Stepwise Multivariable Cox Analysis Research Articles

Prognostic value of low skeletal muscle mass in patient treated by exclusive curative radiochemotherapy for a NSCLC

Low skeletal muscle mass is a well-known prognostic factor for patients treated for a non-small-cell lung cancer by surgery or chemotherapy. However, its impact in patients treated by exclusive radiochemotherapy has never been explored. Our study tries to evaluate the prognostic value of low skeletal muscle mass and other antropometric parameters on this population. Clinical, nutritional and anthropometric date were collected for 93 patients treated by radiochemotherapy for a NSCLC. Anthropometric parameters were measured on the PET/CT by two methods. The first method was a manual segmentation at level L3, used to define Muscle Body Area (MBAL3), Visceral Fat Area (VFAL3) and Subcutaneous Fat Area (SCFAL3). The second method was an software (Anthropometer3D), allowing an automatic multislice measurement of Lean Body Mass (LBMAnthro3D), Fat Body Mass (FBMAnthro3D), Muscle Body Mass (MBMAnthro3D), Visceral Fat Mass (VFMAnthro3D), and Sub-Cutaneous Fat Mass (SCFMAnthro3D) on the PET/CT. All anthropometrics parameters were normalised by the patient's height. The primary end point was overall survival time. Univariate and then stepwise multivariate cox analysis were performed for significant parameters. Finally, Spearman's correlation between MBAL3 and MBMAnthro3D was assessed. Forty-one (44%) patients had low skeletal muscle mass. The median overall survival was 18 months for low skeletal muscle mass patients versus 36 months for non-low skeletal muscle mass patients (p = 0.019). Low skeletal muscle mass (HR = 1.806, IC95% [1.09–2.98]), serums albumin level < 35 g/l (HR = 2.203 [1.19–4.09]), Buzby Index < 97.5 (HR = 2.31 [1.23–4.33]), WHO score = 0 (HR = 0.59 [0.31–0.86] and MBMAnthro3D < 8.56 kg/m2 (HR = 2.36 [1.41–3.90]) were the only significant features in univariates analysis. In the stepwise multivariate Cox analysis, only MBMAnthro3D < 8.56 kg/m2 (HR = 2.16, p = 0.003) and WHO score = 0 (HR = 0.59, p = 0.04) were significant. Finally, muscle quantified by MBAL3 and MBMAnthro3D were found to be highly correlated (Spearman = 0.9). Low skeletal muscle mass, assessed on the pre-treatment PET/CT is a powerful prognostic factor in patient treated by radiochemotherapy for a NSCLC. The automatic software Anthropometer3D can easily identify patients a risk that could benefit an adapted therapy.

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.

Comprehensive molecular analyses of a TNF family-based signature with regard to prognosis, immune features, and biomarkers for immunotherapy in lung adenocarcinoma.

BackgroundTumour Necrosis Factor (TNF) family members play important roles in mounting anti-tumour immune responses, and clinical trials targeting these molecules are ongoing. However, the expression patterns and clinical significance of TNF members in lung adenocarcinoma (LUAD) remain unrevealed. This study aimed to explore the gene expression profiles of TNF family members in LUAD and constructed a TNF family-based prognosis signature.MethodsIn total, 1300 LUAD cases from seven different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and the RNA data from five Gene Expression Omnibus (GEO) datasets and qPCR data from 102 samples were used for validation. The immune profiles and potential immunotherapy response prediction value of the signature were also explored.FindingsAfter univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a TNF family-based signature was constructed in the TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of OS. This signature remained an independent prognostic factor in multivariate analyses. Moreover, the clinical significance of the signature was well validated in different clinical subgroups and independent validation cohorts. Further analysis revealed that signature high-risk patients were characterized by distinctive immune cell proportions and immune-suppressive states. Additionally, signature scores were positively related to multiple immunotherapy biomarkers.InterpretationThis was the first TNF family-based model for predicting outcomes and immune landscapes for patients with LUAD. The capability of this signature for predicting immunotherapy response needs further validation.

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database.

BackgroundThe incidence of young patients with pancreatic cancer (PC) is on the rise, and there is a lack of models that could effectively predict their prognosis. The purpose of this study was to construct nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of young patients with PC.MethodsPC patients younger than 50 years old from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected and randomly divided into training set and validation set. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent factors affecting OS. The Fine and Gray competing risk regression model was used to determine the independent factors affecting CSS. We used significant variables in the training set to construct nomograms predicting prognosis. The discrimination and calibration power of models were evaluated by concordance index (C-index), calibration curve and 10-flod cross-validation.ResultsA total of 4,146 patients were selected. Multivariable Cox analysis showed that gender, race, grade, pathological types, AJCC stage and surgery were independent factors affecting OS. The C-index of the nomogram predicting OS in training and validation was 0.733 (average = 0.731, 95% CI [0.724–0.738]) and 0.742 (95% CI [0.725–0.759]), respectively. Competing risk analysis showed that primary site, pathological types, AJCC stage and surgery were independent factors affecting CSS. The C-index of the nomogram predicting CSS in training and validation set was 0.792 (average = 0.765, 95% CI [0.742–0.788]) and 0.776 (95% CI [0.773–0.779]), respectively. C-index based on nomogram was better in training and validation set than that based on AJCC stage. Calibration curves showed that these nomograms could accurately predict the 1-, 3- and 5-year OS and CSS both in training set and validation set.ConclusionsThe nomograms could effectively predict OS and CSS in young patients with PC, which help clinicians more accurately and quantitatively judge the prognosis of individual patients.

A TNF Family-Based Signature Reveals Significantly Different Prognoses and Immunotherapy Response in Patients with Lung Adenocarcinoma

Tumor Necrosis Factor (TNF) family members play important roles in mounting anti-tumor immune responses, and clinical trials targeting these molecules are ongoing. However, the expression patterns and clinical significance of TNF members in lung adenocarcinoma (LUAD) remain unrevealed. This study involved an integrative analysis of the gene expression profiles of TNF family members in LUAD and constructed a TNF family-based prognosis signature. In total, 1300 LUAD cases from seven different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and the RNA data from five Gene Expression Omnibus (GEO) datasets and qPCR data from 102 samples were used for validation. The immune profiles and immunotherapy response prediction value of the signature were also explored. After univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a TNF family-based signature was constructed in the TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of OS. This signature remained an independent prognostic factor in multivariate analyses. Moreover, the clinical significance of the signature was well validated in different clinical subgroups and independent validation cohorts.Further analysis revealed that signature high-risk patients were characterized by distinctive immune cell proportions and immune-suppressive states. Importantly, signature high-risk cases were considered appropriate candidates for immunotherapy. Collectively, this was the first, reliable, TNF family-based prognostic model for predicting outcomes and immunotherapy response, which may emerge as a clinically useful tool for better prognostic management and increased precision in the application of immunotherapy for LUAD patients. Funding Statement: This work was supported by the CAMS Innovation Fund for Medical Sciences (2017-I2M-1-005, 2016-I2M-1-001), the National Key R&D Program of China (2016YFC1303201), the National Natural Science Foundation of China (81802299, 81502514), the Fundamental Research Funds for the Central Universities (3332018070), the National Key Basic Research Development Plan (2018YFC1312105). Declaration of Interests: The authors declare that they have no conflicts of interest. Ethics Approval Statement: This study was approved by the Institutional Review Boards of The First Affiliated Hospital of Zhengzhou University.

A Novel Prognostic Signature of mRNA-lncRNA in Breast Cancer.

Comprehensive genomic testing will be required to identify appropriate targets for the precision therapy of breast cancer. Although RNA sequencing (RNA-seq) is an unparalleled platform for this purpose, existing molecular-based prognostic signatures are not optimal for RNA-seq data. In this study, we analyzed RNA-seq datasets to generate a novel prognostic gene signature for breast cancer patients. RNA-seq and clinical datasets from breast cancer patients were obtained from The Cancer Genome Atlas and randomly assigned to training (n = 379) and test (n = 378) cohorts. Using the training cohort, sequential univariate Cox analysis, robust likelihood-based survival analysis, and stepwise multivariable Cox analysis identified a five-gene signature composed of one long noncoding RNA gene and four protein-coding genes. The five-gene signature was then used to dichotomize patients into risk groups and validated using Kaplan-Meier and multivariable Cox analyses. In the full test cohort, the high-risk group had worse overall survival (hazard ratio [HR] = 4.74, 95% confidence interval [CI] = 2.33-9.64, p < 0.0001) and worse relapse-free survival (HR = 2.26, 95% CI = 1.11-4.61, p = 0.024) than the low-risk group. Similarly, overall survival was worse in the high-risk group within nearly all clinically important subsets, including early stage disease (I/II) (HR = 7.87, 95% CI = 3.69-16.77, p < 0.0001), and luminal A (HR = 4.23, 95% CI = 1.11-16.12, p = 0.034), luminal B (HR = 12.79, 95% CI = 2.74-59.69, p = 0.001), and basal (HR = 18.11, 95% CI = 3.21-102.05, p = 0.001) subtypes. Notably, the five-gene signature exhibited superior prognostic performance compared with the Oncotype DX 21-gene signature. This novel five-gene signature may therefore be a powerful prognostic tool for personalized treatment of breast cancer patients as part of an integrated RNA-seq clinical sequencing program.

The Autophagy-Related Long Non-Coding RNA Signature for Acute Myeloid Leukemia

Background: LncRNA plays the complex functions in the regulation of autophagy in cancers. However, the effect of autophagy-related lncRNA on the prognosis of AML remains unclear. Methods: Autophagy-related genes and lncRNA profiles were integrated based on TCGA and HADb dataset. autophagy-related lncRNAs were screened out by co-expression analysis, and an autophagy-related lncRNA signature was developed by using univariate Cox analysis followed by stepwise multivariate Cox analysis. Log-rank statistical method and the time-dependent receiver operating characteristic curve were used to assess the efficiency of the model predictions. GSEA was used for biological functional annotation and pathways analysis. Findings: Six lncRNAs, HYMAI, MIR155HG, MGC12916, DIRC3, C1orf220 and HCP5 were final screened to construct an autophagy-related lncRNA signature. Patients with high-risk had significantly shorter OS than low-risk patients (median OS, 0.8137 years vs 3.847 years, p=1.912e-07). The area under the curve of risk score was 0.746, which was much higher than other adverse prognostic factors indicated well accuracy of prediction. The results of GSEA indicated that proteasome-related pathways were more active in high-risk group. Interpretation: Our proposed autophagy-related lncRNA signature has an important prognostic value, and may provide a promising tool for improving the development of AML risk stratification and personalized treatment. Funding: National Natural Science Foundation of China (81770172); Jiangsu Provincial Special Program of Medical Science (BE2017747); Jiangsu Province 333 project (BRA2019103); The Fundamental Research Funds for the Central Universities (2242019K3DZ02); Milstein Medical Asian American Partnership Foundation Research Project Award in Hematology (2017); Key Medical of Jiangsu Province (ZDXKB2016020). Declaration of Interests: The authors declare that they have no competing interests.

Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods.

BackgroundTo identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME).Methods and materialsTranscriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein‐protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database.ResultsGene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan‐Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA‐DOA was significantly related with immune cells infiltration.ConclusionWe screened a list of TME‐related genes which predict poor survival outcomes in ACC patients from TCGA database.

A novel RNA sequencing-based risk score model to predict papillary thyroid carcinoma recurrence.

The papillary thyroid carcinoma (PTC) usually shows an excellent prognosis. But some patients suffer recurrence after treatment. Recent progress in RNA sequencing (RNA-Seq) allows us to explore whole-transcriptomic gene expression profiles to develop RNA-seq based predictive model for stratifying the risk of recurrence of PTC. RNA-seq and clinical data from The Cancer Genome Atlas thyroid carcinoma cohort were divided chronologically into a training cohort (before 2011, n = 240) and a validation cohort (after 2011, n = 239). A risk score model was developed in training cohort using univariate Cox analysis followed by stepwise multivariate Cox analysis, and assessed in the validation cohort. Univariate and multivariate analyses identified five independent predictive genes (TOP2A, RP11-180M15.7, RP11-635N19.1, PROSER3, and TMEM139) significantly (p < 0.05) associated with recurrence-free survival of PTC. The proposed risk score model defined by these five genes was able to divide patients into high-risk and low-risk groups with significantly different recurrence risk in both training cohort [hazard ratio (HR) 6.62, 95% confidence interval (CI) 3.16-13.86] and validation cohort [HR 3.40, 95% CI1.29-8.94). Furthermore, the model is independent of clinicopathologic factors and demonstrated better predictive performance than other clinical covariates in PTC patients with no distant metastasis. Our results indicate that TOP2A, RP11-180M15.7, RP11-635N19.1, PROSER3, and TMEM139 are potential independent predictive genes for the recurrence of PTC and the proposed RNA sequencing-based model has significant predictive value and may improve recurrence risk stratification for early-stage PTC patients.

Development and validation of a five-lncRNA signature with prognostic value in colon cancer.

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients' overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set.After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.

Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer.

Background:Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC).Methods:Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis.Results:After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction.Conclusions:Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

A Robust 8-Gene Prognostic Signature for Early-Stage Non-small Cell Lung Cancer.

Background: The current staging system is imprecise for prognostic prediction of early-stage non–small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision.Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference.Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision.Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma.

The current study aimed to identify the potential clinical significance and molecular mechanisms of kinesin (KIF) family member genes in lung adenocarcinoma (LUAD) using genome-wide RNA sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) database. Clinical parameters and RNA-seq data of patients with LUAD from the TCGA database enabled the assessment of the clinical significance of KIF genes, while the potential mechanisms of their interactions in LUAD were investigated by gene set enrichment analysis (GSEA). A gene signature with potential prognostic value was constructed via a stepwise multivariable Cox analysis. In total, 23 KIF genes were identified to be differentially expressed genes (DEGs) between the LUAD tumor and adjacent non-cancerous tissues. Of these, 8 differentially expressed KIF genes were strongly found to be strongly associated with the overall survival of patients with LUAD. Three of these genes were found to be able to be grouped as a potential prognostic gene signature. Patients with higher risk scores calculated using this gene signature were found to have a markedly higher risk of mortality (adjusted P=0.003; adjusted HR, 1.576; 95% CI, 1.166–2.129). Time-dependent receiver operating characteristic analysis indicated that this prognostic signature was able to accurately predict patient prognosis with an area under curve of 0.636, 0.643,0.665, 0.670 and 0.593 for the 1-, 2-, 3-, 4- and 5-year survival, respectively. This prognostic gene signature was identified as an independent risk factor for LUAD and was able to more accurately predict prognosis in comparison to other known clinical parameters, as shown via comprehensive survival analysis. GSEA enrichment revealed that that KIF14, KIF18B and KIF20A mediated basic cell physiology through the regulation of the cell cycle, DNA replication, and DNA repair biological processes and pathways. On the whole, the findings of this study identified 23 KIF genes that were DEGs between LUAD tumor and adjacent non-cancerous tissues. In total, 8 of these genes had the potential to function as prognostic and diagnostic biomarkers in patients with LUAD.

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas

BackgroundPrognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures.ResultsSomatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis.ConclusionsWe demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.

A B7-CD28 family based signature demonstrates significantly different prognoses and tumor immune landscapes in lung adenocarcinoma.

B7 family ligands and CD28 family receptors have complicated interaction for modulating immune functions. They play a central role in response to immunotherapy and outcome of patients with lung adenocarcinoma (LUAD). Thus, we analyzed B7-CD28 family gene expression profiles in LUAD and generated a signature to predict prognosis and immune host status. B7-CD28 family gene expression profiles and clinical data of LUAD from The Cancer Genome Atlas (TCGA) were analyzed. In the training cohort, prognostic association was assessed and then a prognostic signature was built with stepwise multivariable Cox analysis. The signature was validated by Kaplan-Meier and multivariable Cox analysis in several published gene expression datasets and a Fudan University cohort. Expression of immune cell populations and other immunotherapy predictors was further investigated. In TCGA LUAD cohort, eight B7-CD28 family genes had prognostic association with p values <0.05. Stepwise regression generated a gene signature including two genes, CD28 and CD276. Signature high-risk cases had worse overall survival (OS) and disease-free survival (DFS) in three published gene expression datasets and a Fudan University validation cohort. The B7-CD28 family based signature also significantly stratified OS and DFS in important clinical subsets, including stage I-II and EGFR mutant subsets. Signature high- and low-risk tumor had significantly different expressions of PD-L1 and tumor infiltrating leukocytes. The B7-CD28 family based signature demonstrates significantly different prognoses and tumor immune landscapes in LUAD. Whether it could serve as potential biomarkers for immunotherapy needs further investigation.

Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Survival in Patients with Hepatocellular Carcinoma

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

Higher reticulocyte counts are associated with higher mortality rates in hemodialysis patients: a retrospective single-center cohort study

BackgroundWe assessed laboratory data related to mortality in hemodialysis (HD) patients. In our preliminary study, we examined all of the data for HD outpatients in our facility according to whether the patient had survived. A statistically significant difference was observed for the reticulocyte count, which has not previously been considered a prognostic factor. We subsequently verified the relationship between all-cause and cardiovascular mortality and reticulocyte count.MethodsWe retrospectively analyzed the data of 358 hemodialysis outpatients who were followed up for an average of 41.4 months. The patients were divided into quartiles according to the reticulocyte count levels.ResultsHigher reticulocyte counts were associated with female gender, an increase in interdialytic body weight gain, serum erythropoietin level, white blood cell count, and increased levels of lactate dehydrogenase, inorganic phosphorus, non-high-density lipoprotein, and glucose. As compared with patients in the lowest quartile, those in the highest quartile showed significantly higher adjusted hazard ratios (HRs) for all-cause (HR 3.12; 95% confidence interval (CI) 1.26 to 7.74) and cardiovascular (HR 4.93; 95% CI 1.24 to 19.56) mortality. For every 104 cells/μL increment in the reticulocyte count, the adjusted HRs for all-cause and cardiovascular mortality were 1.33 (95% CI 1.17 to 1.51) and 1.38 (95% CI 1.17 to 1.63), respectively. The association of reticulocyte count with all-cause and cardiovascular mortality was independent of other prognostic factors. Stepwise multivariable Cox analysis indicated that only age showed stronger association with all-cause mortality than reticulocyte count. Regarding cardiovascular mortality, reticulocyte count was found as the strongest progenitor. We also examined the relationship between the reticulocyte count and the temporal hemoglobin trend (a slope of changes in hemoglobin levels over time). A statistically significant negative correlation was found.ConclusionsHigher reticulocyte counts were associated with higher mortality. We speculate that this result reflects tissue hypoxia, which results in a higher erythropoietin level, or a compensatory erythropoietic response due to the accelerated clearance of erythrocytes. Prospective studies are warranted to confirm our findings.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature. Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided. In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures. Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Efficacy of interferon-beta plus ribavirin combination treatment on the development of hepatocellular carcinoma in Japanese patients with chronic hepatitis C.

Although there is much evidence of an antitumor effect of pegylated interferon (IFN)-α-based treatment, limited data is available about that of IFN-β-based treatment. Our goal was to evaluate the impact of IFN-β plus ribavirin (RBV) treatment on the suppression of hepatocellular carcinoma (HCC). This retrospective, multicenter study consisted of 124 chronic hepatitis C patients who were treated with IFN-β plus RBV treatment, including 61 with advanced fibrosis and five with pretreatment HCC. All participants were followed for a median of 2.8 years (range, 2.2-3.2) after the end of their antiviral treatment. The data of 112 patients who finished the treatment were available for analysis. Cox proportional hazard analyses were performed to determine factors significantly associated with HCC development. Cumulative incidence curves for HCC were plotted using the Kaplan-Meier method and differences between groups were assessed using the log-rank test. The 2.9% rate of HCC development of patients with sustained virological response (SVR) was significantly lower (P = 0.027) than the 15.9% of non-SVR patients. Interestingly, no significant difference was observed between the rates of HCC development of patients with and without advanced fibrosis (P = 0.733), even though the SVR rate of patients with advanced fibrosis was significantly lower than that of those without advanced fibrosis (P < 0.001). Stepwise multivariable Cox analysis extracted that only SVR was significantly associated with HCC development (hazard ratio, 0.20; 95% confidence interval, 0.03-0.84, P = 0.027). SVR was significantly associated with a lower risk of HCC development after IFN-β plus RBV treatment.

Predictors of Long-Term Outcomes in Patients With Significant Myxomatous Mitral Regurgitation Undergoing Exercise Echocardiography

Significant myxomatous mitral regurgitation leads to progressive left ventricular (LV) decline, resulting in congestive heart failure and death. Such patients benefit from mitral valve surgery. Exercise echocardiography aids in risk stratification and helps decide surgical timing. We sought to assess predictors of outcomes in such patients undergoing exercise echocardiography. This is an observational study of 884 consecutive patients (age, 58 ± 14 years; 67% men) with grade III+ or greater myxomatous mitral regurgitation who underwent exercise echocardiography between January 2000 and December 2011 (excluding functional mitral regurgitation, prior valvular surgery, hypertrophic cardiomyopathy, rheumatic valvular disease, or greater than mild mitral stenosis). Clinical and echocardiographic data (mitral regurgitation, LV ejection fraction, LV dimensions, right ventricular systolic pressure) and exercise variables (metabolic equivalents, heart rate recovery at 1 minute after exercise) were recorded. Composite events of death, myocardial infarction, stroke, and progression to congestive heart failure were recorded. Mean LV ejection fraction, indexed LV end-systolic dimension, resting right ventricular systolic pressure, peak stress right ventricular systolic pressure, metabolic equivalents achieved, and heart rate recovery were 58 ± 5%, 1.6 ± 0.4 mm/m(2), 31 ± 12 mm Hg, 46 ± 17 mm Hg, 9.6 ± 3, and 33 ± 14 beats, respectively. During 6.4 ± 4 years of follow-up, there were 87 events. On stepwise multivariable Cox analysis, percent of age/sex-predicted metabolic equivalents (hazard ratio, 0.99; 95% confidence interval, 0.98-0.99; P=0.005), heart rate recovery (hazard ratio, 0.29; 95% confidence interval, 0.17-0.50; P<0.001), resting right ventricular systolic pressure (hazard ratio, 1.03; 95% confidence interval, 1.004-1.05; P=0.02), atrial fibrillation (hazard ratio, 1.91; 95% confidence interval, 1.07-3.41; P=0.03), and LV ejection fraction (hazard ratio, 0.96; 95% confidence interval, 0.92-0.99; P=0.04) predicted outcomes. In patients with grade III+ or greater myxomatous mitral regurgitation undergoing exercise echocardiography, lower percent of age/sex-predicted metabolic equivalents, lower heart rate recovery, atrial fibrillation, lower LV ejection fraction, and high resting right ventricular systolic pressure predicted worse outcomes.