The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

T-cell receptors provide potential prognostic signatures for breast cancer

Although T-cell receptors (TCRs) are related to the progression of breast cancer (BC), their prognostic values remain unclear. We downloaded the messenger RNA (mRNA) profiles and corresponding clinical information of 1413 BC patients from the Cancer Genome Atlas and Gene Expression Omnibus database, respectively. The different expression analysis of 104 TCRs in BC samples was performed, and the consensus clustering based on 104 TCRs was performed by using the K-mean method of R language. Univariate cox regression analysis was used to screen TCRs significantly associated with the prognosis of BC, and LASSO Cox analysis was applied to optimize key TCRs. The risk score was calculated using the prognostic model constructed based on six optimal TCRs, and multivariate Cox regression analysis was used to determine whether it was an independent prognostic signature. Finally, the nomogram was constructed to predict the overall survival of BC patients. Six optimal TCRs (ZAP70, GRAP2, NFKBIE, IFNG, NFKBIA, and PAK5), which were favorable for the prognosis of BC patients, were screened. Risk score could reliably predict the prognosis of BC patients as an independent prognostic signature. In addition, when bringing into two independent prognostic signatures, age and risk score, the nomogram model could better predict the overall survival of BC patients. Our results suggested that the poor prognosis of BC patients with high risk might be due to an immunosuppressive microenvironment. In summary, a prognostic risk model based on six TCRs was established and could efficiently predict the prognosis of BC patients.     

Development and validation of a prognostic gene-expression signature for lung adenocarcinoma

Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.     

Lymph node ratio-based nomogram for prognosis evaluation and treatment optimization of non-metastatic oral cavity squamous cell carcinoma

BackgroundLymph node ratio (LNR) has been increasingly reported as a prognostic factor in oral cavity squamous cell carcinoma (OCSCC). This study aimed to develop and validate a prognostic nomogram integrating LNR and to further assess its role in guiding adjuvant therapy for OCSCC.MethodsA total of 8703 OCSCC patients treated primarily with surgery in the Surveillance, Epidemiology and End Results (SEER) database were retrieved and randomly divided into training and validation cohorts. The nomogram was created based on the factors identified by Cox model. The value of PORT and chemotherapy was respectively evaluated in each prognostic group according to nomogram-deduced individualized score.ResultsThe final nomogram included tumor site, grade, T stage, number of positive lymph nodes and LNR. Calibration plots demonstrated a good match between predicted and observed rates of overall survival (OS). The concordance indexes for training and validation cohorts were 0.720 (95% confidence interval (CI): 0.708, 0.732) and 0.711 (95% CI: 0.687, 0.735), both significantly higher than did TNM stage (p< 0.001). According to individualized nomogram score, patients were stratified into three subgroups with significantly distinct outcome. PORT presented survival benefit among medium- and high-risk groups whereas a near-detrimental effect in low-risk group. Chemotherapy was found to be beneficial only in high-risk group.ConclusionThis LNR-incorporated nomogram surpassed the conventional TNM stage in predicting prognosis of patients with non-metastatic OCSCC and identified sub-settings that could gain survival benefit from adjuvant thearpy.     

Comprehensive analysis of the functional microRNA–mRNA regulatory network identifies miRNA signatures associated with glioma malignant progression

Glioma is the most common and fatal primary brain tumour with poor prognosis; however, the functional roles of miRNAs in glioma malignant progression are insufficiently understood. Here, we used an integrated approach to identify miRNA functional targets during glioma malignant progression by combining the paired expression profiles of miRNAs and mRNAs across 160 Chinese glioma patients, and further constructed the functional miRNA–mRNA regulatory network. As a result, most tumour-suppressive miRNAs in glioma progression were newly discovered, whose functions were widely involved in gliomagenesis. Moreover, three miRNA signatures, with different combinations of hub miRNAs (regulations≥30) were constructed, which could independently predict the survival of patients with all gliomas, high-grade glioma and glioblastoma. Our network-based method increased the ability to identify the prognostic biomarkers, when compared with the traditional method and random conditions. Hsa-miR-524-5p and hsa-miR-628-5p, shared by these three signatures, acted as protective factors and their expression decreased gradually during glioma progression. Functional analysis of these miRNA signatures highlighted their critical roles in cell cycle and cell proliferation in glioblastoma malignant progression, especially hsa-miR-524-5p and hsa-miR-628-5p exhibited dominant regulatory activities. Therefore, network-based biomarkers are expected to be more effective and provide deep insights into the molecular mechanism of glioma malignant progression.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.     

MicroRNA signature predicts survival in papillary thyroid carcinoma

Papillary thyroid cancer (PTC) accounts for the majority of malignant thyroid tumors. Recently, several microRNA (miRNA) expression profiling studies have used bioinformatics to suggest miRNA signatures as potential prognostic biomarkers in various malignancies. However, a prognostic miRNA biomarker has not yet been established for PTC. The aim of the present study was to identify miRNAs with prognostic value for the overall survival (OS) of patients with PTC by analyzing high-throughput miRNA data and their associated clinical characteristics downloaded from The Cancer Genome Atlas database. From our dataset, 150 differentially expressed miRNAs were identified between tumor and nontumor samples; of these miRNAs, 118 were upregulated and 32 were downregulated. Among the 150 differentially expressed miRNAs, a four miRNA signature was identified that reliably predicts OS in patients with PTC. This miRNA signature was able to classify patients into a high-risk group and a low-risk group with a significant difference in OS (P < .01). The prognostic value of the signature was validated in a testing set ( P < .01). The four miRNA signature was an independent prognostic predictor according to the multivariate analysis and demonstrated good performance in predicting 5-year disease survival with an area under the receiver operating characteristic curve area under the curve (AUC) score of 0.886. Thus, this signature may serve as a novel biomarker for predicting the survival of patients with PTC.     

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2–APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

A two-CpG-based prognostic signature for oral squamous cell carcinoma overall survival

Oral squamous cell carcinoma (OSCC) represents one of the most common head and neck cancer that with dire prognosis due partly to the lack of reliable prognostic biomarker. Here, we aimed to develop a CpG site–based prognostic signature through which we could accurately predict overall survival (OS) of patients with OSCC. We obtained OSCC-related DNA methylation and gene expression data sets from the public accessible Gene Expression Omnibus. Correlations between methylation level of CpG sites and OS of patients with OSCC were assessed by univariate Cox regression analysis followed by robust likelihood-based survival analysis on those CpG sites with permutation P < 0.05 for further screening the optimal CpG sites for OSCC OS prediction based on the risk score formula that composed of the methylation level of optimal CpG sites weighted by their regression coefficients. Besides, differential expression genes (DEGs) and differential methylation genes (DMGs) in OSCC samples compared with normal samples were obtained and shared genes were considered as vital genes in OSCC tumorgenesis and progression. As a result, two CpG sites including cg17892178 and cg17378966 that located in NID2 and IDO1, respectively, were identified as the optimal prognostic signatures for OSCC OS. In addition, 12 overlapping genes between DEGs and DMGs that closely associated with inflammation or blood and tissue development–related biological processes were obtained. In conclusions, this study should provide valuable signatures for OSCC diagnosis and treatment.     

Identification of a prognostic signature of epithelial ovarian cancer based on tumor immune microenvironment exploration

This study aims to develop an immune-related genes (IRGs) prognostic signature to stratify the epithelial ovarian cancer (EOC) patients. We identified 332 up- and 154 down-regulated EOC-specific IRGs. As a result, candidate IRGs were idendified to construct prognostic models respectivy for overall survial and progression-free survival. The risk score was validated as a risk factor for prognosis and was used to built a combined nomogram. According to the IRG-related prognostic model, EOC patients were divided into high- and low- risk group and were further explored their association with tumor immune microenvironment (TME). CIBERSORT algorithm showed higher macrophages M1 cell, T cells follicular helper cell and plasma cells infiltrating levels in the low-risk group. In addition, the low-risk group was found with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. These findings may shed light on the development of novel immune biomarkers and target therapy of EOC.     

Protein signatures correspond to survival outcomes of AJCC stage III melanoma patients

Outcomes for melanoma patients with stage III disease differ widely even within the same subcategory. Molecular signatures that more accurately predict prognosis are needed to stratify patients according to risk. Proteomic analyses were used to identify differentially abundant proteins in extracts of surgically excised samples from patients with stage IIIc melanoma lymph node metastases. Analysis of samples from patients with poor (n = 14, <1 yr) and good (n = 19, >4 yr) survival outcomes identified 84 proteins that were differentially abundant between prognostic groups. Subsequent selected reaction monitoring analysis verified 21 proteins as potential biomarkers for survival. Poor prognosis patients are characterized by increased levels of proteins involved in protein metabolism, nucleic acid metabolism, angiogenesis, deregulation of cellular energetics and methylation processes, and decreased levels of proteins involved in apoptosis and immune response. These proteins are able to classify stage IIIc patients into prognostic subgroups (P < 0.02). This is the first report of potential prognostic markers from stage III melanoma using proteomic analyses. Validation of these protein markers in larger patient cohorts should define protein signatures that enable better stratification of stage III melanoma patients.     

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BackgroundThe low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.MethodsSaliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.ResultsThere were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.ConclusionsThe results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.     

Prediction of overall survival and response to immune checkpoint inhibitors: An immune-related signature for gastric cancer

Gastric cancer (GC) is common in East Asia and South and Central America. Most GC patients miss the opportunities for surgery. Despite their therapeutic potential, immune checkpoint inhibitors (ICIs) only work in part of patients with GC. Thus, this study was aimed at constructing a signature for diagnosis, prognosis, and prediction of response to ICIs. A multivariate analysis showed that the 8-immune-related-gene (IRG) signature was an independent prognostic factor of overall survival among GC patients. In the high-risk group of 8IRG signature risk score, the fractions of CD4 T cells, macrophage M2 and monocyte, which is associated with the progression of cancers, were higher. The low-risk group had a higher immunophenoscore, which meant a better response to ICIs.     

A Novel Statistical Prognostic Score Model That Includes Serum CXCL5 Levels and Clinical Classification Predicts Risk of Disease Progression and Survival of Nasopharyngeal Carcinoma Patients

Background

Aberrant expression of C-X-C motif chemokine 5 (CXCL5) contributes to the progression of various cancers. This study analyzed the clinical significance of serum CXCL5 (sCXCL5) levels of nasopharyngeal carcinoma (NPC) patients, with the goal of building a novel prognostic score model.

Experimental Design

Serum samples were collected prior to treatment from 290 NPC patients for the detection of sCXCL5 with ELISA. Half of the patients (n = 145) were randomly assigned to the training set to generate the sCXCL5 cutoff point using receiver operator characteristic (ROC) analysis, while the other half (n = 145) were assigned to the testing set for validation. Associations between sCXCL5 levels and clinical characteristics were analyzed. A prognostic score model was built using independent predictors derived from multivariate analysis. A concordance index (C-Index) was used to evaluate prognostic ability.

Results

The sCXCL5 cutoff point was 0.805 ng/ml. Sex, age, histology, T classification, clinical classification and local recurrence were not associated with sCXCL5 levels. However, sCXCL5 levels were positively associated with N classification, distant metastasis and disease progression (P<0.05). A high sCXCL5 level predicted poor 6-year overall survival (OS), poor 6-year distant metastasis-free survival (DMFS), and poor 6-year progression-free survival (PFS). A prognostic score model was subsequently constructed based on sCXCL5 levels and clinical classification (C-C model), which are independent predictors of OS, DMFS, and PFS, as confirmed by the multivariate analysis. Furthermore, this novel model successfully divided the patients into four risk subgroups in the training set, the testing set and the entire set of patients. The C-Indices were 0.751 and 0.762 for the training set and the testing set, respectively.

Conclusions

sCXCL5 level was determined to be an independent prognostic factor for NPC patients. The novel statistical C-C model, which includes sCXCL5 levels and clinical classification, could be helpful in predicting the prognosis of NPC patients.     

胃癌组织miR-203、miR-4317的表达及临床意义

目的:探讨胃癌组织中微小核糖核酸(mi R)-203、mi R-4317的表达情况及其临床意义。方法:选择2014年1月至2016年12月我院收治的胃癌患者92例,应用实时定量荧光PCR(qRT-PCR)检测患者胃癌组织及其相应癌旁组织中mi R-203、mi R-4317的表达情况,分析mi R-203、mi R-4317表达与胃癌患者临床病理参数的关系,所有患者随访3年,根据mi R-203、mi R-4317在胃癌组织中的中位表达量将患者分为mi R-203高表达组(49例)、mi R-203低表达组(43例)、mi R-4317高表达组(51例)、mi R-4317低表达组(41例)。分析mi R-203、mi R-4317与预后的关系及预后的影响因素。结果:胃癌组织中mi R-203、mi R-4317相对表达量显著低于癌旁组织(P<0.05)。不同性别、年龄、肿瘤大小胃癌患者胃癌组织中mi R-203、mi R-4317相对表达量比较无统计学差异(P>0.05),中低分化、TNM分期为III~IV期、有淋巴结转移胃癌患者胃癌组织中mi R-203、mi R-4317相对表达量显著低于高分化、TNM分期为I~II期、无淋巴结转移胃癌患者(P<0.05)。mi R-203高表达组3年生存率显著高于mi R-203低表达组(P<0.05),mi R-4317高表达组3年生存率显著高于mi R-4317低表达组(P<0.05)。COX多因素分析显示,中低分化、TNM分期为III~IV期、有淋巴结转移、mi R-203低表达、mi R-4317低表达是影响胃癌患者预后的独立危险因素(P<0.05)。结论:胃癌组织中mi R-203、mi R-4317异常低表达,其水平与胃癌预后密切相关,且胃癌患者预后与分化程度、临床分期、淋巴结转移等相关。     

A P53-related microRNA model for predicting the prognosis of hepatocellular carcinoma patients

Studies have shown that microRNAs (miRNAs) play a vital role in tumor progression and patients’ prognosis. Therefore, we aimed to construct a miRNA model for forecasting the survival of hepatocellular carcinoma (HCC) patients. The gene expression data of 433 patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus public databases were remined by survival analysis and receptor manipulation characteristic curve (ROC). A prognostic model including six miRNAs (hsa-mir-26a-1-3p, hsa-mir-188-5p, hsa-mir-212-5p, hsa-mir-149-5p, hsa-mir-105-5p, and hsa-mir-132-5p) were constructed in the training dataset (TCGA, n = 333). HCC patients were stratified into a high-risk group and a low-risk group with significantly different survival (median: 2.75 vs. 8.93 years, log-rank test p < .001). Then we proved its performance of stratification in another independent dataset (GSE116182, median: 2.55 vs 6.96 years, log-rank test p = .008). Cox regression analysis showed that the prognostic model was an independent prognostic indicator for HCC patients. Then time-dependent ROC analyses were performed to test the prognostic ability of the model with that of TNM staging, we found the model had a better performance, especially at 5 years (AUC = 0.76). Functional prediction showed that the genes targeted by the six prognostic miRNAs in the prognostic model were highly expressed in the P53-related pathway. In conclusion, we constructed a prognostic miRNA model that could indicate the survival of HCC patients.     

Increased CYFRA 21-1, CEA and NSE are Prognostic of Poor Outcome for Locally Advanced Squamous Cell Carcinoma in Lung: A Nomogram and Recursive Partitioning Risk Stratification Analysis

OBJECTIVES: This study aimed to: (1) assess the prognostic significance of serum tumor markers in locally advanced squamous cell carcinoma in lung (LA-SCCL); (2) generate a nomogram to predict the overall survival (OS) and (3) identify a prognostic stratification to assist the therapeutic decision-making. METHODS: LA-SCCL patients receiving definitive radiotherapy and baseline tumor marker measurement were eligible for this retrospective study. Cox proportional hazards regression was used to determine independent factors associated with various survival indexes and a nomogram was created to estimate the 5-year OS probability for individual patient. The identified prognostic factors were recruited into a recursive partitioning analysis (RPA) for OS to stratify patients with distinct outcome. RESULTS: A total of 224 patients were eligible for analysis. Increased cytokeratin-19 fragment (CYFRA 21-1) was independently associated with inferior OS, progression free survival (PFS) and a borderline decreased local-regional progression free survival (LRPFS). Elevated carcino-embryonic antigen (CEA) served as an unfavorable determinant for OS and increased neuron-specific enolase (NSE) was predictive of poor distant metastasis free survival (DMFS). A nomogram integrating KPS, TNM stage, CEA and CYFRA 21-1 was created, resulting in a c-index of 0.62. RPA identified 4 prognostic classifications, with median OS of 27.6, 19.9, 17.3 and 10.9?months for low, intermediate, high and very-high risk groups, respectively. CONCLUSIONS: Baseline tumor marker panel including CYFRA 21-1, CEA and NSE can be prognostic of outcome for LA-SCCL receiving definitive radiotherapy. The RPA identified four prognostic subgroups, which could assist personalized therapy and clinical trial design in LA-SCCL.