The role of intestinal flora on tumor immunotherapy: recent progress and treatment implications.

Immunotherapy, specifically immune checkpoint inhibitors, has emerged as a promising approach for treating malignant tumors. The gut, housing approximately 70 % of the body's immune cells, is abundantly populated with gut bacteria that actively interact with the host's immune system. Different bacterial species within the intestinal flora are in a delicate equilibrium and mutually regulate each other. However, when this balance is disrupted, pathogenic microorganisms can dominate, adversely affecting the host's metabolism and immunity, ultimately promoting the development of disease. Emerging researches highlight the potential of interventions such as fecal microflora transplantation (FMT) to improve antitumor immune response and reduce the toxicity of immunotherapy. These remarkable findings suggest the major role of intestinal flora in the development of cancer immunotherapy and led us to the hypothesis that intestinal flora transplantation may be a new breakthrough in modifying immunotherapy side effects.

Sensitive and discriminative detection of cysteine by a Nile red-based NIR fluorescence probe.

Cysteine (Cys) is a significant biological mercaptan that achieves key roles in several important physiological processes, such as reversible redox homeostasis in living organisms. Abnormal levels of Cys in the human body are directly related to many diseases. In this work, we constructed a sensitive sensor (Cys-NR) by connecting a Cys recognition group to a Nile red derivative. Due to photo-induced electron transfer (PET), the Cys-NR probe showed little fluorescence at 650 nm. With the addition of Cys to the assay solution, the chlorine unit of the probe was substituted by the thiol group of Cys. Further, the amino and sulfhydryl groups in cysteine underwent an intramolecular rearrangement, which led to the Cys-NR probe water solution turning from colorless to pink with an enhancement in fluorescence. The red fluorescence at 650 nm increased about 20 times. Based on the turn-on signal, a selective Cys detection method is developed. The probe signal is not affected by various potential interferences or other competing biothiols and the limit of detection (LOD) is determined to be 0.44 µM. In addition, the probe is further employed for imaging of Cys in live cells, revealing good biological imaging ability that could provide a new way of intracellular Cys detection.

Application of a photoelectric magnifier to nasal injury in preterm infants receiving non-invasive ventilation: A prospective observational study.

PURPOSE: This study was to explore a photoelectric magnifier assessing nasal injury in preterm infants receiving non-invasive ventilation (NIV) treatment, and to describe the occurrence status and risk factors of nasal injuries. DESIGN: A prospective observational study. SUBJECTS: and setting: Preterm infants admitted from the Second Affiliated Hospital of Wenzhou Medical University between October 2018 and October 2019. METHODS: A continuously convenient sample of 127 preterm infants was included. When the infants were receiving NIV treatment, nursing staff checked nasal skin and mucosa daily with a photoelectric magnifier to confirm whether nasal injuries occurred. Meanwhile, a self-designed questionnaire was used to collect data. The Chi-square test, t-test, univariate and multiple logistic regression model were applied. RESULTS: A total of 127 preterm infants received 175 times NIV therapy, showing that the incidence of nasal injuries (infants number) was 27.6% (35/127) and the incidence of nasal injuries (ventilation cases) was 21.1% (37/175). Of the 37 cases of nasal injuries, 27 cases were I stage, while Ⅱ stage, Ⅲ stage and mucosa injuries appeared 2 cases, 2 cases and 6 cases. In the multivariate logistic analysis, nasal mask interface and NIV treatment more than seven days were independent risk factors affecting the occurrence of nasal injuries. CONCLUSIONS: The premature infants who received nasal NIV treatment were susceptible to nasal injuries, and clinical nurse capable of identifying risk factors and inintervening should be strengthened to prevent the occurrence and progression.

A Comparison of the Nutritional Risk Screening 2002 Tool With the Subjective Global Assessment Tool to Detect Nutritional Status in Chinese Patients Undergoing Surgery With Gastrointestinal Cancer.

The objectives of this study were to describe the nutritional status of Chinese patients with gastrointestinal cancer undergoing surgery and to compare the ease of use, diversity, and concordance of the Nutritional Risk Screening 2002 with the Subjective Global Assessment in the same patients. A total of 280 gastrointestinal cancer patients admitted for elective surgery were evaluated by the Nutritional Risk Screening 2002 (NRS 2002) and Subjective Global Assessment (SGA) tools within 48 hours of admission from April to October 2012. Related opinions about ease of using the tools were obtained from 10 nurses. The prevalence of patients at nutritional risk with the SGA and NRS 2002 was 33.9% and 53.2% on admission. In the total group, ≤70 age group, and >70 age group, respectively, consistency was observed in 214 (76.4%), 175 (91.1%), and 39 (44.3%); and kappa values were 0.54 (p < .001), 0.81 (p < .001), and 0.085 (p = .096). McNemar paired chi-square test showed a significant difference between the NRS 2002 and SGA in the total group and >70 age group (p < .001); however, no difference was observed in the ≤70 age group (p = .14). Nurses reported ease of use of the NRS 2002 as a "very easy" or "easy" to complete (3-5 minutes) and the SGA as an "easy" or "fair" tool to complete (5-10 minutes). The diversity and concordance between the SGA and NRS 2002 were varied in different age groups. The NRS 2002 is more suitable in nursing practice than the SGA to identify the nutritional status of patients with gastrointestinal cancer undergoing surgery, but it appeared to detect more patients at nutritional risk in the >70 age group.

A network-based, integrative approach to identify genes with aberrant co-methylation in colorectal cancer.

Epigenetic changes, including aberrations in DNA methylation, are a common hallmark of many cancers. The identification and interpretation of epigenetic changes associated with cancers may benefit from integration with protein interactomes. Based on the assumption that genes implicated in a specific tumor phenotype will show high aberrant co-methylation patterns with their interacting partners, we propose an integrated approach to uncover cancer-associated genes by integrating a DNA methylome with an interactome. Aberrant co-methylated interactions were first identified in the specific cancer, and genes were then prioritized based on their enrichment in aberrant co-methylation. By applying this to a large-scale colorectal cancer (CRC) dataset, the proposed method increases the power to capture known genes. More importantly, genes possessing high aberrant co-methylation patterns, located at the topological center of the original protein-protein interaction network (PPIN), affect several cancer-associated pathways and form hotspots that are frequently hijacked in cancer. Additionally, the top-ranked candidate genes may also be useful as an indicator of CRC diagnosis and prognosis. Five fold cross-validation of the top-ranked genes in diagnosis reveals that it can achieve an area under the receiver operating characteristic (ROC) curve ranging from 82.2% to 98.4% in three independent datasets. Five of these genes form a core repressive module. CCNA1 and ESR1 in particular are evidently silenced by promoter hypermethylation in CRC cell lines and tissues, whose re-expression markedly suppresses tumor cell survival and clonogenicity. These results show that the network-centric method could identify novel disease biomarkers and model how oncogenic lesions mediate epigenetic changes, providing important insights into tumorigenesis.

Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer.

Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target-dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.

Disease-driven detection of differential inherited SNP modules from SNP network.

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.

MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features.

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.