PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk.

OBJECTIVE: Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS: Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS: The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION: We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

[Expression of long non-coding RNA linc00467 in childhood acute myeloid leukemia and its role in drug resistance].

OBJECTIVE: To study the expression and function of long non-coding RNA linc00467 in childhood acute myeloid leukemia (AML). METHODS: Bone marrow samples were collected from 5 children with AML who were diagnosed from May 2016 to June 2018. Normal bone marrow samples based on bone marrow examination were collected from 3 children as controls. Quantitative real-time PCR was used to measure the expression of linc00467 in the two groups. A lentivirus system was used to achieve overexpression of linc00467 in AML cells (HL-60) (linc00467 overexpression group), and empty vector expressing green fluorescent protein (GFP) was transfected into AML cells to establish a GFP control group. A lentivirus system was used to insert an interfering sequence into AML cells (sh-linc00467 interfering group), and a random sequence was inserted to establish an sh-NC control group. Cell proliferation and resistance to doxorubicin were observed for all groups. RESULTS: Compared with the normal control group, the children with AML had a significant increase in linc00467 (P=0.018). Overexpression and interference with linc00467 expression had no significant effect on cell proliferation. Compared with the GFP control group, the linc00467 overexpression group had a significant increase in the viability of HL-60 cells at the adriamycin concentrations of 0.1, 0.2, 0.3, 0.4, and 0.5 µg/mL (P<0.05). Compared with the sh-NC control group, the sh-linc00467 interfering group had a significant reduction in the viability of HL-60 cells at the adriamycin concentrations of 0.1, 0.2, 0.3, 0.4, and 0.5 µg/mL (P<0.05). Compared with the untreated group, the adriamycin treatment group had a significant increase in the expression of linc00467 in HL-60 cells (P<0.05). CONCLUSIONS: This study reveals the biological function of linc00467 to promote the resistance to adriamycin in AML, which provides a basis for developing new therapeutic drugs for AML.

Asthma and Risk of Stroke: A Systematic Review and Meta-analysis.

BACKGROUND: Several studies have suggested that asthma is associated with an increased risk of stroke. However, the results are inconsistent. The aim of this study is to investigate the relation of asthma and the risk of stroke through a systematic review and meta-analysis of published research. METHODS: Pertinent studies were identified by a search of the PubMed and the Web of Science databases to June 2015. Study-specific hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using fixed-effect or random-effect models when appropriate. Associations were tested in subgroups representing different participants and study characteristics. Publication bias was assessed with Egger's test. RESULTS: Five articles comprising 524,637 participants and 6031 stroke cases were eligible for inclusion. Asthma was associated significantly with increased risk of stroke, and the pooled HR was 1.32 (95% CI: 1.13, 1.54, I(2)=80.4%). Subgroup analyses revealed that the association between asthma and stroke risk was stronger among female patients (HR = 1.42, 95% CI: 1.15-1.76) and prospective cohort study design (HR = 1.52, 95% CI: 1.21-1.91). CONCLUSION: Asthma is associated with a significantly increased risk of stroke. This finding may have clinical and public health importance.

Exposure to bisphenol A and the development of asthma: A systematic review of cohort studies.

BACKGROUND: There is conflicting evidence about the association between bisphenol A (BPA) exposure and childhood asthma risk. We aimed to review the epidemiological literature on the relationship between prenatal or postnatal exposure to BPA and the risk of childhood asthma/wheeze. METHODS: The PubMed database was systematically searched, and additional studies were found by searching reference lists of relevant articles. RESULTS: Six studies fulfilled the eligibility criteria. Three studies found that prenatal BPA exposure is associated with an increased risk of childhood wheeze, while another study reported a reduced risk of wheeze. Regarding the postnatal BPA exposure, three studies demonstrated an increased risk of childhood asthma/wheeze. CONCLUSIONS: The mean prenatal BPA was associated with the risk of childhood wheeze/asthma. Besides, the influence of BPA exposure during the second trimester of pregnancy on the prevalence of childhood wheeze was marked. Further studies are urgently needed to explore the underlying mechanism about adverse effect of BPA exposure on childhood wheeze/asthma.