Huaqiang Zhou

Marital status is an independent prognostic factor for pancreatic neuroendocrine tumors patients: An analysis of the Surveillance, Epidemiology, and End Results (SEER) database

BACKGROUND AND OBJECTIVES Marital statuss prognostic impact on pancreatic neuroendocrine tumors (PNET) has not been rigorously studied. We aimed to explore the relationship between marital status and outcomes of PNET. METHODS We retrospectively investigated 2060 PNET cases between 2004 and 2010 from Surveillance, Epidemiology, and End Results (SEER) database. Variables were compared by Chi2 test, t-test as appropriate. Kaplan-Meier methods and COX proportional hazard models were used to ascertain independent prognostic factors. RESULTS Married patients had better 5-year overall survival (OS) (53.37% vs. 42.27%, P<0.001) and 5-year pancreatic neuroendocrine tumor specific survival (PNSS) (67.76% vs. 59.82%, P=0.001) comparing with unmarried patients. Multivariate analysis revealed marital status is an independent prognostic factor, with married patients showing better OS (HR=0.74; 95% CI: 0.65-0.84; P<0.001) and PNSS (HR=0.78; 95% CI: 0.66-0.92; P=0.004). Subgroup analysis suggested marital status plays a more important role in the PNET patients with distant stage rather than regional or localized disease. CONCLUSIONS Marital status is an independent prognostic factor for survival in PNET patients. Poor prognosis in unmarried patients may be associated with a delayed diagnosis with advanced tumor stage, psychosocial and socioeconomic factors. Further studies are needed.

Nomogram to Predict Cause-Specific Mortality in Patients With Surgically Resected Stage I Non-Small-Cell Lung Cancer: A Competing Risk Analysis

Micro‐Abstract It is important to consider the competing risks when evaluating prognosis. We evaluated the cumulative incidence function of cause‐specific death in 20,850 patients with surgically resected stage I non–small‐cell lung cancer. We also built the first competing risk nomogram to predict prognosis. Our nomograms show a relatively good performance and can be a convenient individualized predictive tool for prognosis. Background: The objective of this study was to evaluate the probability of cause‐specific death and other causes of death in patients with stage I non–small‐cell lung cancer (NSCLC) who underwent surgery. We also built competing risk nomograms to predict the prognosis of patients with NSCLC. Patients and Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified patients who underwent surgery with stage I NSCLC between 2004 and 2013. We estimated the cumulative incidence function (CIF) for cause‐specific death and other causes of death, and tested the differences using Gray’s test. The Fine and Gray proportional subdistribution hazard approach was applied to model CIF. We also built competing risk nomograms on the basis of Fine and Gray’s model. Results: We identified 20,850 stage I NSCLC patients from 2004 to 2013 in the SEER database. The 5‐year cumulative incidence of cause‐specific death for stage I NSCLC was 21.9% and 14.2% for other causes of death. Variables associated with cause‐specific mortality included age, sex, marital status, histological grade, TNM stage, and surgery. The nomograms were well calibrated, and had good discriminative ability, with a c‐index of 0.64 for the cancer‐specific mortality model and 0.66 for the competing mortality model. Conclusion: We evaluated the CIF of cause‐specific death and competing risk death in patients with surgically resected stage I NSCLC using the SEER database. We also built proportional subdistribution models and the first competing risk nomogram to predict prognosis. Our nomograms show a relatively good performance and can be a convenient individualized predictive tool for prognosis.

Integrated Analysis of Expression Profile Based on Differentially Expressed Genes in Middle Cerebral Artery Occlusion Animal Models.

Stroke is one of the most common causes of death, only second to heart disease. Molecular investigations about stroke are in acute shortage nowadays. This study is intended to explore a gene expression profile after brain ischemia reperfusion. Meta-analysis, differential expression analysis, and integrated analysis were employed on an eight microarray series. We explored the functions and pathways of target genes in gene ontology (GO) enrichment analysis and constructed a protein-protein interaction network. Meta-analysis identified 360 differentially expressed genes (DEGs) for Mus musculus and 255 for Rattus norvegicus. Differential expression analysis identified 44 DEGs for Mus musculus and 21 for Rattus norvegicus. Timp1 and Lcn2 were overexpressed in both species. The cytokine-cytokine receptor interaction and chemokine signaling pathway were highly enriched for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We have exhibited a global view of the potential molecular differences between middle cerebral artery occlusion (MCAO) animal model and sham for Mus musculus or Rattus norvegicus, including the biological process and enriched pathways in DEGs. This research helps contribute to a clearer understanding of the inflammation process and accurate identification of ischemic infarction stages, which might be transformed into a therapeutic approach.

Integrated clinicopathological features and gene microarray analysis of pancreatic neuroendocrine tumors.

Pancreatic neuroendocrine tumors are relatively rare pancreatic neoplasms over the world. Investigations about molecular biology of PNETs are insufficient for nowadays. We aimed to explore the expression of messenger RNA and regulatory processes underlying pancreatic neuroendocrine tumors from different views. The expression profile of GSE73338 were downloaded, including samples with pancreatic neuroendocrine tumors. First, the Limma package was utilized to distinguish the differentially expressed messenger RNA. Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to explore the functions and pathways of target genes. In addition, we constructed a protein-protein interaction network. NEK2, UBE2C, TOP2A and PPP1R1A were revealed with continuous genomic alterations in higher tumor stage. 91 up-regulated and 36 down-regulated genes were identified to be differentially expressed in malignant PNETs. Locomotory behavior was significantly enriched for biological processes of metastasis PNETs. GCGR and GNAS were identified as the hub of proteins in the protein-protein interaction sub-network of malignant PNETs. We showed the gene expression differences in PNETs according to different clinicopathological aspects. NEK2, UBE2C, TOP2A are positively associated with high tumor grade, and PPP1R1A negatively. GCGR and GNAS are regarded as the hub of the PPI sub-network. CXCR4 may affect the progression of PNETs via the CXCR4-CXCL12-CXCR7 chemokine receptor axis. However, more studies are required.

A bibliometric analysis in gene research of myocardial infarction from 2001 to 2015

Objectives We aimed to evaluate the global scientific output of gene research of myocardial infarction and explore their hotspots and frontiers from 2001 to 2015, using bibliometric methods. Methods Articles about the gene research of myocardial infarction between 2001 and 2015 were retrieved from the Web of Science Core Collection (WoSCC). We used the bibliometric method and Citespace V to analyze publication years, journals, countries, institutions, research areas, authors, research hotspots, and trends. We plotted the reference co-citation network, and we used key words to analyze the research hotspots and trends. Results We identified 1,853 publications on gene research of myocardial research from 2001 to 2015, and the annual publication number increased with time. Circulation published the highest number of articles. United States ranked highest in the countries with most publications, and the leading institute was Harvard University. Relevant publications were mainly in the field of Cardiovascular system cardiology. Keywords and references analysis indicated that gene expression, microRNA and young women were the research hotspots, whereas stem cell, chemokine, inflammation and cardiac repair were the frontiers. Conclusions We depicted gene research of myocardial infarction overall by bibliometric analysis. Mesenchymal stem cells Therapy, MSCs-derived microRNA and genetic modified MSCs are the latest research frontiers. Related studies may pioneer the future direction of this filed in next few years. Further studies are needed.

Multiple Machine Learnings Revealed Similar Predictive Accuracy for Prognosis of PNETs from the Surveillance, Epidemiology, and End Result Database

Background: Prognosis prediction is indispensable in clinical practice and machine learning has been proved to be helpful. We expected to predict survival of pancreatic neuroendocrine tumors (PNETs) with machine learning, and compared it with the American Joint Committee on Cancer (AJCC) staging system. Methods: Data of PNETs cases were extracted from The Surveillance, Epidemiology, and End Result (SEER) database. Statistic description, multivariate survival analysis and preprocessing were done before machine learning. Four different algorithms (logistic regression (LR), support vector machines (SVM), random forest (RF) and deep learning (DL)) were used to train the model. We used proper imputations to manage missing data in the database and sensitive analysis was performed to evaluate the imputation. The model with the best predictive accuracy was compared with the AJCC staging system using the SEER cases. Results: The four models had similar predictive accuracy with no significant difference existed (p = 0.664). The DL model showed a slightly better predictive accuracy than others (81.6% (± 1.9%)), thus it was used for further comparison with the AJCC staging system and revealed a better performance for PNETs cases in SEER database (Area under receiver operating characteristic curve: 0.87 vs 0.76). The validity of missing data imputation was supported by sensitivity analysis. Conclusions: The models developed with machine learning performed well in survival prediction of PNETs, and the DL model have a better accuracy and specificity than the AJCC staging system in SEER data. The DL model has potential for clinical application but external validation is needed.

Impact of prior cancer history on the overall survival of patients newly diagnosed with cancer: A pan-cancer analysis of the SEER database: Survival impact of prior cancer history for patients

The population of cancer survivors with prior cancer is rapidly growing. Whether a prior cancer diagnosis interferes with outcome is unknown. We conducted a pan‐cancer analysis to determine the impact of prior cancer history for patients newly diagnosed with cancer. We identified 20 types of primary solid tumors between 2004 and 2008 in the Surveillance, Epidemiology, and End Results database. Demographic and clinicopathologic variables were compared by χ2 test and t‐test as appropriate. The propensity score‐adjusted Kaplan‐Meier method and Cox proportional hazards models were used to evaluate the impact of prior cancer on overall survival (OS). Among 1,557,663 eligible patients, 261,474 (16.79%) had a history of prior cancer. More than 65% of prior cancers were diagnosed within 5 years. We classified 20 cancer sites into two groups (PCI and PCS) according to the different impacts of prior cancer on OS. PCI patients with a prior cancer history, which involved the colon and rectum, bone and soft tissues, melanoma, breast, cervix uteri, corpus and uterus, prostate, urinary bladder, kidney and renal pelvis, eye and orbits, thyroid, had inferior OS. The PCS patients (nasopharynx, esophagus, stomach, liver, gallbladder, pancreas, lung, ovary and brain) with a prior cancer history showed similar OS to that of patients without prior cancer. Our pan‐cancer study presents the landscape for the survival impact of prior cancer across 20 cancer types. Compared to the patients without prior cancer, the PCI group had inferior OS, while the PCS group had similar OS. Further studies are still needed.

Trends in incidence and associated risk factors of suicide mortality in patients with non-small cell lung cancer

Lung cancer patients have an increased risk for committing suicide. But no comprehensive study about the suicide issues among non‐small‐cell lung cancer (NSCLC) patients has been published. We aimed to estimate the trend of suicide rate and identify the high‐risk group of NSCLC patients. Patients diagnosed with primary NSCLC were identified from Surveillance, Epidemiology, and End Results (SEER) database (1973‐2013). Suicide mortality rate (SMR) were calculated. Multivariable logistic regression was employed to find out independent risk factors for suicide. Among 495 889 NSCLC patients, 694 (0.14%) of them died from suicide. The suicide mortality rates have significantly decreased (before 1993: 0.21%, 1994‐2003: 0.16%, after 2004: 0.09%, P < .001). Male (OR 6.22, 95% CI: 4.96‐7.98, P < .001), white (OR 3.89, 95% CI: 2.66‐5.97, P < .001), being unmarried (OR 1.43, 95% CI: 1.22‐1.67, P < .001), the elderly (60‐74 vs <60: OR 1.24, 95% CI: 1.03‐1.50, P = .024, >75 vs <60: OR 1.31, 95% CI: 1.05‐1.63, P = .018) were independently associated with higher risk of suicide mortality. Surgery (OR: 1.44, 95% CI: 1.19‐1.73, P < .001) was also relative with higher risk of suicide. Our study observed significant decrease in suicide mortality among NSCLC patients in US over past decades. Older age, male sex, unmarried status, and surgery were risk factors of committing suicide. Clinicians should be aware of these high‐risk groups.

Unsupervised clustering reveals new prostate cancer subtypes

Background: Prostate cancer is the second most common cancer in men. It is urgent to develop a genetic classification for prostate cancer. We aimed to establish the basis of genetic typing. Methods: We used four series of prostate cancer data. The Cancer Genome Atlas (TCGA) RNA-Seq data were used to train the classifier. Three subgroups based on the classifier were tested whether to have significant differences in the clinical data. The other three sets were classified by the classifier and validated with respective clinical data. Results: The classifier had 183 genes. Prostate cancer subtype 1 (PCS1) was characterized by high expression of GSTP1, with lower Gleason scores (P Conclusions: We established a PCS classifier (183 genes) based on RNA-Seq data, and identified three PCSs. The classification was robustly relating to clinical data which may have potential for clinical use.

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Background Oral tongue squamous cell carcinoma (OTSCC) is the most common subtype of oral cancer. A predictive gene signature is necessary for prognosis of OTSCC. Methods Five microarray data sets of OTSCC from the Gene Expression Omnibus (GEO) and one data set from The Cancer Genome Atlas (TCGA) were obtained. Differentially expressed genes (DEGs) of GEO data sets were identified by integrated analysis. The DEGs associated with prognosis were screened in the TCGA data set by univariate survival analysis to obtain a gene signature. A risk score was calculated as the summation of weighted expression levels with coefficients by Cox analysis. The signature was used to distinguish carcinoma, estimated by receiver operator characteristic curves and the area under the curve (AUC). All were validated in the GEO and TCGA data sets. Results Integrated analysis of GEO data sets revealed 300 DEGs. A 16-gene signature and a risk score were developed after survival analysis. The risk score was effective to stratify patients into high-risk and low-risk groups in the TCGA data set (P < 0.001). The 16-gene signature was valid to distinguish the carcinoma from normal samples (AUC 0.872, P < 0.001). Discussion We identified a useful 16-gene signature for prognosis of OTSCC patients, which could be applied to clinical practice. Further studies were needed to prove the findings.