Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer

Background: Gastric cancer is a prevalent cause of tumor death. Tumor immunotherapy aims to reshape the specific immunity to tumors in order to kill the tumor. LncRNAs play a pivotal role in regulating the tumor immune microenvironment. Herein, immune-related lncRNAs were used to establish a prognosis risk-assessment model for gastric cancer and provide personalized predictions while providing insights and targets for gastric cancer treatment to enhance patient prognosis. Methods: Gastric adenocarcinoma transcriptome and clinical data were acquired from the The Cancer Genome Atlas (TCGA) database to screen the immune-related lncRNAs. Then, LASSO COX regression was utilized to construct the prognosis risk-assessment model. Afterward, the reliability of the model was evaluated the relationship between immune infiltration, clinical characteristics, and the model was analyzed. Results: We identified 13 lncRNAs and constructed the prognosis assessment model. According to the median risk score of the training set, the patients were assigned to different risk groups. Overall survival time was shorter in the high-risk group. In the high-risk group, higher infiltration of mono-macrophages, dendritic cells, CD4+ T cells, and CD8+ T cells was observed. Moreover, the model was positively related to tumor metastasis. Conclusion: The prognosis risk-assessment model developed in this research can effectively predict the prognosis of gastric cancer patients. This tool is expected to be further applied to clinics in the future, thus providing a novel target for immunotherapy in gastric cancer patients

The Clinicopathologic Importance of Serum Lactic Dehydrogenase in Patients with Gastric Cancer

Background. To explore possible correlation between serum lactate dehydrogenase (SLDH) levels and gastric cancer. Materials and Methods. We retrospectively reviewed 365 patients with gastric cancer. The correlation of SLDH levels with clinicopathologic features and survival rate was studied. Results. SLDH levels were closely associated with the pathological (p) T stage (P=0.011), metastasis (P=0.012), pTNM stage (P=0.001), and recurrence (P=0.012). Moreover, we found a significant SLDH level difference among Borrmann type (P=0.027), pT stage (P=0.004), lymph node metastasis (P=0.027), metastasis (P<0.001), pTNM stage (P=0.006), and recurrence (P=0.002). In addition, we detected a significant SLDH level difference between alive and dead subgroups (P=0.001). In addition, both univariate analysis and multivariate analysis showed that high SLDH levels were independent prognostic factor. For the subgroup with normal LDH (median point of 157.0 U/L), we detected that the subset with SLDH levels ≥157 U/L (158–245 U/L) showed poorer OS (P=0.005) and DFS (P=0.01) than that of ≤157 subgroup. Conclusions. Our results suggest that high SLDH level could be an independent poor prognostic biomarker. Gastric cancer patients with relative high SLDH level (158–245 U/L) were prone to develop a shorter OS and DFS

FMO family may serve as novel marker and potential therapeutic target for the peritoneal metastasis in gastric cancer

ObjectiveTo explore the relationship between flavin-containing monooxygenases (FMOs) and peritoneal metastasis (PM) in gastric cancer (GC).Materials and methodsTIMER 2.0 was used to perform pan-cancer analysis and assess the correlation between the expression of FMOs and cancers. A dataset from The Cancer Genome Atlas (TCGA) was used to analyze the correlation between FMOs and clinicopathological features of GC. PM is well established as the most common mode of metastasis in GC. To further analyze the correlation between FMOs and PM of GC, a dataset was obtained from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database. The results were validated by immunohistochemistry. The relationship between FMOs and PM of GC was explored, and a novel PM risk signature was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis. The regression model’s validity was tested by multisampling. A nomogram was established based on the model for predicting PM in GC patients. The mechanism of FMOs in GC patients presenting with PM was assessed by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in TCGA and GEO datasets. Finally, the potential relationship between FMOs and immunotherapy was analyzed.ResultsThe pan-cancer analysis in TCGA and GEO datasets showed that FMO1 was upregulated, while FMO2 and FMO4 were downregulated in GC. Moreover, FMO1 and FMO2 correlated positively with the T and N stage of GC in the TCGA dataset. FMO1 and FMO2 expression was a risk factor for GC (hazard ratio: 1.112 and 1.185). The overexpression of FMO1 was significantly correlated with worse disease-free-survival (DFS) and overall survival (OS). However, no relationship was found between FMO2 expression in GC and DFS and OS. PM was highly prevalent among GC patients and typically associated with a worse prognosis. FMO1 was highly expressed in GC with PM. FMO1 and FMO2 were positively correlated with PM in GC. We identified a 12-gene panel for predicting the PM risk signature by LASSO (Area Under Curve (AUC) = 0.948, 95%CI: 0.896–1.000). A 10-gene panel for PM prediction was identified (AUC = 0.932, 95%CI: 0.874–0.990), comprising FMO1 and FMO2. To establish a model for clinical application, a 7-gene panel was established (AUC = 0.927, 95% CI: 0.877–0.977) and successfully validated by multisampling. (AUC = 0.892, 95% CI: 0.878–0.906). GO and KEGG analyses suggest that FMO1 and FMO2 regulate the extracellular matrix and cell adhesion. FMO1 and FMO2 were positively correlated with the immune score of GC, and their expression was associated with the infiltration of immune cells.ConclusionPM in GC is strongly correlated with FMOs. Overall, FMO1 and FMO2 have huge prospects for application as novel diagnostic and therapeutic targets

A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks

The recently evolving remote healthcare technology could potentially aid the realization of cost-effective and lasting solutions to life-threatening diseases such as heart failure. Such a remote healthcare system should integrate an effectual heart failure risk monitoring and prediction platform. However, developing a heart failure risk (HFR) prediction method that objectively incorporate individual contributive characteristics of HFR risk factors, that are required for adequate prediction remains a challenge. Towards addressing this research gap, a new approach driven by hierarchical neighborhood component-based-learning (HNCL) and adaptive multi-layer networks (AMLN) is proposed. In the proposed method, the HNCL module firstly learns the interrelations among the HFR attributes/ risk factors to construct a set of informative features, regarded as the global weight vector that reflects individual contribution of each risk factor. Subsequently, the constructed global weight vector is applied in building an AMLN model for the prediction of HFR. Moreover, the proposed method's performances were extensively validated with a benchmark clinical database of potential heart failure patients and compared with previous studies using prediction accuracies, performance plots, receiving operating characteristic analysis, error-histogram analysis, specificity, and sensitivity metrics. From the experimental results, we found that the proposed method (AMLN–HNCL)​ achieved significantly higher and stable predictions with an improvement of approximately 11.10% over the commonly applied method. Additionally, the proposed method recorded 9.09% and 12.48% improvements for specificity and sensitivity, respectively compared to the commonly applied method. The superiority in performances achieved by the proposed method should be because the interrelations amongst the risk factors were adequately learnt and their individual contribution was objectively accounted for in the prediction task. Thus, we believe that the proposed method could potentially facilitate the practical implementation of accurately robust HFR prediction module in the context of the currently emerging remote healthcare system, especially in Internet of Medical Things (IoMT) systems. Also, the method may be applied in wearable mobile health-care gadgets capable of monitoring the heart failure status in individuals

Targeting castration-resistant prostate cancer with a novel RORγ antagonist elaiophylin

Prostate cancer (PCa) patients who progress to metastatic castration-resistant PCa (mCRPC) mostly have poor outcomes due to the lack of effective therapies. Our recent study established the orphan nuclear receptor RORγ as a novel therapeutic target for CRPC. Here, we reveal that elaiophylin (Elai), an antibiotic from Actinomycete streptomyces, is a novel RORγ antagonist and showed potent antitumor activity against CRPC in&nbsp;vitro and in&nbsp;vivo. We demonstrated that Elai selectively binded to RORγ protein and potently blocked RORγ transcriptional regulation activities. Structure-activity relationship studies showed that Elai occupied the binding pocket with several key interactions. Furthermore, Elai markedly reduced the recruitment of RORγ to its genomic DNA response element (RORE), suppressed the expression of RORγ target genes AR and AR variants, and significantly inhibited PCa cell growth. Importantly, Elai strongly suppressed tumor growth in both cell line based and patient-derived PCa xenograft models. Taken together, these results suggest that Elai is novel therapeutic RORγ inhibitor that can be used as a drug candidate for the treatment of human CRPC