Disulfidptosis signature predicts immune microenvironment and prognosis of gastric cancer.

BACKGROUND: Disulfidptosis is a newly identified mechanism of cell death triggered by disulfide stress. Thus, gaining a comprehensive understanding of the disulfidptosis signature present in gastric cancer (GC) could greatly enhance the development of personalized treatment strategies for this disease. METHODS: We employed consensus clustering to identify various subtypes of disulfidptosis and examined the distinct tumor microenvironment (TME) associated with each subtype. The Disulfidptosis (Dis) score was used to quantify the subtype of disulfidptosis in each patient. Subsequently, we assessed the predictive value of Dis score in terms of GC prognosis and immune efficacy. Finally, we conducted in vitro experiments to explore the impact of Collagen X (COL10A1) on the progression of GC. RESULTS: Two disulfidptosis-associated molecular subtypes (Discluster A and B) were identified, each with distinct prognosis, tumor microenvironment (TME), immune cell infiltration, and biological pathways. Discluster A, characterized by high expression of disulfidptosis genes, exhibited a high immune score but poor prognosis. Furthermore, the Dis score proved useful in predicting the prognosis and immune response in GC patients. Those in the low Dis score group showed better prognosis and increased sensitivity to immunotherapy. Finally, our experimental findings validated that downregulation of COL10A1 expression attenuates the proliferation and migration capabilities of GC cells while promoting apoptosis. CONCLUSIONS: This study demonstrates that the disulfidptosis signature can assist in risk stratification and personalized treatment for patients with GC. The results offer valuable theoretical support for anti-tumor strategies.

Clinical Significance of Fibrinogen and Platelet to Pre-Albumin Ratio in Predicting the Prognosis of Advanced Gastric Cancer.

Objective: The aim of this study was to investigate the clinical significance of Fibrinogen and Platelet to Pre-albumin Ratio(FPAR) in predicting the prognosis of patients with advanced gastric cancer(AGC) and to construct a predictive model. Methods: We collected clinical data from 489 postoperative patients with AGC. FPAR was divided into high and low groups according to the receiver operating characteristic (ROC) curve. The value of FPAR in predicting the prognosis of progressive gastric cancer was analysed using univariate and multivariable Cox regression analysis and its relationship with clinicopathological features. Finally, the Overall Survival(OS) and recurrence-free survival(RFS) prediction models were constructed and validated using FPAR. Results: Univariate and multifactorial cox regression analysis showed that grade (P<0.001), TNM-stage (P<0.001), chemotherapy (P<0.001), and FPAR (OR=3.054,95% CI:2.088-4.467, P<0.001) were independent risk factors for OS; grade (P=0.021), N-stage (P=0.024), TNM-stage (P=0.033), and FPAR (OR=2.215,95% CI:1.634-3.003, P<0.001) were independent risk factors for RFS. Subgroup analysis showed that the FPAR-low group had higher OS and RFS than the FPAR-high group, regardless of the patient's TNM stage (p<0.05). However, OS was instead higher in the the stage III-FPAR-low group than in the the stage II-FPAR-high group (p<0.05), while RFS was not significantly different. Predictive models incorporating FPAR had better predictive performance than those without FPAR, showing wide range of net benefit and AUC. After correction, the 2-year AUC, 3-year AUC and C-index of the OS model were 0.737, 0.756, and 0.746; the 2-year AUC, 3-year AUC, and C-index of the RFS model were 0.738, 0.758, and 0.711. Conclusion: FPAR levels were associated with prognosis in patients with AGC and could independently predict RFS and OS.

Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer.

Background: RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients. Methods: We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair. Results: We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways. Conclusion: Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.

Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer.

Background: The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC). Methods: The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability. Results: The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility. Conclusion: CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.

Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer.

Distant metastasis (DM) is relatively uncommon in T1 stage gastric cancer (GC). The aim of this study was to develop and validate a predictive model for DM in stage T1 GC using machine learning (ML) algorithms. Patients with stage T1 GC from 2010 to 2017 were screened from the public Surveillance, Epidemiology and End Results (SEER) database. Meanwhile, we collected patients with stage T1 GC admitted to the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Nanchang University from 2015 to 2017. We applied seven ML algorithms: logistic regression, random forest (RF), LASSO, support vector machine, k-Nearest Neighbor, Naive Bayesian Model, Artificial Neural Network. Finally, a RF model for DM of T1 GC was developed. The AUC, sensitivity, specificity, F1-score and accuracy were used to evaluate and compare the predictive performance of the RF model with other models. Finally, we performed a prognostic analysis of patients who developed distant metastases. Independent risk factors for prognosis were analysed by univariate and multifactorial regression. K-M curves were used to express differences in survival prognosis for each variable and subvariable. A total of 2698 cases were included in the SEER dataset, 314 with DM, and 107 hospital patients were included, 14 with DM. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. A combined analysis of seven ML algorithms in the training and test sets found that the RF prediction model had the best prediction performance (AUC: 0.941, Accuracy: 0.917, Recall: 0.841, Specificity: 0.927, F1-score: 0.877). The external validation set ROCAUC was 0.750. Meanwhile, survival prognostic analysis showed that surgery (HR = 3.620, 95% CI 2.164-6.065) and adjuvant chemotherapy (HR = 2.637, 95% CI 2.067-3.365) were independent risk factors for survival prognosis in patients with DM from stage T1 GC. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. ML algorithms had shown that RF prediction models had the best predictive efficacy to accurately screen at-risk populations for further clinical screening for metastases. At the same time, aggressive surgery and adjuvant chemotherapy can improve the survival rate of patients with DM.

Clinicopathological characteristics and prognosis of gastric signet ring cell carcinoma.

BACKGROUND: The clinicopathological features and prognosis of gastric signet ring cell carcinoma (GSRC) remain controversial, particularly with regard to sensitivity to postoperative adjuvant therapy. AIM: To compare the pathological features of GSRC with those of gastric adenocarcinoma of different degrees of differentiation and the differences in survival prognosis between the different disease processes. METHODS: By screening gastric cancer patients from 2010 to 2015 in the database of Surveillance, Epidemiology and End Results, and collecting the clinicopathological and prognostic data of gastric cancer patients who underwent surgery from January 2014 to December 2016 in the Second Affiliated Hospital of Nanchang University, we analyzed the general pathological characteristics of GSRC by the chi-square test. Univariate and multivariate analyses were conducted to compare the factors affecting the survival and prognosis of early and advanced gastric adenocarcinoma. The Kaplan-Meier curves were plotted to reveal the survival difference between early and advanced GSRC and different differentiated types of gastric adenocarcinoma. The prognosis model of advanced GSRC was established with R software, and the area under curve (AUC) and C-index were used to assess the accuracy of the model. RESULTS: Analysis of pathological features revealed that signet ring-cell carcinoma (SRC) was more frequently seen in younger (< 60 years), female, and White patients compared to non-SRC patients. SRC was less commonly associated with early gastric cancer (EGC) (23.60% vs 39.10%), lower N0 (38.61% vs 61.03%), and larger tumour sizes > 5 cm (31.15% vs 27.10%) compared to the differentiated type, while the opposite was true compared to the undifferentiated type. Survival prognostic analysis found no significant difference in the prognosis of SRC patients among EGC patients. In contrast, among advanced gastric cancer (AGC) patients, the prognosis of SRC patients was correlated with age, race, tumour size, AJCC stage, T-stage, and postoperative adjuvant therapy. The predictive model showed that the 3-year AUC was 0.787, 5-year AUC was 0.806, and C-index was 0.766. Compared to non-SRC patients, patients with SRC had a better prognosis in EGC [hazard ratio (HR): 0.626, 95% confidence interval (CI): 0.427-0.919, P < 0.05] and a worse prognosis in AGC (HR: 1.139, 95%CI: 1.030-1.258, P < 0.05). When non-SRC was divided into differentiated and undifferentiated types for comparison, it was found that in EGC, SRC had a better prognosis than differentiated and undifferentiated types, while there was no significant difference between differentiated and undifferentiated types. In AGC, there was no significant difference in prognosis between SRC and undifferentiated types, both of which were worse than differentiated types. A prognostic analysis of postoperative adjuvant therapy for SRC in patients with AGC revealed that adjuvant postoperative radiotherapy or chemotherapy significantly improved patient survival (34.6% and 36.2% vs 18.6%, P < 0.05). CONCLUSION: The prognosis of SRC is better than that of undifferentiated type, especially in EGC, and its prognosis is even better than that of differentiated type. SRC patients can benefit from early detection, surgical resection, and aggressive adjuvant therapy.

The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With Distinct M6a Methylation and Tumor Microenvironment Infiltration Characteristics.

The interaction between hypoxia and RNA N6-methyladenosine (m6A) is an emerging focus of investigation. However, alterations in m6A modifications at distinct hypoxia levels remain uncharacterized in gastric cancer (GC). Unsupervised hierarchical clustering was performed to stratify samples into different clusters. Differentially expressed gene analysis, univariate Cox proportional hazards regression analysis, and hazard ratio calculations were used to establish an m6A score to quantify m6A regulator modification patterns. After using an algorithm integrating Least absolute shrinkage and selection operator (LASSO) and bootstrapping, we identified the best candidate predictive genes. Thence, we established an m6A-related hypoxia pathway gene prognostic signature and built a nomogram to evaluate its predictive ability. The area under the curve (AUC) value of the nomogram was 0.811, which was higher than that of the risk score (AUC=0.695) and stage (AUC=0.779), suggesting a high credibility of the nomogram. Furthermore, the clinical response of anti-PD-1/CTLA-4 immunotherapy between high- and low-risk patients showed a significant difference. Our study successfully explored a brand-new GC pathological classification based on hypoxia pathway genes and the quantification of m6A modification patterns. Comprehensive immune analysis and validation demonstrated that hypoxia clusters were reliable, and our signature could provide a new approach for clinical decision-making and immunotherapeutic strategies for GC patients.

Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer.

Background: Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer. Methods: The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model. Results: The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard. Conclusion: This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.

Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer.

Background: The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation. Methods: Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation. Results: 33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility. Conclusions: We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.

Adjuvant treatment and molecular mechanism of probiotic compounds in patients with gastric cancer after gastrectomy.

Gastrectomy is the main treatment for gastric cancer (GC) at present. Surgery improves the survival rate of patients, but the complications seriously affect the recovery and lack effective treatment measures. In the present study, probiotic compounds (4 strains; Lactobacillus plantarum MH-301 (CGMCC NO. 18618), L. rhamnosus LGG-18 (CGMCC NO. 14007), L. acidophilus and Bifidobacterium animalis subsp.lactis LPL-RH (CGMCC NO. 4599)), through clinical and animal model verification, were studied to try to find the auxiliary treatment measures after gastrectomy, and explore its potential mechanism. Clinical research results showed that probiotic compounds treatment could significantly lower postoperative inflammation, enhance immunity, resume gut microbiota composition and promote postoperative recovery. The results in rat models indicated that gastrostomy led to the aggravation of inflammation, the impairment of immunity and intestinal barrier, and the disorder of gut microbiota in vivo. Furthermore, probiotic compounds' administration could downregulate the inflammatory and permeability signaling pathways in the intestinal tissue, reduce the levels of proinflammatory factors, maintain the intestinal mucosal barrier and immune function, and recover the disorder of gut microbiota after gastrectomy in rats. Therefore, we conclude that probiotic compounds can restore gut microbiota homeostasis, reduce inflammation, maintain intestinal mucosal barrier and immunity, finally promote recovery after gastrectomy, and is expected to improve the prognosis of patients.

Nine-long non-coding ribonucleic acid signature can improve the survival prediction of colorectal cancer.

BACKGROUND: Investigating molecular biomarkers that accurately predict prognosis is of considerable clinical significance. Accumulating evidence suggests that long non-coding ribonucleic acids (lncRNAs) are frequently aberrantly expressed in colorectal cancer (CRC). AIM: To elucidate the prognostic function of multiple lncRNAs serving as biomarkers in CRC. METHODS: We performed lncRNA expression profiling using the lncRNA mining approach in large CRC cohorts from The Cancer Genome Atlas (TCGA) database. Receiver operating characteristic analysis was performed to identify the optimal cutoff point at which patients could be classified into the high-risk or low-risk groups. Based on the Cox coefficient of the individual lncRNAs, we identified a nine-lncRNA signature that was associated with the survival of CRC patients in the training set (n = 175). The prognostic value of this nine-lncRNA signature was validated in the testing set (n = 174) and TCGA set (n = 349). The prognostic models, consisting of these nine CRC-specific lncRNAs, performed well for risk stratification in the testing set and TCGA set. Time-dependent receiver operating characteristic analysis indicated that this predictive model had good performance. RESULTS: Multivariate Cox regression and stratification analysis demonstrated that this nine-lncRNA signature was independent of other clinical features in predicting overall survival. Functional enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms further indicated that these nine prognostic lncRNAs were closely associated with carcinogenesis-associated pathways and biological functions in CRC. CONCLUSION: A nine-lncRNA expression signature was identified and validated that could improve the prognosis prediction of CRC, thereby providing potential prognostic biomarkers and efficient therapeutic targets for patients with CRC.

Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer.

OBJECTIVE: This study aimed to establish the best early gastric cancer lymph node metastasis (LNM) prediction model through machine learning (ML) to better guide clinical diagnosis and treatment decisions. METHODS: We screened gastric cancer patients with T1a and T1b stages from 2010 to 2015 in the Surveillance, Epidemiology and End Results (SEER) database and collected the clinicopathological data of patients with early gastric cancer who were treated with surgery at the Second Affiliated Hospital of Nanchang University from January 2014 to December 2016. At the same time, we applied 7 ML algorithms-the generalized linear model (GLM), RPART, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), regularized dual averaging (RDA), and the neural network (NNET)-and combined them with patient pathological information to develop the best prediction model for early gastric cancer lymph node metastasis. Among the SEER set, 80% were randomly selected to train the models, while the remaining 20% were used for testing. The data from the Second Affiliated Hospital were considered as the external verification set. Finally, we used the AUROC, F1-score value, sensitivity, and specificity to evaluate the performance of the model. RESULTS: The tumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. Comprehensive comparison of the prediction model performance of the training set and test set showed that the RDA model had the best prediction performance (F1-score = 0.773; AUROC = 0.742). The AUROC of the external validation set was 0.73. CONCLUSIONS: Tumour size, tumour grade, and depth of tumour invasion were independent risk factors for early gastric cancer LNM. ML predicted LNM risk more accurately, and the RDA model had the best predictive performance and could better guide clinical diagnosis and treatment decisions.

Prognostic thirteen-long non-coding RNAs (IncRNAs) could improve the survival prediction of gastric cancer / El pronóstico de 13 largos RNA no codificantes (IncRNA) podría mejorar la predicción de supervivencia del cáncer gástrico

OBJECTIVE: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play important regulatory roles in the tumorigenesis and progression of gastric cancer (GC). The aim of this study was to construct the prognostic predictive model of lncRNAs signature and improve the survival prediction of GC. PATIENTS AND METHODS: The expression profiling of lncRNAs in large GC cohorts was performed from The Cancer Genome Atlas (TCGA) databases using the lncRNAs-mining approach, including training data set (N=160) and testing data set (N=159). A 13-lncRNAs signature significantly associated with overall survival (OS) in the training data set was selected. The prognostic value of this 13-lncRNAs signature was then confirmed in the test validation set and the entire validation set, respectively. RESULTS: Based on lncRNA expression profiling of 319 patients with stomach adenocarcinoma (STAD), prognostic 13-lncRNAs signature was found to be significantly associated with the prognosis of GC. Compared to patients with low-risk scores, patients with high-risk scores had a significantly shorter survival time. Moreover, functional enrichment analysis indicated that this 13-lncRNAs signature was potentially involved in multiple biological processes, such as DNA replication and cell cycle signaling pathway. CONCLUSIONS: The prognostic model of the 13-lncRNAs signature established by our study could improve the survival prediction of GC to a greater extent

OBJETIVO: Las pruebas acumuladas demostraron que los ARN no codificantes de larga duración (ARNlC) desempeñaban los importantes papeles reguladores en la tumorigénesis y la progresión del cáncer gástrico (CG). El objetivo de este estudio fue construir el modelo predictivo de pronóstico de la firma de los lncRNA y mejorar la predicción de supervivencia del GC. PACIENTES Y MÉTODOS: El perfil de expresión de los lncARN en grandes cohortes de GC se realizó a partir de las bases de datos del Atlas del Genoma del Cáncer (TCGA) utilizando el enfoque de minería de lncARN, incluyendo el conjunto de datos de entrenamiento (N=160) y el conjunto de datos de pruebas (N=159). Se eligió la firma de 13 lncARN significativamente asociada con la supervivencia general (OS) en la serie de capacitación. El valor pronóstico de esta firma de 13-lncARN se confirmó luego en la serie de validación de pruebas y en toda la serie de validación, respectivamente. RESULTADOS: Basado en el perfil de expresión de lncRNA de 319 pacientes con adenocarcinoma de estómago (STAD), se encontró que la firma de 13-lncRNA de pronóstico estaba significativamente asociada con el pronóstico de GC. En comparación con los pacientes con puntuaciones de bajo riesgo, los pacientes con puntuaciones de alto riesgo tuvieron un tiempo de supervivencia significativamente más corto. Además, el análisis de enriquecimiento funcional indicó que esta firma de 13-lncARN estaba potencialmente involucrada en múltiples procesos biológicos, como la replicación del ADN y la vía de señalización del ciclo celular. CONCLUSIONES: El modelo de pronóstico de la firma de 13-lncARN establecido por nuestro estudio podría mejorar mejor la predicción de supervivencia del GC

Integrative bioinformatics analysis of prognostic alternative splicing signatures in gastric cancer.

BACKGROUND: The potential prognostic value of alternative splicing (AS) variants and regulatory splicing factors in gastric carcinogenesis is unclear. We aimed to exploit the aberrant AS signatures and splicing factors involved in gastric cancer (GC) and to determine their prognostic predictive values. METHODS: We performed detailed data acquisition using the Cancer Genome Atlas project and profiled genome-wide AS signatures in a cohort of 190 patients with stomach adenocarcinoma (STAD). Prognostic prediction models and splicing correlation networks were assessed using an integrative bioinformatics analysis approach. RESULTS: We detected 1,308 overall survival (OS)-related AS signatures in 993 genes, most of which were favorable prognostic factors. Six splicing factors have been suggested to be dysregulated in GC, i.e., DHX15, PPP4R2, PRPF38B, RBM9, RBM15, and ILF3. Another notable finding was that most favorable prognosis AS events were positively correlated with expression of splicing factors, while a majority of poor survival prognostic AS genes were negatively associated with the expression of splicing factors. CONCLUSIONS: To our knowledge, the current study provided the first comprehensive profiling of global modifications in the RNA splicing to identify survival associated AS signatures of GC specific genes. Our findings contribute to a better understanding of aberrant AS signatures and splicing factors in STAD, which can potentially be used as prognostic biomarkers and therapeutic targets for GC.

Prognostic thirteen-long non-coding RNAs (IncRNAs) could improve the survival prediction of gastric cancer.

OBJECTIVE: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play important regulatory roles in the tumorigenesis and progression of gastric cancer (GC). The aim of this study was to construct the prognostic predictive model of lncRNAs signature and improve the survival prediction of GC. PATIENTS AND METHODS: The expression profiling of lncRNAs in large GC cohorts was performed from The Cancer Genome Atlas (TCGA) databases using the lncRNAs-mining approach, including training data set (N=160) and testing data set (N=159). A 13-lncRNAs signature significantly associated with overall survival (OS) in the training data set was selected. The prognostic value of this 13-lncRNAs signature was then confirmed in the test validation set and the entire validation set, respectively. RESULTS: Based on lncRNA expression profiling of 319 patients with stomach adenocarcinoma (STAD), prognostic 13-lncRNAs signature was found to be significantly associated with the prognosis of GC. Compared to patients with low-risk scores, patients with high-risk scores had a significantly shorter survival time. Moreover, functional enrichment analysis indicated that this 13-lncRNAs signature was potentially involved in multiple biological processes, such as DNA replication and cell cycle signaling pathway. CONCLUSIONS: The prognostic model of the 13-lncRNAs signature established by our study could improve the survival prediction of GC to a greater extent.

Is Primary Tumor Excision and Specific Metastases Sites Resection Associated With Improved Survival in Stage â £ Colorectal Cancer? Results From SEER Database Analysis.

OBJECTIVE: We aimed to explore the prognostic value of primary tumor and specific metastases excision on survival among patients with stage IV colorectal cancer (CRC) in the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: Patients with stage IV CRC were selected using SEER database between 2010 and 2013. Survival rate was calculated according to the Kaplan-Meier method, and differences between curves were tested by the log-rank test. Cox proportional hazards model was used in the multivariable analysis. RESULTS: Included in this study were 27 878 patients with distant metastatic CRC. Among the single organ site of metastatic CRC, patients with solitary metastasis of lung showed the highest median overall survival (OS). Both primary and metastatic sites surgical resection for patients with liver, lung, and simultaneous liver and lung metastases had better median OS. Age younger than 65 years, Asian and Pacific Islander, distal colon and rectum, and palliative primary tumor and metastatic lesions resection were associated with better OS after multivariate analysis. Palliative primary tumor and metastatic lesions resection had a significant survival benefit compared with nonsurgical group in selected patients. CONCLUSION: These findings support the use of preemptive surgery in the management of highly selected metastatic CRC patients.

Impact of Site-Specific Metastases on Surgical Value and Survival among Metastatic Colorectal Cancer Patients.

We aimed to explore the potential prognostic impact of the metastatic site on the management approach and prognosis of stage IV colorectal cancer patients with synchronous metastases. Synchronous metastatic colorectal cancer patients reported to the Surveillance, Epidemiology, and End Results Program database between 2010 and 2013 were included in this study. Overall survival (OS) was compared between patients with different treatment options using risk-adjusted Cox proportional hazard regression models. Overall, 17,776 patients with stage IV colorectal cancer were identified. Of these patients, 2,052 (11.5%) underwent surgical resection for tumors at both the primary and metastatic sites. Patients who underwent surgical resection of both primary and metastatic sites with liver, lung, and simultaneous liver and lung metastases had a longer median OS (P < 0.001) than patients who underwent nonsurgical treatments. Cox regression analysis revealed that surgical resection of both primary and metastatic sites was associated with a significantly enhanced OS (P < 0.001). Colorectal cancer patients with hepatic or pulmonary metastases, who underwent metastasectomy, even in selected patients with both hepatic and pulmonary metastases after multidisciplinary evaluation, could have a better survival benefit than patients who underwent nonsurgical treatments.

Oral Probiotics Alleviate Intestinal Dysbacteriosis for People Receiving Bowel Preparation.

Background: Bowel preparation is necessary for successful colonoscopy, while it can seriously affect intestinal microbial composition and damage the intestinal mucosal barriers in humans. Methods: To figure out whether probiotics can sustain intestinal homeostasis and guard people's health, the probiotic drug of Bifidobacterium Tetragenous viable Bacteria Tablets (P group, n = 16) or placebo (C group, n = 16) was used for volunteers receiving bowel preparation, and high-throughput sequencing method was applied to monitor their intestinal microbial changes. Results: The present results suggested that bowel preparation obviously reduced the intestinal microbial diversity, while taking probiotics significantly restored it to normal level. In addition, probiotics sharply reduced the abundance of pathogenic Proteobacteria, and obviously lowered the ratio of Firmicutes/Bacteroidetes compared with control group at phylum level (P < 0.05). And probiotics markedly decreased the abundance of pathogenic Acinetobacter and Streptococcus, while greatly enriched the relative abundance of beneficial bacteria Bacteroides, Roseburia, Faecalibacterium, and Parabacteroides at genus level (P < 0.05). Conclusion: Probiotic drugs, e.g., Bifidobacterium Tetragenous viable Bacteria Tablets, can be used to restore intestinal dysbacteriosis caused by bowel preparation, and reduce side effects during colonoscopy.