Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction.

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera.

An increasing amount of evidence has proven that serum metabolites can instantly reflect disease states. Therefore, sensitive and reproducible detection of serum metabolites in a high-throughput manner is urgently needed for clinical diagnosis. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a high-throughput platform for metabolite detection, but it is hindered by significant signal fluctuations because of the "sweet spot" effect of organic matrices. Here, by screening two transformation methods and four normalization techniques to reduce the significant signal fluctuations of the DHB matrix, an integrated MALDI-MS data processing approach combined with machine learning methods was established to reveal metabolic biomarkers of lung cancer. In our study, 13 distinctive features with statistically significant differences (p < 0.001) between 34 lung cancer patients and 26 healthy controls were selected as significant potential biomarkers of lung cancer. 6 out of the 13 distinctive features were identified as intact metabolites. Our results demonstrate the potential for clinical application of MALDI-MS in serum metabolomics for biomarker screening in lung cancer.

Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data.

Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC).

Effect of selective lymph node dissection on immune function in patients with T1 stage non-small cell lung cancer: a randomized controlled trial.

BACKGROUND: Many lymph nodes resected from early-stage non-small cell lung cancer (NSCLC) patients haven't metastasis. Selective lymph node dissection (SLND) can reduce surgical trauma by retaining non-metastatic lymph nodes, we aimed to investigate whether SLND could reduce immune impairment compared with systematic lymph node dissection. METHODS: According to the selection criteria, patients with no metastasis in hilar and regional lymph nodes were selected as the research subjects. The patients were randomly divided into 2 groups: the SLND group (Group SD) and the systematic lymph node dissection group (Group CD). At 24 hours before surgery and on the 1st and 3rd postoperative days (POD), fasting venous blood was sampled to detect cytokine indicators [interleukin-6 (IL-6), C-reactive protein (CRP)], cellular immune indicators [lymphocytes, natural killer cells (NK cells), CD4+, CD8+, CD4+/CD8+], and humoral immune indicators (IgG, IgA, IgM). At the same time, clinically indicators of the patients were recorded. All indicators between the 2 groups were compared. RESULTS: The comparison of clinical indicators between the two groups showed that SLND is more conducive to patients' rapid recovery after surgery. CRP and IL-6 levels in Group CD were significantly higher than those in Group SD after surgery (P<0.05). There were no statistical differences between the 2 groups in the proportions of lymphocytes and NK cells after surgery (P>0.05). The proportions of CD4+ cells and CD4+/CD8+ in Group CD were significantly lower than those in Group SD at POD1 (P<0.05). The proportion of CD8+ cells was significantly higher in Group CD than in Group SD at POD3 (P<0.05). There were no significant differences in IgG, IgA, and IgM levels between the 2 groups at the same point in time (P>0.05). CONCLUSIONS: Compared with systematic lymph node dissection, SLND has the following advantages: (I) it is more conducive to patients' rapid recovery after surgery; (II) it can reduce the body's acute inflammatory response and non-specific immune damage; (III) it can reduce the damage to cellular immune function caused by surgery. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100045893.

Insight into an Oxidative DNA-Cleaving DNAzyme: Multiple Cofactors, the Catalytic Core Map and a Highly Efficient Variant.

An oxidative DNA-cleaving DNAzyme (PL) employs a double-cofactor model "X/Cu2+" for catalysis. Herein, we verified that reduced nicotinamide adenine dinucleotide (NADH), flavin mononucleotide, cysteine, dithiothreitol, catechol, resorcinol, hydroquinone, phloroglucinol, o-phenylenediamine, 3,3',5,5'-tetramethylbenzidine, and hydroxylamine acted as cofactor X. According to their structural similarities or fluorescence property, we further confirmed that reduced nicotinamide adenine dinucleotide phosphate (NADPH), 2-mercaptoethanol, dopamine, chlorogenic acid, resveratrol, and 5-carboxyfluorescein also functioned as cofactor X. Superoxide anions might be the commonality behind these cofactors. We subsequently determined the conservative change of individual nucleotides in the catalytic core under four different cofactor X. The nucleotides A4 and C5 are highly conserved, whereas the conservative levels of other nucleotides are dependent on the types of cofactor X. Moreover, we observed that the minor change in the PL's secondary structure affects electrophoretic mobility. Finally, we characterized a highly efficient variant T3G and converted its double-cofactor NADH/Cu2+ to sole-cofactor NADH.

A Cyclin D1-Specific Single-Chain Variable Fragment Antibody that Inhibits HepG2 Cell Growth and Proliferation.

Cyclin D1 is a key regulatory factor of the G1 to S transition during cell cycle progression. Aberrant cyclin D gene amplification and abnormal protein expression have been linked to hepatocellular carcinoma (HCC) tumorigenesis. Intrabodies, effective anticancer therapies that specifically inhibit target protein function within all intracellular compartments, may block cyclin D1 function. Here, a single-chain variable fragment (scFv) antibody against cyclin D1 (ADκ) selected from a human semi-synthetic phage display scFv library is expressed in Escherichia coli as soluble ADκ. Purified ADκ specifically binds to recombinant and endogenous cyclin D1 with high affinity. To enable blocking of intracellular cyclin D1 activity, an endoplasmic reticulum (ER) retention signal sequence is added to the ADκ sequence to encode anti-cyclin D1 intrabody ER-ADκ. Transfection of HepG2 cells with expression vector encoding ER-ADκ elicited intracellular ER-ADκ expression leading to cyclin D1 binding, significant G1 phase arrest, and apoptosis that are mechanistically tied to decreased intracellular phosphorylated retinoblastoma protein (Rb) levels. Meanwhile, ER-ADκ dramatically inhibited subcutaneous human HCC xenografts growth in nude mice in vivo after injection of tumors with expression vector encoding ER-ADκ. These results demonstrate the potential of intrabody-based cyclin D1 targeting therapy as a promising treatment for HCC.

Selective lymph node dissection for clinical T1 stage non-small cell lung cancer.

BACKGROUND: More and more pulmonary nodules are detected by CT scan, and postoperative pathology reveals many lymph nodes without metastasis. The purpose of this study was to investigate the characteristics of T1 stage lymph node metastasis in non-small cell lung cancer (NSCLC) and to explore the indications for selective lymph node dissection (SLND). METHODS: A total of 841 patients with stage T1 of NSCLC were performed lobectomy and systemic lymphadenectomy. We analyzed the types of lymph node metastases and the relationship between lymph node metastasis and pulmonary pleural invasion, thrombosis of vascular carcinoma and tumor size in all patients. RESULTS: Among them, 257 cases of tumor in the right upper lobe (RUL) and 186 cases in the left upper lobe (LUL), and no metastasis was found in the inferior mediastinal lymph nodes. Tumor metastases occurred in subcarinal lymph nodes, with hilar and/or mediastinal lymph node metastasis. Among the 171 cases with right lower lobe (RLL) tumors and the 151 cases with left lower lobe (LLL) tumors, patients with superior lymph node metastasis were all associated with hilar and/or subcarinal lymph node metastasis. Among the 76 cases with right middle lobe (RML) tumors, no metastasis with inferior mediastinal lymph node was observed. Lymph node metastasis is much easier in patients with pulmonary pleural invasion or thrombosis of vascular cancer. The larger the tumor diameter, the greater the possibility of lymph node metastasis. CONCLUSIONS: SLND is a feasible treatment for clinical T1 stage NSCLC under the guidance of intraoperative frozen results of lobe-specific lymph nodes.

Jumonji domain containing 2C promotes cell migration and invasion through modulating CUL4A expression in lung cancer.

Jumonji domain containing 2C (JMJD2C), also named as KDM4C, was found to a transcriptional cofactor and enzyme that catalyzes demethylation of histone H3 lysine 9 and 36. Here in this study, we found that the expression of JMJD2C increased in a majority of the human lung cancer tissues examined compared with adjacent tissues. Furthermore, the expression of JMJD2C was found to be higher in metastatic lung cancer tissues than which in non-metastatic lung cancer tissues. Knockdown of JMJD2C inhibited the ability of migration and invasion of lung cancer cells. Moreover, JMJD2C knockdown was proven to inhibit the tumor hepatic metastasis of lung cancer cells in vivo and epithelial-mesenchymal transition (EMT) in vitro. On the contrary, over-expression of JMJD2C was found to promote the ability of migration, invasion and EMT. As to mechanism, knockdown of JMJD2C was found to inhibit the expression of CUL4A while to promote the expression of p53 and p27. Furthermore, we found that JMJD2C regulated the activities of lung cancer cells by directly controlling the expression of CUL4A in JMJD2C over-expression cell line, and interference of CUL4A was found to reverse the ability of migration, invasion and EMT which JMJD2C over-expression bought to. Together, these results of this study not only enriched the JMJD2C biological function of lung cancer, but also illuminated exploring the prevention and treatment of the invasion and metastasis of lung cancer.

Cumulative review and meta-analyses on the association between MTHFR rs1801133 polymorphism and breast cancer risk: a pooled analysis of 83 studies with 74,019 participants.

BACKGROUND: The association between methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and breast cancer risk has been extensively explored, but their results are conflicting rather than conclusive. To clarify the precise effects of MTHFR polymorphisms on the risk of breast cancer, a systemic review and most comprehensive meta-analysis of all available studies relating MTHFR rs1801133 gene polymorphism to the risk of breast cancer was conducted. METHODS: Eligible articles were identified by search of databases including Medline (Mainly PubMed), Embase, Web of Science, Chinese Biomedical Literature database (CBM), CNKI and Wanfang Medical databases. Crude ORs with 95% CIs were used to assess the strength of association. RESULTS: Finally, a total of 83 studies with 35,029 cases and 38,990 controls were included. Overall, MTHFR rs1801133 gene polymorphism was proved to contribute to the risk of breast cancer under all genetic models (TT vs. CC: Pheterogeneity <0.001, OR=1.141, 95%CI=1.065-1.222, P <0.001; TT vs. CT: Pheterogeneity <0.001, OR=1.085, 95%CI=1.021-1.154, P=0.009; TT + CT vs. CC: Pheterogeneity <0.001, OR=1.040, 95%CI=1.020-1.061, P <0.001; TT vs. CC + CT: Pheterogeneity <0.001, OR=1.131, 95%CI=1.052-1.215, P=0.0478; T allele vs. C allele: Pheterogeneity <0.001, OR=1.040, 95%CI=1.009-1.071, P=0.010). CONCLUSIONS: The results of this meta-analysis suggest that MTHFR rs1801133 gene polymorphism may the therapeutic target for breast cancer.

MicroRNA-497 suppresses angiogenesis by targeting vascular endothelial growth factor A through the PI3K/AKT and MAPK/ERK pathways in ovarian cancer.

MicroRNAs (miRNAs) have been shown to play an important role in diverse biological processes and cancer progression. The objective of the present study was to investigate the role of miR-497 in ovarian cancer angiogenesis. We found that miR-497 expression was downregulated in human ovarian cancer tissues, and the low miR-497 expression was significantly associated with increased angiogenesis. Functionally, exogenous expression of miR-497 suppressed the ability of ovarian cancer cells to promote capillary tube formation of endothelial cells. We further disclosed that miR-497 exerted its function of anti-angiogenesis by suppressing VEGFA expression in ovarian cancer cells and, in turn, impairing the VEGFR2-mediated PI3K/AKT and MAPK/ERK pathways. Our findings suggest that downregulation of miR-497 may contribute to angiogenesis in ovarian cancer. miR-497 may be a promising candidate target for prevention and treatment of ovarian cancer.

MicroRNA-497 inhibition of ovarian cancer cell migration and invasion through targeting of SMAD specific E3 ubiquitin protein ligase 1.

Ovarian cancer is the leading cause of death from gynecological malignancies worldwide. Understanding the molecular mechanism underlying ovarian cancer progression facilitates the development of promising strategy for ovarian cancer therapy. Previously, we observed frequent down-regulation of miR-497 expression in ovarian cancer tissues. In this study, we investigated the role of miR-497 in ovarian cancer metastasis. We found that endogenous miR-497 expression was down-regulated in the more aggressive ovarian cancer cell lines compared with the less aggressive cells. Exogenous expression of miR-497 suppressed ovarian cancer cell migration and invasion, whereas reduction of endogenous miR-497 expression induced tumor cell migration and invasion. Mechanistic investigations confirmed pro-metastatic factor SMURF1 as a direct target of miR-497 through which miR-497 ablated tumor cell migration and invasion. Further studies revealed that lower levels of miR-497 expression were associated with shorter overall survival as well as increased SMURF1 expression in ovarian cancer patients. Our results indicate that down-regulation of miR-497 in ovarian cancer may facilitate tumor metastasis. Restoration of miR-497 expression may be a promising strategy for ovarian cancer therapy.