Disarming the guarded prognosis: predicting survival in newly referred patients with incurable cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Stockler MR
PROVIDER: S-EPMC2361107 | biostudies-other | 2006 Jan
REPOSITORIES: biostudies-other
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Project description:Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.
| S-EPMC10801949 | biostudies-literature
Project description:BACKGROUND:Brain metastases (BM) are a frequent complication of advanced cancer and are characterized by a variety of neurological symptoms. Although the presence of neurological symptoms is included in the response assessment in patients with primary brain tumors, to the authors' knowledge little is known regarding the prognostic impact of neurological symptoms in patients with BM. METHODS:Patients with newly diagnosed BM from non-small cell lung cancer were identified from the Vienna Brain Metastasis Registry and were evaluated according to the incidence, distribution, and prognostic impact of neurological symptoms at the time of diagnosis of BM. RESULTS:A total of 1608 patients (57.3% male and 42.7% female; median age, 62 years) were available for further analyses. Neurological symptoms including focal deficits (985 patients; 61.3%), signs of increased intracranial pressure (483 patients; 30.0%), epileptic seizures (224 patients; 13.9%), and neuropsychological symptoms (233 patients; 14.5%) were documented in 1186 of the 1608 patients (73.8%). Patients with asymptomatic BM presented with a longer median overall survival after the diagnosis of BM compared with patients with symptomatic BM (11 months vs 7 months; P < .001). In multivariate analysis with a diagnosis-specific graded prognostic assessment (hazard ratio, 1.41; 95% CI, 1.33-1.50 [P < .001]), the presence of neurological symptoms (hazard ratio, 1.39; 95% CI, 1.23-1.57 [P < .001]) was found to be independently associated with survival prognosis from the time of diagnosis of BM. CONCLUSIONS:Neurological symptoms at the time of BM diagnosis demonstrated a strong and independent association with survival prognosis. The results of the current study have highlighted the need for the integration of the presence of neurological symptoms into the prognostic assessment of patients with BM from non-small cell lung cancer. LAY SUMMARY:Neurological symptom evaluation is included regularly in the assessment of patients with primary brain tumors. However, to the authors' knowledge, little is known regarding the prognostic impact in patients with newly diagnosed brain metastases (BM). The current study has provided a detailed clinical characterization of the incidence, distribution, and prognostic impact of neurological symptoms in a large, real-life cohort of patients with BM from non-small cell lung cancer. In this cohort, neurological symptoms at the time of diagnosis of BM demonstrated a strong, independent prognostic impact on the survival prognosis. The results of the current study have highlighted the need for the integration of neurological symptom burden into the prognostic assessment of patients with BM from non-small cell lung cancer.
| S-EPMC7540353 | biostudies-literature
Project description:BackgroundSystemic inflammation measured by the neutrophil-to-lymphocyte ratio (NLR), leucocyte-to-lymphocyte ratio (LLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and CRP/albumin ratio (CRP/Alb) was shown to impact the survival prognosis in patients with extracranial solid cancer.MethodsOne thousand two hundred and fifty patients with newly diagnosed brain metastases (BM) were identified from the Vienna Brain Metastasis Registry.ResultsPLR and CRP/Alb were higher in patients with progressive extracranial disease and lower in patients with no evidence of extracranial disease. Lower NLR (cut-off = 5.07; 9.3 vs. 5.0 months), LLR (cut-off = 5.76; 10.0 vs. 5.3 months), PLR (cut-off = 335; 8.0 vs. 3.8 months), MLR (cut-off = 0.53; 6.0 vs. 3.5 months) and CRP/Alb (cut-off = 2.93; 8.5 vs. 3.7 months; padj < 0.05) were associated with longer overall survival (OS). In multivariate analysis with graded prognostic assessment (hazard ratio (HR) 1.45; 95% confidence interval (CI): 1.32-1.59; padj = 1.62e - 13), NLR (HR 1.55; 95% CI: 1.38-1.75; padj = 1.92e - 11), LLR (HR 1.57; 95% CI: 1.39-1.77; padj = 1.96e - 11), PLR (HR 1.60; 95% CI: 1.39-1.85; padj = 2.87955e - 9), MLR (HR 1.41; 95% CI: 1.14-1.75; padj = 0.027) and CRP/Alb (HR 1.83; 95% CI: 1.54-2.18; padj = 2.73e - 10) remained independent factors associated with OS at BM diagnosis.ConclusionsSystemic inflammation, measured by NLR, LLR, PLR, MLR and CRP/Alb, was associated with OS in patients with BM. Further exploration of immune modulating therapies is warranted in the setting of BM.
| S-EPMC8007827 | biostudies-literature
Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray
2011-01-20 | E-GEOD-26712 | biostudies-arrayexpress
Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population.
2011-01-20 | GSE26712 | GEO
Project description:Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.
| S-EPMC7957482 | biostudies-literature
Project description:Background: Previous studies about liver metastases (LM) in newly diagnosed ovarian cancer (NDOC) patients based on Surveillance, Epidemiology, and End Results (SEER) program disregarded selection bias of missing data. Methods: We identified Data of NDOC patients from SEER between 2010 and 2016, presented a comprehensive description of this dataset, and limited possible biases due to missing data by applying multiple imputation (MI). We determined predictive factors for underlying LM development in NDOC patients and evaluated prognostic factors in NDOC patients with LM (OCLM). We then established predictive nomograms, assessed by the concordance index, calibration curve, decision curve analysis (DCA), and clinical impact curves (CIC). Results: The amount of missing data for different variables in SEER dataset ranges from 0 to 36.11%. The results between complete dataset and MI datasets are similar. LM prevalence in NDOC patients was 7.18%, and median overall survival for OCLM patients was 11 months. The C-index of risk nomogram for LM development in the training cohort (TC) and validation cohort (VC) were 0.764 and 0.759, respectively. The C-index and integrated area under curve within five years of prognostic nomogram for OCLM patients in the TC and VC were 0.743 and 0.773, 0.714 and 0.733, respectively. For both nomograms, DCA revealed favorable clinical use and calibration curves suggested good consistency. Conclusion: The risk nomogram is expected to aid clinicians in identifying high-risk groups of LM development in NDOC patients for intensive screening. The prognostic nomogram could facilitate individualized prediction and stratification for clinical trials in OCLM patients.
| S-EPMC8734403 | biostudies-literature
Project description:Objective: The purpose of this study was to investigate multigene panel markers to predict long-term survival in patients with colon cancer. Methods and materials: GSE39582 was randomly divided into a training set and a validation set, while TCGA-COAD and GSE17536 were treated as two independent validation cohorts. Survival-associated genes were included in elastic net penalized Cox proportional hazards regression (ENCPH) model. Based on the results of the ENCPH, a multigene panel was constructed. We evaluated predictive performance of the multigene panel by univariate and multivariate survival analysis, and time-dependent ROC analysis. Results: A total of 1025 colon cancer patients were included in the study, and 94 genes were showed to be related with the overall survival of colon cancer patients, of which 7 genes were integrated to construct a multigene panel according to ENCPH model. The multigene panel could stratify colon cancer patients into notably different risk groups in the training set and three verification cohort. Results of multivariable CPH model suggested that the multigene panel was an independent prognostication factor. The multigene-containing nomogram showed reliable prediction ability on the 3- and 5-year survival of colon cancer patients with internally and externally validated C-indexes exceeded 0.7. Conclusion: The multigene panel we introduced showed considerable prognosis performance in colon cancer, and the multigene panel containing nomogram would help clinicians assess long-term survival probability.
| S-EPMC6909949 | biostudies-literature
Project description:PurposeFunctions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer.MethodsGene expressions were evaluated for breast cancer through The Cancer Genome Atlas (TCGA) database, and we calculated Pearson correlations to discover potential related lncRNAs. Differentially expressed genes were identified via criteria of FDR < 0.05 and |FC|> 2. Total samples were separated into training and validating sets randomly. Univariate Cox regression identified 14 prognostic lncRNAs in training set. A prognostic model was constructed to evaluate the accuracy in predicting prognosis. The univariate and multivariate Cox analysis were performed to verify whether lncRNA signature could be an independent prognostic factor The signature was validated in validating set. Immune infiltration levels were assessed.ResultsEighty-nine differentially expressed lncRNAs were identified from 420 Glycolysis-related lncRNAs. 14 lncRNAs were correlated with prognosis in training set and were selected to establish the prognostic model. Low risk group had better prognosis in both training (p= 9.025 e -10) and validating (p= 4.272 e -3) sets. The univariate and multivariate Cox analysis revealed that risk score of glycolysis-related lncRNAs (P< 0.001) was an independent prognostic factor in both training and validating sets. The neutrophils (p= 4.214 e -13, r=-0.223), CD4+ T cells (p= 1.833 e -20, r=-0.283), CD8+ T cells (p= 7.641 e -12, r=-0.211), B cells (p= 2.502 e -10, r=-0.195) and dendritic cells (p= 5.14 e -18, r=-0.265) were negatively correlated with risk score of prognostic model. The Macrophage (p= 0.016, r= 0.0755) was positively correlated with the risk score.ConclusionOur study indicated that glycolysis-related lncRNAs had a significant role to facilitate the individualized survival prediction in breast cancer patients, which would be a potential therapeutic target.
| S-EPMC9198763 | biostudies-literature
Project description:Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.
| S-EPMC7763234 | biostudies-literature