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Background: The aim of this work was to screen and validate biomarkers of ovarian cancer-initiating cells to detect the mechanisms of recurrence of epithelial ovarian cancer (EOC).Methods: Stably labelled the amino acid in side population (SP) cells of epithelial ovarian cancer which were rich in cancer-initiating cells and non-SP cells with isotope in culture and differentially expressed cellular membrane proteins in SP cells were identified through proteomics technology. The new candidate biomarker was screened and validated through RT-PCR and western blot. Both in cell lines and primary EOC, cancer-initiating biofunctions of CDC50A positive cells were validated. Moreover, the characteristics of mesenchymal transition (EMT) was also detected and the correlation between the biomarker and clinical prognosis was observed.Results: Through proteomics technology, candidate protein CDC50A was screened, and its significantly differential expression in SP cells was validated. CDC50A-positive cells from cell lines and primary ovarian cancer tissues were validated to show characteristics of cancer-initiating cells both in vitro and in vivo, including sphere-forming, self-renewal, differentiation, tumor metastasis and tumorigenicity in mice. The relationship between CDC50A-positive cells from primary tissues and tumour metastasis was confirmed based on their mesenchymal transition characteristics. Among 16 high-grade ovarian serous cancer patients, a high ratio of CDC50A-positive cells in primary tumours was correlated with a shorter platinum-free interval (p = 0.031, HR 0.260, 95% CI 0.77 ~ 0.885).Conclusion: CDC50A could be used to screen ovarian cancer-initiating cells and might be a new target to resolve tumour development in EOC patients. [ABSTRACT FROM AUTHOR]

Background: Monocyte count and serum albumin (Alb) have been proven to be involved in the process of systemic inflammation. Therefore, we investigated the prognostic value of monocyte-to-albumin ratio (MAR) in patients who underwent percutaneous coronary intervention (PCI). Methods: We enrolled a total of 3561 patients in the present study from January 2013 to December 2017. They were divided into two groups according to MAR cut-off value (MAR < 0.014, n=2220; MAR = 0.014, n=1119) as evaluated by receiver operating characteristic (ROC) curve. The average follow-up time was 37.59 +- 22.24 months. Results: The two groups differed significantly in the incidences of all-cause mortality (ACM; P<0.001), cardiac mortality (CM; P<0.001), major adverse cardiovascular events (MACEs; P=0.038), and major adverse cardiovascular and cerebrovascular events (MACCEs; P=0.037). Multivariate Cox regression analyses revealed MAR as an independent prognostic factor for ACM and CM. The incidence of ACM increased by 56.5% (hazard ratio [HR] = 1.565; 95% confidence interval [CI], 1.086-2.256; P=0.016) and that of CM increased by 76.3% (HR = 1.763; 95% CI, 1.106-2.810; P=0.017) in patients in the higher-MAR group. Kaplan-Meier survival analysis suggested that patients with higher MAR tended to have an increased accumulated risk of ACM (Log-rank P<0.001) and CM (Log-rank P<0.001). Conclusion: The findings of the present study suggested that MAR was a novel independent predictor of long-term mortality in patients who underwent PCI. [ABSTRACT FROM AUTHOR]

Background: Sidedness (right/left) of colorectal cancer (CRC) is essential for treatment. Whether carcinogenesis of tobacco varies by sidedness remains unclear. The present study aims to evaluate the sidedness tendency of cigarette smoking and to explore its impact on prognosis.Methods: In the multi-center retrospective study, data on 46 166 Chinese CRC patients were extracted from a big-data platform. Logistic regression analyses were performed to evaluate qualitative and quantitative associations between smoking and tumor sidedness. Survival analyses were conducted in metastatic CRC.Results: History of smoking was associated with left-sided CRC (LSCRC; Adjusted odds ratio, 1.25; 95% CI, 1.16 - 1.34; P < .001). The sidedness tendency towards LSCRC increased from non-smokers, to ex-smokers, and to current smokers (P for trend < .001). Longer duration (P for trend < .001) and larger total amount of cigarette smoking (P for trend < .001) were more associated with LSCRC, respectively. The association was confirmed in both left-sided colon cancer and rectal cancer, but was stronger for rectal cancer (P = .016). Alcoholism significantly enhanced the association by 7% (P = .027). Furthermore, prognostic advantage of metastatic LSCRC diminished among ever-smokers, with contrary survival impacts of smoking on either side of CRC.Conclusions: History of smoking was associated with LSCRC in a positive dose-response relationship, and presented opposite prognostic impacts on right- and left-sided tumors. Smoking potentially plays an instrumental role in the mechanism for sidedness heterogeneity in CRC. [ABSTRACT FROM AUTHOR]

Background: Aberrant DNA methylation is significantly associated with breast cancer. Methods: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. Results: In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Conclusions: Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: To identify how Epstein‐Barr virus (EBV) status combined with molecular profiling predicts the prognosis of gastric cancer patients and their associated clinical actionable biomarkers. Experimental Design: A next‐generation sequencing assay targeting 295 cancer‐related genes was performed in 73 EBV‐associated gastric cancer (EBVaGC) and 75 EBV‐negative gastric cancer (EBVnGC) specimens and these results were compared with overall survival (OS). Results: PIK3CA, ARID1A, SMAD4, and PIK3R1 mutated significantly more frequently in EBVaGC compared with their corresponding mutation rate in EBVnGC. As the most frequently mutated gene in EBVnGC (62.7%), TP53 also displayed a mutation rate of 15.1% in EBVaGC. PIK3R1 was revealed as a novel mutated gene (11.0%) associated almost exclusively with EBVaGC. PIK3CA, SMAD4, PIK3R1, and BCOR were revealed to be unique driver genes in EBVaGC. ARID1A displayed a significantly large proportion of inactivated variants in EBVaGC. A notable finding was that integrating the EBV status with tumor mutation burden (TMB) and large genomic instability (LGI) categorized the tumors into four distinct molecular subtypes and optimally predicted patient prognosis. The corresponding median OSs for the EBV+/TMB‐high, EBV+/TMB‐low, EBV‐/LGI‐, and EBV‐/LGI+ subtypes were 96.2, 75.3, 44.4, and 20.2 months, respectively. The different subtypes were significantly segregated according to distinct mutational profiles and pathways. Conclusions: Novel mutations in PIK3R1 and TP53 genes, driver genes such as PIK3CA, SMAD4, PIK3R1, BCOR, and ARID1A, and distinguished genomic profiles from EBVnGC were identified in EBVaGC tumors. The classification of gastric cancer by EBV, TMB, and LGI could be a good prognostic indicator, and provides distinguishing, targetable markers for treatment. [ABSTRACT FROM AUTHOR]

Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineering or prior knowledge to model at an individual level. Some survival analysis models can avoid these problems by using machine learning extended the CPH model, and higher performance has been reported. In this paper, we propose an innovative loss function that is defined as the sum of an extended mean squared error loss and a pairwise ranking loss based on ranking information on survival data. We apply this loss function to optimize a deep feed-forward neural network (RankDeepSurv), which can be used to model survival data. We demonstrate that the performance of our model, RankDeepSurv, is superior to that of other state-of-the-art survival models based on an analysis of 4 public medical clinical datasets. When modelling the prognosis of nasopharyngeal carcinoma (NPC), RankDeepSurv achieved better prognostic accuracy than the CPH established by clinical experts. The difference between high and low risk groups in the RankDeepSurv model is greater than the difference in the CPH. The results show that our method has considerable potential to model survival data in medical settings. [ABSTRACT FROM AUTHOR]

The prognostic heterogeneity in patients with BRAF V600E metastatic colorectal cancer (mCRC) remains poorly defined. Real-world data of 93 BRAF V600E mCRC patients from Sun Yat-sen University Cancer Center were evaluated using the prognostic factors affecting overall survival (OS). Treatment of metastases served as an independent prognosticator, where curative locoregional interventions (LRIs) were associated with superior clinical outcomes (adjusted hazard ratio (HR): 0.46, 95% confidence interval (CI): 0.22–0.98; p = 0.044). The LRIs group showed an improved median OS of 49.4 months versus 18.3 months for the palliative treatments (PTs) group. The median OS of patients with colorectal liver metastasis (CRLM) was significantly prolonged after undergoing LRIs (42.4 vs. 23.7 months; HR: 0.11, 95% CI: 0.01–1.22; p = 0.030), and patients in the LRIs plus liver-limited or lung-limited metastasis (LLM) group benefited more than those in the LRIs plus non-LLM group when compared to the PTs group (LLM from LRIs vs. PTs, HR: 0.16, 95% CI: 0.04–0.68; p = 0.006. Non-LLM from LRIs vs. PTs, HR: 0.47, 95% CI: 0.21–1.05; p = 0.074). In conclusion, we confirmed the positive prognostic value of LRIs in BRAF V600E mCRC, particularly in patients with CRLM or LLM. [ABSTRACT FROM AUTHOR]