Survival trends and conditional survival in primary non-metastatic esophageal cancer: a SEER population-based study and external validation.

Background: The dynamic survival trend of patients with primary non-metastatic esophageal cancer (nMEC) is unknown. We conducted a conditional survival (CS) analysis and developed a novel nomogram to predict it. Methods: Patients with primary nMEC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors of cancer-specific survival (CSS) were identified. The log-rank test and Cox analysis were used to calculate probabilities of CS. We constructed nomograms to predict survival trends and CS probabilities based on the prognostic factors. Calibration curves and C-indexes were used for internal and external validation. Results: A total of 9,008 patients were identified from the SEER database and 37 patients were recruited as an external validation cohort. The 1- and 3-year CS rates were 69.6% and 43.1% at diagnosis, rising to 95.2% and 86.2% at the fifth conditional year. CS probabilities by different variables continuously improved over time. The calibration curves of the CS nomograms fit well. The C-indexes were 0.700 (95% CI: 0.693-0.709) in the training cohort, 0.693 (95% CI: 0.669-0.717) in the internal validation cohort, and 0.683 (95% CI: 0.556-0.810) in the external validation cohort. Conclusions: CS rates are more dynamic than traditional survival rates for patients surviving for a relatively longer period. The CS rates of patients with nMEC improved over time and became stable after surviving for a few years. We developed and validated nomograms to predict CS probabilities.

Mu opioid receptor mRNA overexpression predicts poor prognosis among 18 common solid cancers: A pan-cancer analysis.

Background: Opioids are widely used for patients with solid tumors during surgery and for cancer pain relief. We conducted a pan-cancer genomic analysis to investigate the prognostic features of Mu opioid receptor (MOR) mRNA expression across 18 primary solid cancers. Methods: All the data of cancer with MOR mRNA were retrieved from cBioPortal for Cancer Genomics. Logistic regression was used to determine the associations between MOR mRNA expression and clinicopathological features. Log-rank test and Cox regression was used for survival analysis. Subgroup analysis and propensity score matching were also carried out. Results: 7,274 patients, including 1,112 patients with positive MOR mRNA expression, were included for data analyses. Positive MOR mRNA expression was associated with more advanced stage of T (adjusted Odds ratio [OR], 1.176; 95% confidence interval [CI], 1.022-1.354; P=0.024), M (adjusted OR, 1.548; 95% CI, 1.095-2.189; P=0.013) except N (adjusted OR, 1.145; 95% CI, 0.975-1.346; P=0.101), and worse prognosis for overall survival (Hazard ratio [HR] 1.347, 95% CI 1.200-1.512, P<0.001), progression-free survival (HR 1.359, 95% CI 1.220-1.513, P<0.001), disease-free survival (HR 1.269, 95% CI 1.016-1.585, P<0.001) and disease-specific survival (HR 1.474, 95% CI 1.284-1.693, P<0.001). Patients with positive MOR mRNA expression tended to be classified as tumor microenvironment immune types II, representing low PD-L1 and low CD8A expression. Conclusion: MOR mRNA overexpression is associated with poor prognosis and poor response to PD-L1 therapy.

Association between Socioeconomic Status and One-Month Mortality after Surgery in 20 Primary Solid Tumors: a Pan-Cancer Analysis.

Background: Surgery is the main therapy for primary solid tumors. One-month postoperative mortality remains an important criterion for assessing the quality of surgery. Socioeconomic status (SES) plays an important role in the biopsychosocial medical model. We performed a pan-cancer analysis to explore the relationship between SES and one-month mortality after surgery in 20 primary solid tumors. Methods: Eight SES factors and the top 20 common cancer sites were selected between 2007 and 2014 based on the Surveillance, Epidemiology, and End Results database. The primary outcome was that patients died within one month after surgery. The control group survived beyond one month. Multivariable logistic regression model, propensity score matching and subgroup analysis were used to detect the association. Results: There were 15980 (1.4%) patients who died within one month after surgery among 1132666 patients with primary solid cancers. Patients with unmarried status (aOR 1.516, 95% CI 1.462-1.573, P < 0.001), Medicaid/uninsured status (aOR 1.610, 95% CI 1.534-1.689, P < 0.001), low income (aOR 1.122, 95% CI 1.053-1.196, P < 0.001), low education (aOR 1.088, 95% CI 1.033-1.146, P = 0.001), or high poverty (aOR 1.085, 95% CI 1.026-1.147, P = 0.004) had high risks of one-month postoperative mortality. After propensity score matching and subgroup analysis, the effects of marriage and insurance on mortality were almost consistent with overall. Conclusions: There was a strong association between SES status and one-month postoperative mortality in primary solid tumors. Socioeconomically disadvantaged people had high risks of dying within one month after surgery. Unmarried or Medicaid/uninsured status were associated with much higher risks than other factors.

Applying deep learning in recognizing the femoral nerve block region on ultrasound images.

BACKGROUND: Identifying the nerve block region is important for the less experienced operators who are not skilled in ultrasound technology. Therefore, we constructed and shared a dataset of ultrasonic images to explore a method to identify the femoral nerve block region. METHODS: Ultrasound images of femoral nerve block were retrospectively collected and marked to establish the dataset. The U-net framework was used for training data and output segmentation of region of interest. The performance of the model was evaluated by Intersection over Union and accuracy. Then the predicted masks were highlighted on the original image to give an intuitive evaluation. Finally, cross validation was used for the whole data to test the robust of the results. RESULTS: We selected 562 ultrasound images as the whole dataset. The training set intersection over union (IoU) was 0.713, the development set IoU is 0.633 and the test set IoU is 0.638. For the single image, the median and upper/lower quartiles of IoU were 0.722 (0.647-0.789), 0.653 (0.586-0.703), 0.644 (0.555-0.735) for the training set, development set and test set respectively. The segmentation accuracy of the test set was 83.9%. For 10-fold cross validation, the median and quartiles of the 10-iteration sum IoUs was 0.656 (0.628-0.672); for accuracy, they were 88.4% (82.1-90.7%). CONCLUSIONS: We provided a dataset and trained a model for femoral-nerve region segmentation with U-net, obtaining a satisfactory performance. This technique may have potential clinical application.

Nomograms to predict survival of stage IV tongue squamous cell carcinoma after surgery.

To develop clinical nomograms for prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with stage IV tongue squamous cell carcinoma (TSCC) after surgery based on the Surveillance, Epidemiology, and End Results (SEER) program database.We collected data of resected stage IV TSCC patients from the SEER database, and divided them into the training set and validation set by 7:3 randomly. Kaplan-Meier analysis and Cox regression analysis were adopted to distinguish independent risk factors for OS and CSS. Clinical nomograms were constructed to predict the 3-year and 5-year probabilities of OS and CSS for individual patients. Calibration curves and Harrell C-indices were used for internal and external validation.A total of 1550 patients with resected stage IV TSCC were identified. No statistical differences were detected between the training and validation sets. Age, race, marital status, tumor site, AJCC T/N/M status, and radiotherapy were recognized as independent prognostic factors associated with OS as well as CSS. Then nomograms were developed based on these variables. The calibration curves displayed a good agreement between the predicted and actual values of 3-year and 5-year probabilities for OS and CSS. The C-indices predicting OS were corrected as 0.705 in the training set, and 0.664 in the validation set. As for CSS, corrected C-indices were 0.708 in the training set and 0.663 in the validation set.The established nomograms in this study exhibited good accuracy and effectiveness to predict 3-year and 5-year probabilities of OS and CSS in resected stage IV TSCC patients. They are useful tools to evaluate survival outcomes and helped choose appropriate treatment strategies.

Dexmedetomidine versus midazolam and propofol for sedation in critically ill patients: Mining the Medical Information Mart for Intensive Care data.

BACKGROUND: The benefits of dexmedetomidine on reducing mortality and length of intensive care unit (ICU) stay are still controversial. We aimed to evaluate the superiority of dexmedetomidine by comparing it with midazolam and propofol. METHODS: Subjects who were given dexmedetomidine, midazolam and propofol exclusively as sedatives in the Beth Israel Deaconess Medical Center between 2001 and 2012 were identified from the Medical Information Mart for Intensive Care (MIMIC) III database. Univariate, multivariate and stratified analysis was performed to compare the mortality and length of ICU stay between the dexmedetomidine, midazolam and propofol groups. To compare the depth of sedation between the midazolam and propofol group, we used propensity score matching (PSM) to create comparable units and their Richmond Agitation Sedation Score (RASS) were analyzed. RESULTS: A total of 1,542 unique ICU records were identified in the MIMIC-III database, among which 163 belonged to the dexmedetomidine group and 531 belonged to the midazolam group and 848 belonged to the propofol group. Mortality was decreased in dexmedetomidine group compared with midazolam group (OR 15.25; 95% CI, 5.29-64.80, P<0.001) and propofol group (OR 5.51; 95% CI, 1.91-23.45, P=0.006). In patients with high Simplified Acute Physiologic Score (SAPS) II (>52), midazolam was related to a higher mortality (~50%). But competing risk analysis revealed that dexmedetomidine was associated with longer ICU stay (P<0.001). There was no significant difference in the RASS between propofol and midazolam group (P=0.300). CONCLUSIONS: Dexmedetomidine was significantly related to lower mortality when compared with midazolam and propofol. Midazolam had a comparably higher mortality than propofol and dexmedetomidine in patients with high SAPS II. Propofol and midazolam were equivalent in sedative efficacy. Further evaluation is needed.

Impact of Insurance Status on Stage, Treatment, and Survival in Patients with Colorectal Cancer: A Population-Based Analysis.

BACKGROUND This study aimed to analyze data from the Surveillance, Epidemiology, and End Results (SEER) program to identify patients with colorectal cancer (CRC) who had specific insurance details and the effects of stage at diagnosis, definitive treatment, and survival outcome with insurance status. MATERIAL AND METHODS Between 2007 and 2009, SEER database analysis identified 54,232 patients with CRC. Logistic models examined the associations between insurance status and disease stage and definitive treatment. Kaplan-Meier analysis, the Cox model, and the Fine and Gray model were used to compare the tumor cause-specific survival (TCSS) for patients with different insurance status. RESULTS Insured patients were more likely to have earlier tumor stage at diagnosis when compared with patients receiving Medicaid (adjusted OR, 1.318; 95% CI, 1.249-1.391; P<0.001) and when compared with uninsured patients (adjusted OR, 1.479; 95% CI, 1.352-1.618; P<0.001). Insured patients were significantly more likely to undergo definitive treatment when compared with patients receiving Medicaid (adjusted OR, 0.591; 95% CI, 0.470-0.742; P<0.001) and compared with patients who were uninsured (adjusted OR, 0.404; 95% CI, 0.282-0.579; P<0.001). Insured patients had a significantly increased TCSS when compared with patients receiving Medicaid (HR, 1.298; 95% CI, 1.236-1.363; P<0.001) and compared with patients who were uninsured (HR 1.195, 95% CI, 1.100-1.297; P<0.001). CONCLUSIONS Insurance status was a significant factor that determined early diagnosis, definitive treatment, and clinical outcome and was an independent factor for TCSS in patients with CRC.

Nomogram Predicting Cause-Specific Mortality in Nonmetastatic Male Breast Cancer: A Competing Risk Analysis.

Introduction: Male breast cancer (MBC) is a rare tumor with few cases for research. Using the Surveillance, Epidemiology, and End Results program database, we carried out a competing risk analysis in patients with primary nonmetastatic MBC and built a predictive nomogram. Materials and Methods: We extracted primary nonmetastatic MBC patients according to the inclusion and exclusion criteria. Cumulative incidence function (CIF) and proportional subdistribution hazard model were adopted to explore risk factors for breast cancer-specific death (BCSD) and other cause-specific death (OCSD). Then we built a nomogram to predict the 3-year, 5-year and 8-year probabilities of BCSD and OCSD. C-indexes, Brier scores and calibration curves were chosen for validation. Results: We identified 1,978 nonmetastatic MBC patients finally. CIF analysis showed that the 3-year, 5-year and 8-year mortalities were 5.2%, 10.6% and 16.5% for BCSD, and 6.1%, 9.6% and 14.4% for OCSD. After adjustment of Fine and Gray models, black race, PR (-), advanced T/N/grade and no surgery were independently associated with BCSD. Meanwhile, elderly, unmarried status, advanced AJCC stage and no chemotherapy resulted in OCSD more possibly. A graphic nomogram was developed according to the coefficients from the Fine and Gray models. The calibration curves displayed exceptionally, with C-indexes nearly larger than 0.700 and Brier scores nearly smaller than 0.100. Conclusion: The competing risk nomogram showed good accuracy for predictive prognosis in nonmetastatic MBC patients. It was a useful implement to evaluate crude mortalities of BCSD and OCSD, and help clinicians to choose appropriate therapeutic plans.

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.

Prolonged dexmedetomidine infusion in critically ill adult patients: a retrospective analysis of a large clinical database Multiparameter Intelligent Monitoring in Intensive Care III.

BACKGROUND: There is no conclusive evidence for the effects of prolonged infusion of dexmedetomidine in critically ill patients. We aimed to investigate the safety of long-term dexmedetomidine infusion in a large critically ill patients cohort from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. METHODS: We retrospectively extracted records from MIMIC-III database. Dexmedetomidine administration time was the basis for group. Variables were compared by chi-square tests, and Mann-Whitney U test as appropriate. We used logistic regression model for multivariate analysis. Contour maps were drawn to measure rebound of heart rate (HR) and blood pressure (BP). RESULTS: We finally got 1,946 records including 1,368 distinct individuals. Age, body mass index (BMI), length of stay in hospital, accumulated doses of dexmedetomidine and Sequential Organ Failure Assessment (SOFA) score were independent risk factors of in-hospital mortality (P<0.05). But prolonged dexmedetomidine infusion (≥24 h) and abrupt cessation did not increase in-hospital mortality. Furthermore, the rebound of HR and BP was more likely to occur in patients with prolonged infusion of dexmedetomidine. CONCLUSIONS: Prolonged dexmedetomidine infusion is not related to an increased in-hospital mortality, but it is associated with the rebound effect of HR and BP. Further prospective studies are needed.

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

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.

Bioinformatic Analysis of Potential Biomarkers for Spinal Cord-injured Patients with Intractable Neuropathic Pain.

BACKGROUND: Neuropathic pain is one of the common complications after spinal cord injury (SCI), affecting individuals' quality of life. The molecular mechanism for neuropathic pain after SCI is still unclear. We aimed to discover potential genes and microRNAs (miRNAs) related to neuropathic pain by the bioinformatics method. METHODS: Microarray data of GSE69901 were obtained from Gene Expression Omnibus (GEO) database. Peripheral blood samples from individuals with or without neuropathic pain after SCI were collected. Twelve samples from individuals with neuropathic pain and 13 samples from individuals without pain as controls were included in the downloaded microarray. Differentially expressed genes (DEGs) between the neuropathic pain group and the control group were detected using the GEO2R online tool. Functional enrichment analysis of DEGs was performed using the DAVID database. Protein-protein interaction network was constructed from the STRING database. MiRNAs targeting these DEGs were obtained from the miRNet database. A merged miRNA-DEG network was constructed and analyzed with Cytoscape software. RESULTS: In total, 1134 DEGs were identified between individuals with or without neuropathic pain (case and control), and 454 biological processes were enriched. We identified 4 targeted miRNAs, including mir-204-5p, mir-519d-3p, mir-20b-5p, mir-6838-5p, which may be potential biomarkers for SCI patients. CONCLUSION: Protein modification and regulation of the biological process of the central nervous system may be a risk factor in SCI. Certain genes and miRNAs may be potential biomarkers for the prediction of and potential targets for the prevention and treatment of neuropathic pain after SCI.

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.

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

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.

Gene expression profiles and protein­protein interaction networks during tongue carcinogenesis in the tumor microenvironment.

Oral tongue squamous cell carcinoma (OTSCC) has a high incidence and is associated with a high mortality rate. Studies regarding the potential molecular mechanism of OTSCC in the tumor microenvironment (TME) are required. The present study aimed to perform bioinformatic analysis to identify important nodes, clusters and functional pathways during tongue carcinogenesis in the TME. After downloading the gene expression data of GSE42780, differentially expressed genes (DEGs) among carcinoma, dysplastic and normal samples in epithelia and fibroblasts were identified using the affy and limma packages with R version 3.3. Subsequently, the Database for Annotation, Visualization and Integrated Discovery was employed to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Furthermore, a protein­protein interaction (PPI) network was constructed by using the Search Tool for the Retrieval of Interacting Genes/Proteins and analyzed by Cytoscape software. In total, 85 DEGs were identified for tongue epithelia and 46 DEGs were identified for fibroblasts. Neutrophil chemotaxis and inflammatory response from GO, and cytokine­cytokine receptor interaction from KEGG were enriched for epithelia and fibroblasts. The PPI network revealed that C­X­C motif chemokine ligand (Cxcl)1, Cxcl10, Cxcl13, Cxcl2 and pro­platelet basic protein were a key cluster for epithelia, and interleukin (Il)1ß, Il1 receptor 2, Il1a and Il1 receptor antagonist were a key cluster for fibroblasts. Therefore, the results indicate that fibroblasts and cytokines associated with an inflammatory immune response contributed substantially to tongue carcinogenesis in the TME, which is useful for the development of OTSCC targeted therapy. However, further investigation is required to elucidate the molecular and cellular mechanisms underlying the inflammatory immune network in the TME.

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.

The influence of marital status on survival in patients with oral tongue squamous cell carcinoma.

Marital status was found to be an independent prognostic factor for survival in several cancers. However related researches of oral tongue squamous cell carcinoma (OTSCC) are still rare. We explored the Surveillance, Epidemiology, and End Results (SEER) program and finally identified 14,194 patients with OTSCC. Kaplan-Meier analysis and multivariate Cox regression models were used to distinguish risk factors for overall survival (OS) and tumor cause-specific survival (TCSS). Widowed patients had the highest percentage of female, highest average ages and more prevalence with localized SEER Stage significantly, while patients in the single group were younger than other groups. After univariate analysis and multivariate analysis, marital status was demonstrated to be an independent prognostic factor of OS and TCSS. Married patients showed better 5-year OS (65.6%) and 5-year TCSS (89.9%) than other patients. Subgroup survival analysis according to AJCC TNM stage and SEER stage showed that the widowed patients demonstrated worst OS and TCSS compared to other groups. Marital status was an important prognostic factor for survival in patients with OTSCC. Widowed patients exhibited with the highest risk of death compared with other groups.

Racial disparities in pancreatic neuroendocrine tumors survival: a SEER study.

Pancreatic neuroendocrine tumor (pancreatic NETs), is an important cause of cancer-related death worldwide. No study has rigorously explored the impact of ethnicity on pancreatic NETs. We aimed to demonstrate the relationship between ethnicity and the survival of patients with pancreatic NETs. We used the SEER database to identify patients with pancreatic NETs from 2004 to 2013. Kaplan-Meier methods and Cox proportional hazard models were used to evaluate the impact of race on survival in pancreatic NETs patients. A total of 3850 patients were included: 3357 Non-Blacks, 493 Blacks. We stratified races as "Black" and "White/Other." Blacks were more likely to be diagnosed with later stages of tumors (P = 0.021). As for the treatment, the access to surgery seemed to be more limited in Blacks than non-Black patients (P = 0.012). Compared with non-Black patients, Black patients have worse overall survival (OS) (HR = 1.17, 95% CI: 1.00-1.37, P = 0.046) and pancreatic neuroendocrine tumors specific survival (PNSS) (HR = 1.22, 95% CI: 1.01-1.48, P = 0.044). Multivariate Cox analysis identified that disease extension at the time of diagnosis and surgical status contributed to the ethnical survival disparity. Black patients whose stages at diagnosis were localized had significantly worse OS (HR = 2.09, 95% CI: 1.18-3.71, P = 0.011) and PNSS (HR = 3.79, 95% CI: 1.62-8.82, P = 0.002). As for the patients who did not receive surgery, Blacks also have a worse OS (HR = 1.18, 95% CI: 1.00-1.41, P = 0.045). The Black patients had both worse OS and PNSS compared to non-Black patients. The restricted utilization of surgery, and the advanced disease extension at the time of diagnosis are the possible contributors to poorer survival of Blacks with pancreatic NETs.

Gene microarray analysis of expression profiles in liver ischemia and reperfusion.

Liver ischemia and reperfusion (I/R) injury is of primary concern in cases of liver disease worldwide and is associated with hemorrhagic shock, resection and transplantation. Numerous studies have previously been conducted to investigate the underlying mechanisms of liver I/R injury, however these have not yet been fully elucidated. To determine the difference between ischemia and reperfusion in signaling pathways and the relative pathological mechanisms, the present study downloaded microarray data GSE10657 from the Gene Expression Omnibus database. A total of two data groups from 1­year­old mice were selected for further analysis: i) A total of 90 min ischemia; ii) 90 min ischemia followed by 1 h of reperfusion, n=3 for each group. The Limma package was first used to identify the differentially expressed genes (DEGs). DEGs were subsequently uploaded to the Database for Annotation Visualization and Integrated Discovery online tool for Functional enrichment analysis. A protein­protein interaction (PPI) network was then constructed via STRING version 10.0 and analyzed using Cytoscape software. A total of 114 DEGs were identified, including 21 down and 93 upregulated genes. These DEGs were primarily enriched in malaria and influenza A, in addition to the tumor necrosis factor and mitogen activated protein kinase signaling pathways. Hub genes identified in the PPI network were C­X­C motif chemokine ligand (CXCL) 1, C­C motif chemokine ligand (CCL) 2, interleukin 6, Jun proto­oncogene, activator protein (AP)­1 transcription factor subunit, FOS proto­oncogene, AP­1 transcription factor subunit and dual specificity phosphatase 1. CXCL1 and CCL2 may exhibit important roles in liver I/R injury, with involvement in the immune and inflammatory responses and the chemokine­mediated signaling pathway, particularly at the reperfusion stage. However, further experiments to elucidate the specific roles of these mediators are required in the future.

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.