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Abstract The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient’s prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Abstract The formation of gastric cancer (GC) is a complicated process involving multiple factors and multiple steps. The tumor–immune microenvironment is essential for the growth of GC and affects the prognosis of patients. We performed multiple machine learning algorithms to identify immunophenotypes and immunological characteristics in GC patients’ information from the TCGA database and extracted immune genes relevance of the GC immune microenvironment. C-X-C motif chemokine receptor 4 (CXCR4), belongs to the C-X-C chemokine receptor family, which can promote the invasion and migration of tumor cells. CXCR4 expression is significantly correlated to metastasis and the worse prognosis. In this work, we assessed the condition of immune cells and identified the connection between CXCR4 and GC immune microenvironment, as well as the signaling pathways that mediate the immune responses involved in CXCR4. The work showed the risk scores generated by CXCR4-related immunomodulators could distinguish risk groups consisting of differential expression genes and could use for the personalized prognosis prediction. The findings suggested that CXCR4 is involved in tumor immunity of GC, and CXCR4 is considered as a potential prognostic biomarker and immunotherapy target of GC. The prognostic immune markers from CXCR4-associated immunomodulators can independently predict the overall survival of GC.

We hypothesized that epigenetics is a link between smoking/allergen exposures and the development of Asthma and chronic obstructive pulmonary disease (ACO). A total of 75 of 228 COPD patients were identified as ACO, which was independently associated with increased exacerbations. Microarray analysis identified 404 differentially methylated loci (DML) in ACO patients, and 6575 DML in those with rapid lung function decline in a discovery cohort. In the validation cohort, ACO patients had hypermethylated PDE9A (+ 30,088)/ZNF323 (− 296), and hypomethylated SEPT8 (− 47) genes as compared with either pure COPD patients or healthy non-smokers. Hypermethylated TIGIT (− 173) gene and hypomethylated CYSLTR1 (+ 348)/CCDC88C (+ 125,722)/ADORA2B (+ 1339) were associated with severe airflow limitation, while hypomethylated IFRD1 (− 515) gene with frequent exacerbation in all the COPD patients. Hypermethylated ZNF323 (− 296) / MPV17L (+ 194) and hypomethylated PTPRN2 (+ 10,000) genes were associated with rapid lung function decline. In vitro cigarette smoke extract and ovalbumin concurrent exposure resulted in specific DNA methylation changes of the MPV17L / ZNF323 genes, while 5-aza-2′-deoxycytidine treatment reversed promoter hypermethylation-mediated MPV17L under-expression accompanied with reduced apoptosis and decreased generation of reactive oxygen species. Aberrant DNA methylations may constitute a determinant for ACO, and provide a biomarker of airflow limitation, exacerbation, and lung function decline. [ABSTRACT FROM AUTHOR]

Abstract Selecting suitable internal reference genes is an important prerequisite for the application of quantitative real-time PCR (qRT-PCR). However, no systematic studies have been conducted on reference genes in luffa. In this study, seven reference genes were selected, and their expression levels in luffa plants exposed to various simulated abiotic stresses [i.e., cold, drought, heat, salt, H2O2, and abscisic acid (ABA) treatments] were analyzed by qRT-PCR. The stability of the reference gene expression levels was validated using the geNorm, NormFinder, BestKeeper, and RefFinder algorithms. The results indicated that EF-1α was the most stably expressed and suitable reference gene overall and for the heat, cold, and ABA treatments. Additionally, UBQ expression was stable following the salt treatment, whereas TUB was identified as a suitable reference gene for H2O2 and drought treatments. The reliability of the selected reference genes was verified by analyzing the expression of copper/zinc superoxide dismutase (Cu/Zn-SOD) gene in luffa. When the most unstable reference genes were used for data normalizations, the resulting expression patterns had obvious biases when compared with the expression patterns for the most ideal reference genes used alone or combined. These results will be conducive to more accurate quantification of gene expression levels in luffa.

Abstract Sparganii rhizoma (SL) has potential therapeutic effects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF–MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identified compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identified from the aqueous extract of SL. Through network analysis, we identified seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, caffeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K-Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC.

Abstract Elevated ground-level ozone (O3), which is an important aspect of air quality related to public health, has been causing increasing concern. This study investigated the spatiotemporal distribution of ground-level O3 concentrations in China using a dataset from the Chinese national air quality monitoring network during 2013–2015. This research analyzed the diurnal, monthly and yearly variation of O3 concentrations in both sparsely and densely populated regions. In particular, 6 major Chinese cities were selected to allow a discussion of variations in O3 levels in detail, Beijing, Chengdu, Guangzhou, Lanzhou, Shanghai, and Urumchi, located on both sides of the Heihe-Tengchong line. Data showed that the nationwide 3-year MDA8 of ground-level O3 was 80.26 μg/m3. Ground-level O3 concentrations exhibited monthly variability peaking in summer and reaching the lowest levels in winter. The diurnal cycle reached a minimum in morning and peaked in the afternoon. Yearly average O3 MDA8 concentrations in Beijing, Chengdu, Lanzhou, and Shanghai in 2015 increased 12%, 25%, 34%, 22%, respectively, when compared with those in 2013. Compared with World Health Organization O3 guidelines, Beijing, Chengdu, Guangzhou, and Shanghai suffered O3 pollution in excess of the 8-hour O3 standard for more than 30% of the days in 2013 to 2015.