriosidase-1 (CHIT1), chitinase-3-like protein 1 (YKL-40) and monocyte chemoattractant protein-1 (MCP-1) were measured in CSF and serum of patients with ALS (n=105), disease controls (n=102) and patients with a disease mimicking ALS (n=16). The discriminatory performance was evaluated by means of a receiver operating characteristic curve analysis. CSF and serum levels were correlated with several clinical parameters. A multivariate Cox regression analysis, including eight other established prognostic markers, was used to evaluate survival in ALS.ResultsIn CSF, CHIT1, YKL-40 and MCP-1 showed a weak discriminatory performance between ALS and ALS mimics (area under the curve: 0.79, p<0.0001; 0.72, p=0.001; 0.75, p=0.001, respectively). CHIT1 and YKL-40 correlated with the disease progression rate (ρ=0.28, p=0.009; ρ=0.34, p=0.002, respectively). CHIT1 levels were elevated in patients with a higher number of regions displaying motor neuron degeneration (one vs three regions: 4248 vs 13 518 pg/mL, p = 0.0075). In CSF, YKL-40 and MCP-1 were independently associated with survival (HR: 29.7, p=0.0003; 6.14, p=0.001, respectively).ConclusionsOur findings show that inflammation in patients with ALS reflects the disease progression as an independent predictor of survival. Our data encourage the use of inflammatory markers in patient stratification and as surrogate markers of therapy response in clinical trials.

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Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096357

Author(s):

Xiaoyong Gong ◽

Bobin Ning

Keyword(s):

Prostate Cancer ◽

Network Analysis ◽

Cox Regression ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Normal Prostate ◽

Cox Regression Analysis ◽

Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

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A Novel Prognostic Risk Score Based on Ferroptosis-related LncRNAs Predicting Ovarian Cancer Patient Survival

10.21203/rs.3.rs-1188209/v1 ◽

2021 ◽

Author(s):

Keyu Chen ◽

Xiaohong Li ◽

Caixia Qi

Keyword(s):

Ovarian Cancer ◽

Risk Score ◽

Cox Regression ◽

Characteristic Curve ◽

Therapeutic Targets ◽

Cancer Prognosis ◽

Cancer Tissue ◽

Clinical Implementation ◽

Tumour Immunity ◽

Cox Regression Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) are thought to be associated with several processes during cancer development and have been shown to be involved in the regulation of ferroptosis. Ovarian cancer is highly malignant tumour with a poor prognosis. The identification biomarkers with prognostic value in ovarian cancer may improve patient outcomes and can help to elucidate potential future therapeutic targets.Results: We report differential expression of 187 ferroptosis-related lncRNAs in normal and ovarian cancer tissue. Using univariate and multivariable Cox regression analysis, we identified four lncRNAs that were strongly associated with prognosis. We constructed a prognostic risk score based on these four lncRNAs which was effectively able to distinguish between low- and high-risk OC patients based on survival time. Univariate and multivariable Cox regression analyses and time-related receiver operating characteristic curve analyses revealed that this risk score represented an independent prognostic factor in patients with ovarian cancer. For clinical implementation, we developed a nomogram based on the prognostic feature and patient age. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the four ferroptosis-related lncRNAs were related to tumour immunity.Conclusions: we identify four novel ferroptosis-related lncRNAs as predictors of ovarian cancer prognosis and potential future therapeutic targets for ovarian cancer.

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Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis

PLoS ONE ◽

10.1371/journal.pone.0255744 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255744

Author(s):

Yan Lu ◽

Haoyang Guo ◽

Xuya Chen ◽

Qiaohong Zhang

Keyword(s):

Respiratory Failure ◽

Acute Respiratory Failure ◽

Medical Information ◽

Cox Regression ◽

Characteristic Curve ◽

Severe Heart Failure ◽

Albumin Ratio ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

All Cause Mortality

Previous studies have shown that lactate/albumin ratio (LAR) can be used as a prognostic biomarker to independently predict the mortality of sepsis and severe heart failure. However, the role of LAR as an independent prognostic factor in all-cause mortality in patients with acute respiratory failure (ARF) remains to be clarified. Therefore, we retrospectively analyzed 2170 patients with ARF in Medical Information Mart for Intensive Care Database III from 2001 to 2012. By drawing the receiver operating characteristic curve, LAR shows a better predictive value in predicting the 30-day mortality of ARF patients (AUC: 0.646), which is higher than that of albumin (AUC: 0.631) or lactate (AUC: 0.616) alone, and even higher than SOFA score(AUC: 0.642). COX regression analysis and Kaplan-Meier curve objectively and intuitively show that high LAR is a risk factor for patients with ARF, which is positively correlated with all-cause mortality. As an easy-to-obtain and objective biomarker, LAR deserves further verification by multi-center prospective studies.

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Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

10.21203/rs.3.rs-212352/v1 ◽

2021 ◽

Author(s):

Cheng Lijing ◽

Yuan Meiling ◽

Li Shu ◽

Chen Junjing ◽

Zhong Shupeng ◽

...

Keyword(s):

Overall Survival ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Individual Treatment ◽

Normal Brain ◽

Data Set ◽

Cox Regression Analysis ◽

Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

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