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Inflammatory bowel disease (IBD) is a chronic systemic inflammatory condition regarded as a major risk factor for colitis‐associated cancer. However, the underlying mechanisms of IBD remain unclear. First, five GSE data sets available in GEO were used to perform 'batch correction' and Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). Candidate molecules were identified using CytoHubba, and their diagnostic effectiveness was predicted. The CIBERSORT algorithm evaluated the immune cell infiltration in the intestinal epithelial tissues of patients with IBD and controls. Immune cell infiltration in the IBD and control groups was determined using the least absolute shrinkage selection operator algorithm and Cox regression analysis. Finally, a total of 51 DEGs were screened, and nine hub genes were identified using CytoHubba and Cytoscape. GSE87466 and GSE193677 were used as extra data set to validate the expression of the nine hub genes. CD4‐naïve T cells, gamma–delta T cells, M1 macrophages and resting dendritic cells (DCs) are the main immune cell infiltrates in patients with IBD. Signal transducer and activator of transcription 1, CCR5 and integrin subunit beta 2 (ITGB2) were significantly upregulated in the IBD mouse model, and suppression of ITGB2 expression alleviated IBD inflammation in mice. Additionally, the expression of ITGB2 was upregulated in IBD‐associated colorectal cancer (CRC). The silence of ITGB2 suppressed cell proliferation and tumour growth in vitro and in vivo. ITGB2 resting DCs may provide a therapeutic strategy for IBD, and ITGB2 may be a potential diagnostic marker for IBD‐associated CRC. [ABSTRACT FROM AUTHOR]

Diffuse large B‐cell lymphoma (DLBCL) is the most common lymphoid subtype. However, unsatisfactory survival outcomes remain a major challenge, and the underlying mechanisms are poorly understood. N6‐methyladenosine (m6A), the most common internal modification of eukaryotic mRNA, participates in cancer pathogenesis. In this study, m6A‐associated long non‐coding RNAs (lncRNA) were retrieved from publicly available databases. Univariate, LASSO, and multivariate Cox regression analyses were performed to establish an m6A‐associated lncRNA model specific to DLBCL. Kaplan–Meier curves, principal component analysis, functional enrichment analyses and nomographs were used to study the risk model. The underlying clinicopathological characteristics and drug sensitivity predictions against the model were identified. Risk modelling based on the three m6A‐associated lncRNAs was an independent prognostic factor. By regrouping patients using our model‐based method, we could differentiate patients more accurately for their response to immunotherapy. In addition, prospective compounds that can target DLBCL subtypes have been identified. The m6A‐associated lncRNA risk‐scoring model developed herein holds implications for DLBCL prognosis and clinical response prediction to immunotherapy. In addition, we used bioinformatic tools to identify and verify the ceRNA of the m6A‐associated lncRNA ELFN1‐AS1/miR‐182‐5p/BCL‐2 regulatory axis. ELFN1‐AS1 was highly expressed in DLBCL and DLBCL cell lines. ELFN1‐AS1 inhibition significantly reduced the proliferation of DLBCL cells and promoted apoptosis. ABT‐263 inhibits proliferation and promotes apoptosis in DLBCL cells. In vitro and in vivo studies have shown that ABT‐263 combined with si‐ELFN1‐AS1 can inhibit DLBCL progression. [ABSTRACT FROM AUTHOR]

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has spread globally to over 200 countries with more than 23 million confirmed cases and at least 800,000 fatalities as of 23 August 2020. Declared a pandemic on March 11 by World Health Organization, the disease caused by SARS‐CoV‐2 infection, called coronavirus disease 2019 (COVID‐19), has become a global public health crisis that challenged all national healthcare systems. This review summarized the current knowledge about virologic and pathogenic characteristics of SARS‐CoV‐2 with emphasis on potential immunomodulatory mechanism and drug development. With multiple emerging technologies and cross‐disciplinary approaches proving to be crucial in our global response against COVID‐19, the application of PROteolysis TArgeting Chimeras strategy, CRISPR‐Cas9 gene editing technology, and Single‐Nucleotide‐Specific Programmable Riboregulators technology in developing antiviral drugs and detecting infectious diseases are proposed here. We also discussed the available but still limited epidemiology of COVID‐19 as well as the ongoing efforts on vaccine development. In brief, we conducted an in‐depth analysis of the pathogenesis of SARS‐CoV‐2 and reviewed the therapeutic options for COVID‐19. We also proposed key research directions in the future that may help uncover more underlying molecular mechanisms governing the pathology of COVID‐19. [ABSTRACT FROM AUTHOR]

Acute myeloblastic leukemia (AML) is the most prevalent form of AML in adults. Despite the availability of various treatment options, including radiotherapy and chemotherapy, many patients fail to respond to treatment or relapse. Copper is a necessary cofactor for all organisms; however, it turns toxic when concentrations reach a certain threshold maintained by homeostatic systems that have been conserved through evolution. However, the mechanism through which excess copper triggers cell death remains unknown. In this study, data on long non-coding RNAs (lncRNAs) related to cuproptosis were retrieved from publicly available databases. LASSO and univariate and multivariate Cox regression analyses were performed to establish an lncRNA model associated with cuproptosis specific to AML. To investigate the risk model, the Kaplan-Meier curve, principal component analysis, functional enrichment analysis, and nomographs were employed. The underlying clinicopathological characteristics were determined, and drug sensitivity predictions against the model were identified. Six cuproptosis-related lncRNA-based risk models were identified as the independent prognostic factors. By regrouping patients using a model-based method, we were able to more accurately differentiate patients according to their responses to immunotherapy. In addition, prospective compounds targeting AML subtypes have been identified. Using qRT-PCR, we examined the expression levels of six cuproptosis-associated lncRNAs in 30 clinical specimens. The cuproptosis-associated lncRNA risk-scoring model developed herein has implications in monitoring AML prognosis and in the clinical prediction of the response to immunotherapy. Furthermore, we identified and verified the ceRNA of the cuproptosis-related lncRNA HAGLR/miR-326/CDKN2A regulatory axis using bioinformatic tools. HAGLR is highly expressed in AML and AML cell lines. HAGLR inhibition significantly reduced the proliferation of AML cells and promoted apoptosis. Elesclomol promotes the degradation of CDKN2A and inhibits the proliferation of AML cells. Elesclomol combined with si-HAGLR inhibited the AML progression of AML both in vitro and in vivo ., Competing Interests: None., (AJCR Copyright © 2023.)

Cuproptosis is a novel type of cell death that may play a vital role in preventing various types of cancer. Studies examining cuproptosis are limited, and the cuproptosis-related lncRNAs (long non-Coding ribonucleic acids) involved in the regulation of colon cancer remain unclear. This study aimed to identify the prognostic signature of cupronosis-related lncRNAs and explore their potential molecular functions in colon cancer. Data on the clinical correlation were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed cuproptosis-related long non-coding ribonucleic acids (lncRNAs) were analyzed using the "limma" package. Then, the prognostic cuproptosis-related lncRNA signature (CupRLSig) was identified through univariate Cox and co-expression analyses, and a prognostic model was constructed based on CupRLSig using the least absolute shrinkage selection operator (LASSO) algorithm and Cox regression analysis. The Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used for evaluating the model's capacity for prognosis prediction. In addition, the immune landscape, and drug sensitivity of CupRLSig were analyzed. Finally, the functions of AL512306.3 and ZEB1-AS1 were verified through in vitro experiments. The high- or low-risk groups were classified according to the risk score. The signature-based risk score showed a stronger ability to predict patient's survival compared with the traditional clinicopathological features. In addition, immune responses, such as inflammation-promoting response and T-cell co-inhibition, were significantly different between the two groups. Moreover, chemotherapy drugs or inhibitors, such as axitinib, cisplatin, doxorubicin, and elesclomol, may be considered as potential therapeutic drugs for patients in high-risk groups. Finally, inhibition of AL512306.3 and ZEB1-AS1 significantly suppressed the cell proliferation in colon cancer cells. These results provide novel insights into the pathogenesis of colon cancer and offer promising biomarkers with the potential to guide research on carcinogenesis and cancer treatment., Competing Interests: None., (AJCR Copyright © 2022.)