Robust prognostic model based on immune infiltration‐related genes and clinical information in ovarian cancer

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient''s prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K‐M curves, ROC curves and C‐index on test set. In test set, IGCI score (C‐index = 0.630) is significantly better than AJCC stage (C‐index = 0.541, p < 0.05) and CIN25 (C‐index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi‐squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p‐value (p < 0.1) in both chi‐squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti‐tumour drugs has changed significantly (p < 0.05).     

Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction

Background

Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35–50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.

Methodology/Principal Findings

From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.

Conclusions/Significance

The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.     

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 Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group

Background

Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature.

Methods

Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort.

Results

A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2^.507; B = 0^.919; p<0^.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0^.001) and TCGA cohort (p = 0^.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group.

Conclusion

We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.     

Met Kinetic Signature Derived from the Response to HGF/SF in a Cellular Model Predicts Breast Cancer Patient Survival

To determine the signaling pathways leading from Met activation to metastasis and poor prognosis, we measured the kinetic gene alterations in breast cancer cell lines in response to HGF/SF. Using a network inference tool we analyzed the putative protein-protein interaction pathways leading from Met to these genes and studied their specificity to Met and prognostic potential. We identified a Met kinetic signature consisting of 131 genes. The signature correlates with Met activation and with response to anti-Met therapy (p<0.005) in in-vitro models. It also identifies breast cancer patients who are at high risk to develop an aggressive disease in six large published breast cancer patient cohorts (p<0.01, N>1000). Moreover, we have identified novel putative Met pathways, which correlate with Met activity and patient prognosis. This signature may facilitate personalized therapy by identifying patients who will respond to anti-Met therapy. Moreover, this novel approach may be applied for other tyrosine kinases and other malignancies.     

FOXD1 is a prognostic biomarker and correlated with macrophages infiltration in head and neck squamous cell carcinoma

Background: Forkhead Box D1 (FOXD1) is differentially expressed in various tumors. However, its role and correlation with immune cell infiltration remains uncertain in head and neck squamous cell carcinoma (HNSC).Methods: FOXD1 expression was analyzed in The Cancer Genome Atlas (TCGA) pan-cancer data. The clinical prognosis influence of FOXD1 was evaluated by clinical survival data of TCGA. Enrichment analysis of FOXD1 was performed using R packages ‘clusterProfiler’. We downloaded the immune cell infiltration score of TCGA samples from published articles, and analyzed the correlation between immune cell infiltration level and FOXD1 expression.Results: FOXD1 was highly expressed and associated with poorer overall survival (OS, P<0.0001), disease-specific survival (DSS, P=0.00011), and progression-free interval (PFI, P<0.0001) in HNSC and some other tumors. In addition, FOXD1 expression was significantly correlated with infiltration of immune cells. Tumor-associated macrophages (TAMs) infiltration increased in tissues with high FOXD1 expression in HNSC. Immunosuppressive genes such as PD-L1, IL-10, TGFB1, and TGFBR1 were significantly positively correlated with FOXD1.Conclusions: Our study suggests FOXD1 to be an oncogene and act as an indicator of poor prognosis in HNSC. FOXD1 might contribute to the TAM infiltration in HNSC. High FOXD1 may be associated with tumor immunosuppression status.     

Genes Influenced by the Non-Muscle Isoform of Myosin Light Chain Kinase Impact Human Cancer Prognosis

The multifunctional non-muscle isoform of myosin light chain kinase (nmMLCK) is critical to the rapid dynamic coordination of the cytoskeleton involved in cancer cell proliferation and migration. We identified 45 nmMLCK-influenced genes by bioinformatic filtering of genome–wide expression in wild type and nmMLCK knockout (KO) mice exposed to preclinical models of murine acute inflammatory lung injury, pathologies that are well established to include nmMLCK as an essential participant. To determine whether these nmMLCK-influenced genes were relevant to human cancers, the 45 mouse genes were matched to 38 distinct human orthologs (M38 signature) (GeneCards definition) and underwent Kaplan-Meier survival analysis in training and validation cohorts. These studies revealed that in training cohorts, the M38 signature successfully identified cancer patients with poor overall survival in breast cancer (P<0.001), colon cancer (P<0.001), glioma (P<0.001), and lung cancer (P<0.001). In validation cohorts, the M38 signature demonstrated significantly reduced overall survival for high-score patients of breast cancer (P = 0.002), colon cancer (P = 0.035), glioma (P = 0.023), and lung cancer (P = 0.023). The association between M38 risk score and overall survival was confirmed by univariate Cox proportional hazard analysis of overall survival in the both training and validation cohorts. This study, providing a novel prognostic cancer gene signature derived from a murine model of nmMLCK-associated lung inflammation, strongly supports nmMLCK-involved pathways in tumor growth and progression in human cancers and nmMLCK as an attractive candidate molecular target in both inflammatory and neoplastic processes.     

Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2’s predictive value for patients’ restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission (P < 0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability (P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.     

Influence of ATP-Binding Cassette Transporter 1 R219K and M883I Polymorphisms on Development of Atherosclerosis: A Meta-Analysis of 58 Studies

Background

Numerous epidemiological studies have evaluated the associations between ATP-binding cassette transporter 1 (ABCA1) R219K (rs2230806) and M883I (rs4149313) polymorphisms and atherosclerosis (AS), but results remain controversial. The purpose of the present study is to investigate whether these two polymorphisms facilitate the susceptibility to AS using a meta-analysis.

Methods

PubMed, Embase, Web of Science, Medline, Cochrane database, Clinicaltrials.gov, Current Controlled Trials, Chinese Clinical Trial Registry, CBMdisc, CNKI, Google Scholar and Baidu Library were searched to get the genetic association studies. All statistical analyses were done with Stata 11.0.

Results

Forty-seven articles involving 58 studies were included in the final meta-analysis. For the ABCA1 R219K polymorphism, 42 studies involving 12,551 AS cases and 19,548 controls were combined showing significant association between this variant and AS risk (for K allele vs. R allele: OR = 0.77, 95% CI = 0.71–0.84, P<0.01; for K/K vs. R/R: OR = 0.60, 95% CI = 0.51–0.71, P<0.01; for K/K vs. R/K+R/R: OR = 0.69, 95% CI = 0.60–0.80, P<0.01; for K/K+R/K vs. R/R: OR = 0.74, 95% CI = 0.66–0.83, P<0.01). For the ABCA1 M883I polymorphism, 16 studies involving 4,224 AS cases and 3,462 controls were combined. There was also significant association between the variant and AS risk (for I allele vs. M allele: OR = 0.85, 95% CI = 0.77–0.95, P<0.01).

Conclusions

The present meta-analysis suggested that the ABCA1 R219K and M883I polymorphisms were associated with the susceptibility to AS. However, due to the high heterogeneity in the meta-analysis, the results should be interpreted with caution.     

ATIC inhibits autophagy in hepatocellular cancer through the AKT/FOXO3 pathway and serves as a prognostic signature for modeling patient survival

Background: Autophagy regulates many cell functions related to cancer, ranging from cell proliferation and angiogenesis to metabolism. Due to the close relationship between autophagy and tumors, we investigated the predictive value of autophagy-related genes.Methods: Data from patients with hepatocellular carcinoma were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A regression analysis of differentially expressed genes was performed. Based on a prognostic model, patients were divided into a high-risk or low-risk group. Kaplan-Meier survival analyses of patients were conducted. The immune landscapes, as determined using single-sample gene set enrichment analysis (ssGSEA), exhibited different patterns in the two groups. The prognostic model was verified using the ICGC database and clinical data from patients collected at Zhongnan Hospital. Based on the results of multivariate Cox regression analysis, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/inosine monophosphate (IMP) cyclohydrolase (ATIC) had the largest hazard ratio, and thus we studied the effect of ATIC on autophagy and tumor progression by performing in vitro and in vivo experiments.Results: Fifty-eight autophagy-related genes were differentially expressed (false discovery rate (FDR)<0.05, log₂ fold change (logFC)>1); 23 genes were related to the prognosis of patients. A prognostic model based on 12 genes (ATG10, ATIC, BIRC5, CAPN10, FKBP1A, GAPDH, HDAC1, PRKCD, RHEB, SPNS1, SQSTM1 and TMEM74) was constructed. A significant difference in survival rate was observed between the high-risk group and low-risk group distinguished by the model (P<0.001). The model had good predictive power (area under the curve (AUC)>0.7). Risk-related genes were related to the terms type II IFN response, MHC class I (P<0.001) and HLA (P<0.05). ATIC was confirmed to inhibit autophagy and promote the proliferation, invasion and metastasis of liver cancer cells through the AKT/Forkhead box subgroup O3 (FOXO3) signaling pathway in vitro and in vivo.Conclusions: The prediction model effectively predicts the survival time of patients with liver cancer. The risk score reflects the immune cell features and immune status of patients. ATIC inhibits autophagy and promotes the progression of liver cancer through the AKT/FOXO3 signaling pathway.     

Prognostic Factors and Scoring System for Death from Visceral Leishmaniasis: An Historical Cohort Study in Brazil

Background

In Brazil, case-fatality rates attributable to visceral leishmaniasis (VL) are high and knowledge of the risk factors associated with death may help reduce mortality. The aim of this study was to construct and validate a scoring system for prognosis of death from VL by using all cases reported in Brazil from 2007 to 2011.

Methodology

In this historical cohort study, 18,501 VL cases were analyzed; of these, 17,345 cases were cured and 1,156 cases caused death. The database was divided into two series: primary (two-thirds of cases), to develop the model score, and secondary (one-third of cases), to validate the scoring system. Multivariate logistic regression models were performed to identify factors associated with death from VL, and these were included in the scoring system.

Principal Findings

The factors associated with death from VL were: bleeding (score 3); splenomegaly (score 1); edema (score 1); weakness (score 1); jaundice (score 1); Leishmania–HIV co-infection (score 1); bacterial infection (score 1); and age (≤0.5 years [score 5]; >0.5 and ≤1 [score 2]; >19 and ≤50 [score 2]; >50 and <65 [score 3]; ≥65 [score 5]). It was observed that patients with a score of 4 had a probability of death of approximately 4.5% and had a worse prognosis. The sensitivity, specificity, and accuracy of this score were 89.4, 51.2, and 53.5, respectively.

Conclusions/Significance

The scoring system based on risk factors for death showed good performance in identifying patients with signs of severity at the time of clinical suspicion of VL and can contribute to improving the surveillance system for reducing case fatalities. The classification of patients according to their prognosis for death may assist decision-making regarding the transfer of the patients to hospitals more capable of handling their condition, admission to the intensive care unit, and adequate support and specific treatment.     

Analysis of MET mRNA Expression in Gastric Cancers Using RNA In Situ Hybridization Assay: Its Clinical Implication and Comparison with Immunohistochemistry and Silver In Situ Hybridization

We investigated MET mRNA expression status using RNA in situ hybridization (ISH) technique in primary and metastatic lesions of 535 surgically resected gastric carcinoma (GC) cases. We compared the results with those of immunohistochemistry and silver in situ hybridization, and examined the association with clinicopathologic characteristics and prognosis. Among 535 primary GCs, 391 (73.1%) were scored 0, 87 (16.3%) were scored 1, 38 (7.1%) were scored 2, 12 (2.2%) were scored 3 and 7 (1.3%) were scored 4 by RNA ISH. High MET mRNA expression (score ≥3) was associated with lymph node metastasis (P = .014), distant metastasis (P = .001), and higher TNM stage (P<.001). MET mRNA expression was correlated with protein expression (r = 0.398; P<.001) and gene copy number (r = 0.345; P<.001). The patients showing high-MET mRNA in primary or metastatic lesions had shorter overall survival than those showing low-MET mRNA (primary tumors, P = .002; metastatic lymph nodes, P<.001). The patients showing positive conversion of MET mRNA status in metastatic lymph node had shorter overall survival than those with no conversion (P = .011). Multivariate analysis demonstrated that high MET mRNA expression in metastatic lymph node was an independent prognostic factor for overall survival (P = .007). Therefore, this study suggests that MET mRNA expression assessed by RNA ISH could be useful as a potential marker to identify MET oncogene-addicted GC.     

The GADD45A (1506T>C) Polymorphism Is Associated with Ovarian Cancer Susceptibility and Prognosis

GADD45A (growth arrest and DNA damage 45 A) is the first stress-inducible gene identified to be a target of p53. However, no studies to date have assessed variants of the GADD45 gene and their potential relationship to tumor susceptibility. We investigated the association of the GADD45A (1506T>C) polymorphism with ovarian cancer development in 258 ovarian cancer patients and 332 age-matched healthy women as controls using sequence analysis. We found a statistically significant difference in the GADD45A (1506T>C) genotype distributions between the case and control groups (TT vs. TC vs. CC, P = 0.0021) and found that variant 1506T>C was significantly associated with an increased risk of ovarian cancer (P<0.001, OR = 1.71, 95% CI [1.28–2.29]). We observed a statistically significant effect between tumor histology (P = 0.032) and CA125 status (P = 0.021). Carrying the C allele (TC+CC) was associated with an increased risk of positive CA125 (OR = 3.20, 95% CI [1.15–8.71). Carrying the T allele (TT+TC) showed a significant correlation with both higher GADD45A mRNA expression and longer ovarian cancer RFS (relapse-free survival) and OS (overall survival). We are the first group to demonstrate that the GADD45A (1506T>C) polymorphism is associated with ovarian cancer susceptibility and prognosis. These data suggest that GADD45A (1506T>C) is a new tumor susceptibility gene and could be a useful molecular marker for assessing ovarian cancer risk and for predicting ovarian cancer patient prognosis.     

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.     

Integration of OV6 expression and CD68+ tumor-associated macrophages with clinical features better predicts the prognosis of patients with hepatocellular carcinoma

Background: Reliable prognostic indicators for accurately predicting postoperative outcomes in Hepatocellular carcinoma (HCC) patients are lacking. Although cancer stem-like cells (CSCs) and tumor-associated macrophages (TAMs) in tumor microenvironment are implicated in the occurrence and development of HCC, whether the combination of CSC biomarkers and TAM populations could achieve better performance in predicting the prognosis of patients with HCC has been rarely reported.Methods: A total of 306 HCC patients were randomly divided into the training and validation cohorts at a 1:1 ratio, and the expression of OV6 and CD68 was assessed using immunohistochemistry in HCC samples. The prognostic value of these biomarkers for post-surgical survival and recurrence were evaluated by the curve of receiver operating characteristic and multivariate Cox regression analyses.Results: The density of OV6⁺ CSCs was positively correlated with the infiltration of CD68⁺ TAMs in HCC. Both high OV6 expression and CD68⁺ TAM infiltration was closely associated with poor overall survival (OS) and progression-free survival (PFS) of HCC patients. Moreover, overexpression of OV6 and infiltration of CD68⁺ TAMs were identified as independent prognostic factors for OS and PFS after liver resection. The integration of OV6 and CD68 with tumor size and microvascular invasion exhibited highest C-index value for survival predictivity in HCC patients than any other biomarkers or clinical indicators alone.Conclusion: Incorporating intratumoral OV6 expression and CD68⁺ TAMs infiltration with established clinical indicators may serve as a promising prognostic signature for HCC, and could more accurately predict the clinical outcomes for HCC patients after liver resection.