Integrated cancer network

BACKGROUND: Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications...

OBJECTIVE: Brain tumor segmentation on magnetic resonance imaging (MRI) plays an important role in assisting the diagnosis and treatment of cancer patients. Recently, cascaded U-Net models have achieved excellent performance via conducting coarse-to-fine segmentation of MRI brain tumors. However, they still suffer from obvious global and local differences among various brain tumors, which are difficult to solve with conventional convolutions. APPROACH: To address the issue, this work proposes a novel Adaptive Cascaded Transformer U-Net (ACTransU-Net) for MRI brain tumor segmentation, which simultaneously integrates Transformer and dynamic convolution into a single cascaded U-Net architecture to adaptively capture global information and local details of brain tumors...

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC. METHODS: The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively...

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions...

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style...

OBJECTIVES: The goal of this investigation was to identify the main compounds and the pharmacological mechanism of the traditional Chinese medicine formulation, Gong Ying San (GYS), by infrared spectral absorption characteristics, metabolomics, network pharmacology, and molecular-docking analysis for mastitis. The antibacterial and antioxidant activities were determined in vitro. METHODS: The chemical constituents of GYS were detected by ultra-high-performance liquid chromatography Q-extractive mass spectrometry (UHPLC-QE-MS)...

BACKGROUND: Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection...

Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large- and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue- and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions...

Metastatic colorectal cancer (CRC) is still in need of effective treatments. This study applies a holistic approach to propose new targets for treatment of primary and liver metastatic CRC and investigates their therapeutic potential in-vitro. An integrative analysis of primary and metastatic CRC samples was implemented for alternative target and treatment proposals. Integrated microarray samples were grouped based on a co-expression network analysis. Significant gene modules correlated with primary CRC and metastatic phenotypes were identified...

The chemokines of the immune system act as first responders by operating as chemoattractants, directing immune cells to specific locations of inflamed tissues. This promiscuous network is comprised of 50 ligands and 18 receptors where the ligands may interact with the receptors in various oligomeric states i.e., monomers, homodimers, and heterodimers. Chemokine receptors are G-protein coupled receptors (GPCRs) present in the membrane of immune cells. The migration of immune cells occurs in response to a concentration gradient of the ligands...

IMPORTANCE: Organized screening outreach can reduce differences in colorectal cancer (CRC) incidence and mortality between demographic subgroups. Outcomes associated with additional outreach, beyond universal outreach, are not well known. OBJECTIVE: To compare CRC screening completion by race and ethnicity, age, and sex after universal automated outreach and additional personalized outreach. DESIGN, SETTING, AND PARTICIPANTS: This observational cohort study included screening-eligible individuals aged 50 to 75 years assessed during 2019 in a community-based organized CRC screening program within the Kaiser Permanente Northern California (KPNC) integrated health care delivery setting...

UNLABELLED: The 26S proteasome is the major protein degradation machinery in cells. Cancer cells use the proteasome to modulate gene expression networks that promote tumor growth. Proteasome inhibitors have emerged as effective cancer therapeutics, but how they work mechanistically remains unclear. Here, using integrative genomic analysis, we discovered unexpected reprogramming of the chromatin landscape and RNA polymerase II (RNAPII) transcription initiation in breast cancer cells treated with the proteasome inhibitor MG132...

MOTIVATION: Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines...

The alcohol extraction of P. sibiricum has exhibited significant inhibitory effects on the production of free radicals and the proliferation of non-small-cell lung carcinoma (NSCLC) A549 cells. Despite the diverse components found in alcohol extraction of P. sibiricum and its multiple targets, the active components and associated targets remain largely unidentified. Hence, there is a need for additional investigation into the pharmacodynamic elements and mechanisms of action. This study aimed to analyze and identify the components responsible for the anti-tumor activity of alcohol extraction from P...

BACKGROUND: Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. METHODS: This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture...

BACKGROUND: The molecular diagnostic and therapeutic pathway of Non-Small Cell Lung Cancer (NSCLC) stands as a successful example of precision medicine. The scarcity of material and the increasing number of biomarkers to be tested have prompted the routine application of next-generation-sequencing (NGS) techniques. Despite its undeniable advantages, NGS involves high costs that may impede its broad adoption in laboratories. This study aims to assess the detailed costs linked to the integration of NGS diagnostics in NSCLC to comprehend their financial impact...

BACKGROUND: Chromatin regulators (CRs) are implicated in the development of cancer, but a comprehensive investigation of their role in colon adenocarcinoma (COAD) is inadequate. The purpose of this study is to find CRs that can provide recommendations for clinical diagnosis and treatment, and to explore the reasons why they serve as critical CRs. METHODS: We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted Gene Co-Expression Network Analysis (WGCNA) screened tumor-associated CRs...

The Cancer Genome Atlas (TCGA) Research Network described 4 molecular subgroups of endometrial carcinomas with different outcome: 1) POLE ultramutated endometrioid carcinomas which have an indolent behavior; 2) microsatellite instability hypermutated endometrioid carcinomas associated with intermediate prognosis; 3) copy-number low endometrioid carcinomas also with intermediate prognosis; and 4) copy-number high predominantly serous (non-endometrioid) but also serous-like endometrioid carcinomas, almost always carrying TP53 mutations, with poor clinical outcome...

Chaihu Shugan San (CSS) is a well-known traditional herbal formula that has the potential to ameliorate hepatocellular carcinoma (HCC); however, its mechanism of action remains unknown. Here, we identified the key targets of CSS against HCC and developed a prognostic model to predict the survival of patients with HCC. The effect of CSS plus sorafenib on HCC cell proliferation was evaluated using the MTT assay. LASSO-Cox regression was used to establish a three-gene signature model targeting CSS. Correlations between immune cells, immune checkpoints and risk score were determined to evaluate the immune-related effects of CSS...

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM)...