A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.

Background: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. Methods: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. Results: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. Conclusions: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.

BACKGROUNDS: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients. METHODS: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance. RESULTS: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428). CONCLUSIONS: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

Development and validation of a prognostic predictive model of pulmonary spindle cell carcinoma from the surveillance, epidemiology and end results database.

Background: Pulmonary spindle cell carcinoma (PSCC) is a rare type of non-small cell lung cancer (NSCLC). The prognostic influent factors and therapeutic methods of PSCC are unclear, for there are only some case reports or small samples' analysis. This study aims to find prognosis related factors of PSCC, develop and validate a nomogram to predict their survival probability. Methods: The Surveillance, Epidemiology, and End Results (SEER) 18 Registries database (2000-2018) was searched to study PSCC. According to diagnosed time, data was divided into primary cohort (2000-2015) and validation cohort (2016-2018), both followed until December 31 2018. Chosen by Least Absolute Shrinkage and Selection Operator (LASSO) regression, age, sex, stage, surgery, chemotherapy, N, size and history of malignancy were taken out as predictive variables. The primary cohort was used to develop a nomogram to predict 1-, 3- and 5-year overall survival (OS) probability, and be validated by the validation cohort using concordance index (C-index) and calibration curves. Both cohorts were used to conduct a Cox regression to find the influential factors on OS of PSCC. Results: The nomogram shows a good concordance and discrimination on the prediction of OS, both internal (n=457 and C-index is 0.79) and external validation (n=100 and C-index is 0.76). The median survival time of PSCC is 4 months, with 20.1% OS possibility in 5 years. Multivariate analysis identified patients of older age [hazard ratio (HR), 1.02; 95% confidence interval (CI): 1.01-1.04], larger size of neoplasm (HR, 1.01; 95% CI: 1.01-1.01), M1 (HR, 2.96; 95% CI: 2.17-4.04), N2 (HR, 2.55; 95% CI: 1.81-3.59) or N3 (HR, 2.99; 95% CI: 1.58-5.66), regional stages (HR, 2.11; 95% CI: 1.29-3.44) and distant stages (HR, 6.17; 95% CI: 3.83-9.94) had a lower OS possibility, while surgery (HR, 0.39; 95% CI: 0.28-0.53) and history of malignancy (HR, 0.68; 95% CI: 0.48-0.98) was protective factors for PSCC. PSCC survived longer with surgery performed instead of chemotherapy or radiotherapy. Conclusions: Patients of PSCC have a poor prognosis, and using the nomogram developed by this study can predict their 1-, 3- and 5-year OS probability. Surgery is a better choice for PSCC and more studies are necessary to find potential treatment like targeted therapy, programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1).

Long non-coding RNA BZRAP1-AS1 functions in malignancy and prognosis for non-small-cell lung cancer.

Purpose: The function of BZRAP1-AS1 is unknown in lung cancer. We evaluated the clinicopathologic significance of BZRAP1-AS1, and its role in non-small-cell lung cancer (NSCLC) progression. Patient and methods: Sixty-three NSCLC patients from Beijing Chest Hospital were included. The expression of BZRAP1-AS1 was detected by real-time quantitative polymerase chain reaction (RT-qPCR) in tumor tissues and adjacent normal tissues. Then, the clinicopathological significance and prognostic value of BZRAP1-AS1 were analyzed by using our cohort and TCGA cohort. Finally, the effect of BZRAP1-AS1 on proliferation and motility of NSCLC cell lines were evaluated by cell growth assay, colony formation assay, xenograft tumorigenesis experiment in nude mice and transwell assays respectively. Results: Compared with adjacent normal tissues, BZRAP1-AS1 showed lower expression in NSCLC tumor tissues. As for the relationship between BZRAP1-AS1 and clinical characteristics, our results were consistent with those of TCGA data. BZRAP1-AS1 was lower in T1 than T2-T4 patients, N1-N3 than N0 patients. Low level BZRAP1-AS1 was related to shorter overall survival time (OS) in lung adenocarcinoma (LUAD), and poor first progression time (FP) in LUAD and lung squamous cell carcinoma (LUSC) patients. BZRAP1-AS1 was significantly associated with the prognosis of NSCLC patients. Overexpression of BZRAP1-AS1 inhibited proliferation and migration of H1299 and HCC827 cells. Conclusion: BZRAP1-AS1 expression decreases in tumor tissues with the increase of malignancy grades in NSCLC. BZRAP1-AS1 plays an anticancer role by inhibiting cell proliferation, invasion, and metastasis, and has a potential prognostic value in NSCLC. BZRAP1-AS1 may serve as a diagnostic marker and therapeutic target for NSCLC.

Subsegmentectomy versus segmentectomy resection for the treatment of operable patients with stage IA non-small cell lung cancer: A meta-analysis.

Background: There were new points of interest in performing subsegmentectomy and segmentectomy for patients with early stage non-small cell lung cancer (NSCLC). However, whether patients who underwent subsegmentectomy could obtain satisfactory clinical outcomes remains unclear. The present study aimed to compare the clinical outcomes and security of surgical procedures between subsegmentectomy and segmentectomy. Methods: A systematic review and meta-analysis was performed through five online databases to identify the included literatures which presented intact clinical outcome data among different surgical procedures. The included studies were evaluated based on precise and predefined inclusion criteria. Results: There were 4 published studies identified in this meta-analysis. A total of 325 patients who underwent subsegmentectomy and 904 patients who underwent segmentectomy were involved in this analysis. The duration of drainage [MD -0.19; 95%CI (-0.36, -0.02), p = 0.03] and postoperative hospital stay [MD -0.30; 95%CI (-0.58, -0.02), p = 0.009] of subsegmentectomy were significantly less than that of segmentectomy. There was no statistically significant difference among recurrence rate [OR 0.85; 95%CI (0.21, 3.42), p = 0.82], operation time, blood loss, incidence of complications [OR 0.83; 95%CI (0.58, 1.20), p = 0.33] between subsegmentectomy and segmentectomy in patients with stage IA NSCLC. Conclusion: The meta-analysis was firstly performed to compare perioperative outcomes among surgical procedures. The perioperative outcomes were comparable between subsegmentectomy and segmentectomy. Subsegmentectomy might be an alternative treatment for the deep tumor with size less than 1.5 cm and mainly composed of Ground Glass Opacity (GGO).

OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma.

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank p value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as PROM1 (HR = 2.4120, p = 0.0071) and CXCR4 (HR = 1.5578, p < 0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp.

Regulation of G protein-coupled cAMP receptor activation by a hydrophobic residue in transmembrane helix 3.

cAR1, a G protein-coupled cAMP receptor, is essential for multicellular development of Dictyostelium. We previously identified a cAR1-Ile(104) mutant that appeared to be constitutively activated based on its constitutive phosphorylation, elevated affinity for cAMP, and dominant-negative effects on development as well as specific cAR1 pathways that are subject to adaptation. To investigate how Ile(104) might regulate cAR1 activation, we assessed the consequences of substituting it with all other amino acids. Constitutive phosphorylation of these Ile(104) mutants varied broadly, suggesting that they are activated to varying extents, and was correlated with polarity of the substituting amino acid residue. Remarkably, all Ile(104) substitutions, except for the most conservative, dramatically elevated the receptor's cAMP affinity. However, only a third of the mutants (those with the most polar substitutions) blocked development. These findings are consistent with a model in which polar Ile(104) substitutions perturb the equilibrium between inactive and active cAR1 conformations in favour of the latter. Based on homology with rhodopsin, Ile(104) is likely buried within inactive cAR1 and exposed to the cytoplasm upon activation. We propose that the hydrophobic effect normally promotes burial of Ile(104) and hence cAR1 inactivation, while polar substitution of Ile(104) mitigates this effect, resulting in activation.

CHIP protects from the neurotoxicity of expanded and wild-type ataxin-1 and promotes their ubiquitination and degradation.

CHIP (C terminus of Hsc-70 interacting protein) is an E3 ligase that links the protein folding machinery with the ubiquitin-proteasome system and has been implicated in disorders characterized by protein misfolding and aggregation. Here we investigate the role of CHIP in protecting from ataxin-1-induced neurodegeneration. Ataxin-1 is a polyglutamine protein whose expansion causes spinocerebellar ataxia type-1 (SCA1) and triggers the formation of nuclear inclusions (NIs). We find that CHIP and ataxin-1 proteins directly interact and co-localize in NIs both in cell culture and SCA1 postmortem neurons. CHIP promotes ubiquitination of expanded ataxin-1 both in vitro and in cell culture. The Hsp70 chaperone increases CHIP-mediated ubiquitination of ataxin-1 in vitro, and the tetratricopeptide repeat domain, which mediates CHIP interactions with chaperones, is required for ataxin-1 ubitiquination in cell culture. Interestingly, CHIP also interacts with and ubiquitinates unexpanded ataxin-1. Overexpression of CHIP in a Drosophila model of SCA1 decreases the protein steady-state levels of both expanded and unexpanded ataxin-1 and suppresses their toxicity. Finally we investigate the ability of CHIP to protect against toxicity caused by expanded polyglutamine tracts in different protein contexts. We find that CHIP is not effective in suppressing the toxicity caused by a bare 127Q tract with only a short hemagglutinin tag, but it is very efficient in suppressing toxicity caused by a 128Q tract in the context of an N-terminal huntingtin backbone. These data underscore the importance of the protein framework for modulating the effects of polyglutamine-induced neurodegeneration.

Constitutively active G protein-coupled receptor mutants block dictyostelium development.

cAR1, a G protein-coupled receptor (GPCR) for cAMP, is required for the multicellular development of Dictyostelium. The activation of multiple pathways by cAR1 is transient because of poorly defined adaptation mechanisms. To investigate this, we used a genetic screen for impaired development to isolate four dominant-negative cAR1 mutants, designated DN1-4. The mutant receptors inhibit multiple cAR1-mediated responses known to undergo adaptation. Reduced in vitro adenylyl cyclase activation by GTPgammaS suggests that they cause constitutive adaptation of this and perhaps other pathways. In addition, the DN mutants are constitutively phosphorylated, which normally requires cAMP binding and possess cAMP affinities that are approximately 100-fold higher than that of wild-type cAR1. Two independent activating mutations, L100H and I104N, were identified. These residues occupy adjacent positions near the cytoplasmic end of the receptor's third transmembrane helix and correspond to the (E/D)RY motif of numerous mammalian GPCRs, which is believed to regulate their activation. Taken together, these findings suggest that the DN mutants are constitutively activated and block development by turning on natural adaptation mechanisms.

A cAMP receptor-like G protein-coupled receptor with roles in growth regulation and development.

Dictyostelium discoideum uses G protein-mediated signal transduction for many vegetative and developmental functions, suggesting the existence of G protein-coupled receptors (GPCRs) other than the four known cyclic adenosine monophosphate (cAMP) receptors (cAR1-4). Sequences of the cAMP receptors were used to identify Dictyostelium genes encoding cAMP receptor-like proteins, CrlA-C. Limited sequence identity between these putative GPCRs and the cAMP receptors suggests the Crl receptors are unlikely to be receptors for cAMP. The crl genes are expressed at various times during growth and the developmental life cycle. Disruption of individual crl genes did not impair chemotactic responses to folic acid or cAMP or alter cAMP-dependent aggregation. However, crlA(-) mutants grew to a higher cell density than did wild-type cells and high-copy-number crlA expression vectors were detrimental to cell viability, suggesting that CrlA is a negative regulator of cell growth. In addition, crlA(-) mutants produce large aggregates with delayed anterior tip formation indicating a role for the CrlA receptor in the development of the anterior prestalk cell region. The scarcity of GFP-expressing crlA(-) mutants in the anterior prestalk cell region of chimeric organisms supports a cell-autonomous role for the CrlA receptor in prestalk cell differentiation.