RADIP technology comprehensively identifies H3K27me3-associated RNA-chromatin interactions.

Many RNAs associate with chromatin, either directly or indirectly. Several technologies for mapping regions where RNAs interact across the genome have been developed to investigate the function of these RNAs. Obtaining information on the proteins involved in these RNA-chromatin interactions is critical for further analysis.

Development and validation of a nomogram for predicting microvascular invasion and evaluating the efficacy of postoperative adjuvant transarterial chemoembolization.

Background And Aim: Accurately predicting microvascular invasion (MVI) before surgery is beneficial for surgical decision-making, and some high-risk hepatocellular carcinoma (HCC) patients may benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). The purpose of this study was to develop and validate a novel nomogram for predicting MVI and assessing the survival benefits of selectively receiving PA-TACE in HCC patients.

Methods: The 1372 HCC patients who underwent hepatectomy at four medical institutions were randomly divided into training and validation datasets according to a 7:3 ratio.

Genetic prediction of the relationship between metabolic syndrome and colorectal cancer risk: a Mendelian randomization study.

Background: Despite a growing body of observational studies indicating a potential link between metabolic syndrome and colorectal cancer, a definitive causal relationship has yet to be established. This study aimed to elucidate the causal relationship between metabolic syndrome and colorectal cancer through Mendelian randomization.

Methods: We screened for instrumental variables associated with metabolic syndrome and its diagnostic components and with colorectal cancer through the use of a genome-wide association study database, and conducted a preliminary Mendelian randomization analysis.

Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.

""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

Background: Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Antisense-oligonucleotide-mediated perturbation of long non-coding RNA reveals functional features in stem cells and across cell types.

Within the scope of the FANTOM6 consortium, we perform a large-scale knockdown of 200 long non-coding RNAs (lncRNAs) in human induced pluripotent stem cells (iPSCs) and systematically characterize their roles in self-renewal and pluripotency. We find 36 lncRNAs (18%) exhibiting cell growth inhibition. From the knockdown of 123 lncRNAs with transcriptome profiling, 36 lncRNAs (29.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images.

Background: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.

Background: This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).

Materials And Methods: This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, = 388; internal validation cohort, = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.

Background: DNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).

Methods: In this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included.

microRNA-15b-5p encapsulated by M2 macrophage-derived extracellular vesicles promotes gastric cancer metastasis by targeting BRMS1 and suppressing DAPK1 transcription.

Background: Extracellular vesicles (EVs) derived from tumor-associated macrophages are implicated in the progression and metastasis of gastric cancer (GC) via the transfer of molecular cargo RNAs. We aimed to decipher the impact of microRNA (miR)-15b-5p transferred by M2 macrophage-derived EVs in the metastasis of GC.

Methods: Expression of miR-15b-5p was assessed and the downstream genes of miR-15b-5p were analyzed.

A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.

As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear.

Identification of Circular RNA-Based Immunomodulatory Networks in Colorectal Cancer.

Background: Circular RNAs (circRNAs) have been recently proposed as hub molecules in various diseases, especially in tumours. We found that circRNAs derived from ribonuclease P RNA component H1 (RPPH1) were highly expressed in colorectal cancer (CRC) samples from Gene Expression Omnibus (GEO) datasets.

Objective: We sought to identify new circRNAs derived from RPPH1 and investigate their regulation of the competing endogenous RNA (ceRNA) and RNA binding protein (RBP) networks of CRC immune infiltration.

Silencing Inhibits the Proliferation and Metastasis of Gastric Cancer.

RNF114 (E3 ubiquitin ligase RING finger protein 114) was first identified as a zinc-binding protein that promotes psoriasis development; however, its role in gastric cancer is still unclear. We explored the relationship between and gastric cancer using bioinformatics and molecular biology techniques. The results showed that RNF114 was highly expressed in gastric cancer and negatively correlated with the patient's prognosis.