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Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a malignant cancer and chemotherapy ineffectively treats PDAC, leading to the requirement for alternative tumor-targeted treatment. Human amniotic fluid mesenchymal stem cells (hAFMSCs) have been revealed to suppress tumor growth in various cancers and they are a strong candidate for treating PDAC. Methods To evaluate the effects of hAFMSCs on human pancreatic carcinoma cells (PANC1, AsPC1 and BxPC3 cell lines) and the possible mechanism involved, an in vitro cell coculture system was used. A PANC1 orthotopic xenograft mouse model was established and hAFMSCs were injected intravenously at 4 weeks post-xenograft. Results An in vitro coculture assay showed that hAFMSCs inhibited PANC1 cell proliferation by inducing S phase cell cycle arrest and increased cell apoptosis in a time-dependent manner. In PANC1 cells, hAFMSCs caused the downregulation of Cyclin A and Cyclin B1 as well as the upregulation of p21 (CDKN1A) at 24 h post coculture. The upregulation of pro-apoptotic factors Caspase-3/-8 and Bax at 24 h post coculture reduced the migration and invasion ability of PANC1 cells through inhibiting the epithelial-mesenchymal transition (EMT) process. In a PANC1 orthotopic xenograft mouse model, a single injection of hAFMSCs showed significant tumor growth inhibition with evidence of the modulation of cell cycle and pro-apoptotic regulatory genes and various genes involved in matrix metallopeptidase 7 (MMP7) signaling-triggered EMT process. Histopathological staining showed lower Ki67 levels in tumors from hAFMSCs-treated mice. Conclusions Our data demonstrated that hAFMSCs strongly inhibit PDAC cell proliferation, tumor growth and invasion, possibly by altering cell cycle arrest and MMP7 signaling-triggered EMT.

Objective: Breast cancer patients often suffer from body image disturbance due to impairment of their body/appearance resulting from surgeries, other cancer treatments, and/or their complications. Cognitive-based interventions (CBIs) have recently been adopted for patients having breast cancer but their effects on improving body image are uncertain. This systematic review aimed to examine the effects of CBIs on body image in these patients, identify the optimal dose, characteristics, and/or component(s) of an effective intervention for these patients to inform future research and practice. Methods: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, ten online databases and five search engines were used to search for eligible studies. Quality appraisal of included studies and meta-analysis results were conducted using Rob 2 and Grading of Recommendations Assessment, Development and Evaluation profiler Guideline Development Tool, respectively. RevMan and comprehensive meta-analysis software were used to perform data analysis and synthesis. Results: Eleven eligible randomized controlled trials (RCTs) examining the effects of cognitive behavioral, acceptance and commitment, mindfulness, and self-compassion therapies were reviewed. Results of the meta-analysis showed that CBIs significantly reduced negative body image perception (Standardised Mean Difference, SMD ​= ​−0.49, 95% confidence interval [CI], [−0.87, −0.11], I2 ​= ​81%, 6 RCTs, 758 participants), when compared to the control groups (mainly usual care) at immediately post-intervention; in which, CBT-based (SMD ​= ​−0.37, 95% CI (−0.60, −0.13), I2 ​= ​0%) and group-based (SMD ​= ​−0.38, 95% CI (−0.62, −0.13), I2 ​= ​0%) programs had more consistent and significant effects. Conclusions: In view of the highly heterogeneous and limited RCTs identified, high-quality controlled trials of CBIs for improving the body image of patients having breast cancer are suggested. Systematic review registration: PROSPERO, CRD42021259173.

Parents of young children with eczema often experience adverse mental health consequences, including depression, anxiety, stress, and a reduced health-related quality of life (HRQoL), due to the unpredictable nature of flare-ups and exacerbations. This study investigated the roles of psychological flexibility, self-compassion, and self-efficacy in fostering parental mental health outcomes and HRQoL while caring for children diagnosed with eczema. Baseline data from an ongoing clinical trial examining the effects of a family acceptance-and-commitment-therapy-based eczema management program (FACT-EMP) on parent–child dyads affected by eczema (N = 110 dyads, 75.5% mothers; 66.4% boys) were analyzed using adjusted hierarchical regression analyses. The findings indicate that psychological inflexibility was significantly associated with symptoms of anxiety, depression, stress, and HRQoL. Self-compassion was significantly linked to all assessed mental health outcomes, whereas self-efficacy showed a significant association only with symptoms of depression. These results underscore the significance of promoting parental psychological flexibility and self-compassion through acceptance and commitment therapy and compassion-based approaches to enhance mental health and quality of life while managing children’s eczema.

MicroRNA (miRNA) 107 expression is downregulated but Wnt3a protein and β-catenin are upregulated in degenerated intervertebral disc (IVD). We investigated mir-107/Wnt3a-β-catenin signaling in vitro and in vivo following hyperbaric oxygen (HBO) intervention. Our results showed 96 miRNAs were upregulated and 66 downregulated in degenerated nucleus pulposus cells (NPCs) following HBO treatment. The 3′ untranslated region (UTR) of the Wnt3a mRNA contained the “seed-matched-sequence” for miR-107. MiR-107 was upregulated and a marked suppression of Wnt3a was observed simultaneously in degenerated NPCs following HBO intervention. Knockdown of miR-107 upregulated Wnt3a expression in hyperoxic cells. HBO downregulated the protein expression of Wnt3a, phosphorylated LRP6, and cyclin D1. There was decreased TOP flash activity following HBO intervention, whereas the FOP flash activity was not affected. HBO decreased the nuclear translocation of β-catenin and decreased the secretion of MMP-3 and -9 in degenerated NPCs. Moreover, rabbit serum KS levels and the stained area for Wnt3a and β-catenin in repaired cartilage tended to be lower in the HBO group. We observed that HBO inhibits Wnt3a/β-catenin signaling-related pathways by upregulating miR-107 expression in degenerated NPCs. HBO may play a protective role against IVD degeneration and could be used as a future therapeutic treatment.

Herein, A non-invasive pathomics approach was developed to reveal the methylation status in patients with cervical squamous cell carcinoma and predict clinical outcomes and treatment response. Using the MethylMix algorithm, 14 methylation-driven genes were selected for further analysis. We confirmed that methylation-driven genes were differentially expressed in immune, stromal, and tumor cells. In addition, we constructed a methylation-driven model and explored the alterations in immunocyte infiltration between the different models. The methylation-driven subtypes identified in our investigation could effectively predict the clinical outcomes of cervical cancer. To further evaluate the level of methylation-driven patterns, we constructed a risk model with four genes. Significant correlations were observed between the score and immune response markers, including PD1 and CTLA4. Multiple immune infiltration algorithms evaluated the level of immunocyte infiltration between the high- and low-risk groups, while the components of anti-tumor immunocytes in the low-risk group were significantly increased. Subsequently, a total of 205 acquired whole-slide imaging (WSI) images were processed to capture image signatures, and the pathological algorithm was employed to construct an image prediction model based on the risk score classification. The model achieved an area under the curve (AUC) of 0.737 and 0.582 for the training and test datasets, respectively. Moreover, we conducted vitro assays for validation of hub risk gene. The proposed prediction model is a non-invasive method that combines pathomics features and genomic profiles and shows satisfactory performance in predicting patient survival and treatment response. More interdisciplinary fields combining medicine and electronics should be explored in the future.