The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machine learning methods.

Accurately predicting patient risk and identifying survival biomarkers are two important tasks in survival analysis. For the emerging high-throughput gene expression data, random survival forest (RSF) is attracting more and more attention as it not only shows excellent performance on survival prediction problems with high-dimensional variables, but also is capable of identifying important variables according to variable importance automatically calculated within the algorithm. However, RSF still suffers from some problems such as limited predictive accuracy on independent datasets and limited biological interpretation of survival biomarkers. In this study, we integrated gene interaction information into a Reweighted RSF model (RRSF) to improve predictive accuracy and identify biologically meaningful survival markers. We applied RRSF to the prediction of patients with glioblastoma multiforme (GBM) and esophageal squamous cell carcinoma (ESCC). With a reconstructed global pathway network and an mRNA-lncRNA co-expression network as the prior gene interaction information, RRSF showed better overall predictive performance than RSF on three GBM and two ESCC datasets. In addition, RRSF identified a two-gene and three-lncRNA signature, which showed robust prognostic values and had high biological relevance to the development of GBM and ESCC, respectively.

The neuronal olfactory epithelium undergoes permanent renewal because of environmental aggression. This renewal is partly regulated by factors modulating the level of neuronal apoptosis. Among them, we had previously characterized endothelin as neuroprotective. In this study, we explored the effect of cell survival factor deprivation in the olfactory epithelium by intranasal delivery of endothelin receptors antagonists to rat pups. This treatment induced an overall increase of apoptosis in the olfactory epithelium. The responses to odorants recorded by electroolfactogram were decreased in treated animal, a result consistent with a loss of olfactory sensory neurons (OSNs). However, the treated animal performed better in an olfactory orientation test based on maternal odor compared to non-treated littermates. This improved performance could be due to activity-dependent neuronal survival of OSNs in the context of increased apoptosis level. In order to demonstrate it, we odorized pups with octanal, a known ligand for the rI7 olfactory receptor (Olr226). We quantified the number of OSN expressing rI7 by RT-qPCR and whole mount in situ hybridization. While this number was reduced by the survival factor removal treatment, this reduction was abolished by the presence of its ligand. This improved survival was optimal for low concentration of odorant and was specific for rI7-expressing OSNs. Meanwhile, the number of rI7-expressing OSNs was not affected by the odorization in non-treated littermates; showing that the activity-dependant survival of OSNs did not affect the OSN population during the 10 days of odorization in control conditions. Overall, our study shows that when apoptosis is promoted in the olfactory mucosa, the activity-dependent neuronal plasticity allows faster tuning of the olfactory sensory neuron population toward detection of environmental odorants.

The aim of this study was to develop nomograms to predict long-term overall survival and cancer-specific survival of patients with osteosarcoma.We carried out univariate and multivariate analyses and set up nomograms predicting survival outcome using osteosarcoma patient data collected from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute (2004-2011, n = 1426). The patients were divided into a training cohort (2004-2008, n = 863) and a validation cohort (2009-2011, n = 563), and the mean follow-up was 55 months.In the training cohort, 304 patients (35.2%) died from osteosarcoma and 91 (10.5%) died from other causes. In the validation cohort, 155 patients (27.5%) died from osteosarcoma and (12.3%) died from other causes. Nomograms predicting overall survival (OS) and cancer-specific survival (CSS) were developed according to 6 clinicopathologic factors (age, tumor site, historic grade, surgery, AJCC T/N, and M), with concordance indexes (C-index) of 0.725 (OS) and 0.718 (CSS), respectively. The validation C-indexes were 0.775 and 0.742 for OS and CSS, respectively.Our results suggest that we have successfully developed highly accurate nomograms for predicting 5-year OS and CSS for osteosarcoma patients. These nomograms will help surgeons customize treatment and monitoring strategies for osteosarcoma patients.

Cause-specific and relative survival estimates differ. We aimed to examine these differences in common cancers where by possible identifying the most plausible sources of error in each estimate.

The increasing number of sequential treatments complicates the evaluation of overall survival (OS) in clinical trials for hepatocellular carcinoma (HCC), therefore, reliable surrogate endpoints (SEs) are required. This study aimed to evaluate the surrogacy of progression-free survival (PFS) and one-year (1-yr) milestone survival for OS in HCC trials.

As the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on outcomes for intracranial combined solitary-fibrous tumor and hemangiopericytoma (SFT/HPC). This study aimed to explore conditional survival (CS) pattern and develop a survival prediction tool for intracranial SFT/HPC patients.

The Survival Kit is a Fortran 90 Software intended for survival analysis using proportional hazards models and their extension to frailty models with a single response time. The hazard function is described as the product of a baseline hazard function and a positive (exponential) function of possibly time-dependent fixed and random covariates. Stratified Cox, grouped data and Weibull models can be used. Random effects can be either log-gamma or normally distributed and can account for a pedigree structure. Variance parameters are estimated in a Bayesian context. It is possible to account for the correlated nature of two random effects either by specifying a known correlation coefficient or estimating it from the data. An R interface of the Survival Kit provides a user friendly way to run the software.

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/ .

Objective: This study aimed to investigate the roles of MRPL27 in survival from cholangiocarcinoma patients in The Cancer Genome Atlas (TCGA) database. Methods: In TCGA-CHOL profile, MRPL27 gene expression and clinical data were obtained. Cox regression models were used to evaluate the potential links between MRPL27 and cholangiocarcinoma survival. Enrichment analysis of MRPL27 was conducted in Metascape and Gene Set Enrichment Analysis (GSEA) databases. Results: 36 cholangiocarcinoma patients were included in this analysis. MRPL27 mRNA was significantly upregulated in tumor tissues in cholangiocarcinoma patients including intrahepatic, distal and hilar/perihilar cholangiocarcinoma cases (all p < 0.01). Cholangiocarcinoma patients with high MRPL27 had worse overall survival (OS) and disease-free survival (DFS) compared to those with low MRPL27 (all p < 0.05). Univariate and multivariate Cox models indicated that MRPL27 should be a risk factor for the OS and DFS in cholangiocarcinoma patients (both p < 0.01). Bioinformatic analysis revealed that MRPL27 mainly involved in the processes of mitochondrial translation elongation, respiratory electron transport, ATP synthesis, and inner mitochondrial membrane organization. No mutations of MRPL27 were screened in cholangiocarcinoma patients. Conclusion: Upregulated in tumors, MRPL27 contributes to unfavorable survival in cholangiocarcinoma patients.

To predict the survival of appendiceal mucinous adenocarcinoma (AMA) by prognostic nomogram.A total of 3234 patients with AMA were collected from the Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 2015. Univariate and multivariate Cox proportional hazards (PH) regression analyses were used to generate independent prognostic factors. These variables were included in the nomogram to predict overall survival (OS) and disease-specific survival (DSS) at 1-, 3-, and 5- years. These data are validated both internally and externally. The consistency index (C-index) and calibration chart were used to estimate the accuracy of the nomogram.The study cohort was randomly divided into the training (n = 2155) and validation group (n = 1799). According to univariate and multivariate analyses, age at diagnosis, marital status, sex, histological differentiation, SEER extent of disease, number of local lymph nodes examined, whether they were positive, and surgical methods were independent prognostic factors for OS and DSS. These factors were incorporated into the nomogram. Internal validation in the training cohort showed that the C-index values for nomogram predictions of OS and DSS were 0.73 (95% CI 0.70-0.76) and 0.77 (95% CI 0.73-0.81), respectively. Similarly, the corresponding C-index values in the external validation cohort were 0.76 (95% CI 0.70-0.81) and 0.75 (95% CI 0.71-0.80). The Calibration plots revealed that the actual survival and nomogram prediction had a good consistency.Build a nomogram in the SEER database to predict OS and DSS in patients with AMA. It can provide accurate and personalised survival prediction for clinicians and patients.

Total Pelvic Exenteration (TPE) is a radical operation for malignancies in which all of the organs inside the pelvic cavity, including the female reproductive organs, the lower urinary tract, and a part of the rectosigmoid are removed. In this study, we aimed to conduct a systematic review to assess the overall survival (OS) and disease-free survival (DFS) following TPE.

Recent studies claiming to revive ancient microorganisms trapped in fluid inclusions in halite have warranted an investigation of long-term microbial persistence. While starvation-survival is widely reported for bacteria, it is less well known for halophilic archaea-microorganisms likely to be trapped in ancient salt crystals. To better understand microbial survival in fluid inclusions in ancient evaporites, laboratory experiments were designed to simulate growth of halophilic archaea under media-rich conditions, complete nutrient deprivation, and a controlled substrate condition (glycerol-rich) and record their responses. Haloarchaea used for this work included Hbt. salinarum and isolate DV582A-1 (genus Haloterrigena) sub-cultured from 34 kyear Death Valley salt. Hbt. salinarum and DV582A-1 reacted to nutrient limitation with morphological and population changes. Starved populations increased and most cells converted from rods to small cocci within 56 days of nutrient deprivation. The exact timing of starvation adaptations and the physical transformations differed between species, populations of the same species, and cells of the same population. This is the first study to report the timing of starvation strategies for Hbt. salinarum and DV582A-1. The morphological states in these experiments may allow differentiation between cells trapped with adequate nutrients (represented here by early stages in nutrient-rich media) from cells trapped without nutrients (represented here by experimental starvation) in ancient salt. The hypothesis that glycerol, leaked from Dunaliella, provides nutrients for the survival of haloarchaea trapped in fluid inclusions in ancient halite, is also tested. Hbt. salinarum and DV582A-1 were exposed to a mixture of lysed and intact Dunaliella for 56 days. The ability of these organisms to utilize glycerol from Dunaliella cells was assessed by documenting population growth, cell length, and cell morphology. Hbt. salinarum and DV582A-1 experienced size reductions and shape transitions from rods to cocci. In the short-term, these trends more closely resembled the response of these organisms to starvation conditions than to nutrient-rich media. Results from this experiment reproduced the physical state of cells (small cocci) in ancient halite where prokaryotes co-exist with single-celled algae. We conclude that glycerol is not the limiting factor in the survival of haloarchaea for thousands of years in fluid inclusions in halite.

Net survival is commonly quantified as relative survival (observed survival among lung cancer patients versus expected survival among the general population) and cause-specific survival (lung cancer-specific survival among lung cancer patients). These approaches have drastically different assumptions; hence, failure to distinguish between them results in significant implications for study findings. We quantified the differences between relative and cause-specific survival when reporting net survival of patients with non-small cell lung cancer (NSCLC).

To develop nomogram models to predict individualized estimates of overall survival (OS) and cancer-specific survival (CSS) in patients with adrenocortical carcinoma (ACC).

Relapse-free survival (RFS) has been considered a primary endpoint to assess the effects of immunotherapy in the adjuvant setting among patients with early-stage disease. However, it is not clear whether RFS is a valid surrogate endpoint for overall survival (OS) in this clinical context.

Recent years, there has been a rapid increase in the incidence of esophageal adenocarcinoma (EAC), while the prognosis for patients diagnosed remains poor and has slightly improved.

Background: Colorectal cancer (CRC) ranks as the third most frequent cancer type and the second leading cause of cancer-related death worldwide. The liver is the most common metastatic site of CRC with 20%-34% of patients suffering synchronous liver metastasis. Patients with colorectal liver-limited metastasis account for one-third of deaths from colorectal cancer. Moreover, some evidence indicated that CRC patients with synchronous liver disease encounter a worse prognosis and more disseminated disease state comparing with metastatic liver disease that develops metachronously. Methods: Data in this retrospective analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed with basis from a multivariate Cox regression analysis. The prognostic nomograms were validated by C-index, time-dependent receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and calibration curves. Results: A total of 9,958 CRC patients with synchronous liver-limited metastasis were extracted from the SEER database during 2010-2016. Both overall survival (OS) and cancer-specific survival (CSS) were significantly correlated with age, marital status, race, tumor location, pathological grade, histologic type, T stage, N stage, surgery for primary tumor, surgery for liver metastasis, chemotherapy and CEA. All of the significant variables were used to create the nomograms predicting OS and CSS. C-index values, time-dependent ROC curves, DCA curves and calibration curves, proved the superiority of the nomograms. Conclusions: Our research investigated a national cohort of almost 10,000 patients to create and verify nomograms based on pathological, therapeutic and demographic features to predict OS and CSS for synchronous colorectal liver-limited metastasis (SCLLM). The nomograms may act as an excellent tool to integrate clinical characteristics to guide the therapeutic choice for SCLLM patients.

Metastatic colorectal cancer (mCRC) is a highly heterogeneous disease from a clinical, molecular, and immunological perspective. Current predictive models rely primarily in tissue based genetic analysis, which not always correlate with inflammatory response. Here we evaluated the role of a circulating inflammatory signature as a prognostic marker in mCRC.

So far, no randomized trial or meta-analysis has been conducted on overall survival (OS) and recurrence-free survival (RFS) factors in patients treated with radiofrequency ablation (RFA) alone. The purpose of this meta-analysis was to evaluate prognostic factors of OS and RFS in patients treated with RFA.