Despite the success of cancer immunotherapy in treating hematologic malignancies, their efficacy in solid tumors remains limited due to the immunosuppressive tumor microenvironment (TME), which is mainly formed by myeloid-derived suppressor cells (MDSCs). MDSCs not only exert potent immunosuppressive effects that hinder the success of immune checkpoint inhibitors (ICIs) and adaptive cellular therapies, but they also promote tumor advancement through non-immunological pathways, including promoting angiogenesis, driving epithelial-mesenchymal transition (EMT), and contributing to the establishment of pre-metastatic environments. While targeting MDSCs alone or in combination with conventional therapies has shown limited success, emerging evidence suggests that MDSC checkpoint blockade in combination with other immunotherapies holds great promise in overcoming both immunological and non-immunological barriers. In this review, we discussed the dual roles of MDSCs, with a particular emphasis on their underexplored checkpoints blockade strategies. We discussed the rationale behind combination strategies, their potential advantages in overcoming MDSC-mediated immunosuppression, and the challenges associated with their development. Additionally, we highlight future research directions aimed at optimizing combination immunotherapies to enhance cancer therapeutic effectiveness, particularly in solid tumor therapies where MDSCs are highly prevalent.

Emerging evidence has shown the potential of oral microbiota as a noninvasive diagnostic tool in gastrointestinal (GI) cancer. PubMed, Web of Science, and Embase were systematically searched for eligible studies published until May 31, 2019. Of the 17 included studies published between 2011 and 2019, five kinds of GI cancer, including colorectal cancer (n=6), pancreatic cancer (n=5), gastric cancer (n=4), esophageal cancer (n=2) and liver cancer (n=1), were reported. Generally, the diagnostic performance of the multi-bacteria model for GI cancer was strong with the best area under the receiver operator characteristic curve (AUC) exceeding 0.90, but only one study had a validation phase. Pathogens involved in periodontal disease, such as Porphyromonas gingivalis and Tannerella forsythia, were linked to various kinds of GI cancer. Besides, more oral bacteria significantly differed between cases with upper digestive cancer and healthy controls when compared to colorectal cancer (the most common form of lower digestive cancer), probably indicating a different mechanism due to anatomical and physiological differences in the digestive tract. Oral microbiota changes were associated with risk of various kinds of GI cancer, which could be considered as a potential tool for early prediction and prevention of GI cancer, but validation based on a large population, reproducible protocols for oral microbiota research and oral-gut microbiota transmission patterns are required to be resolved in further studies.

Objective The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs. Methods This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model. Results Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines. Conclusion The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model’s test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging.

Previous studies have shown a strong coexistence of colorectal neoplasia (CRN) and cardiovascular diseases (CVD). This study was aimed to summarize the available evidence on association of CVD risk with early CRN detection in asymptomatic populations. PubMed, Web of Science, and Embase were systematically searched for eligible studies published until Dec 20, 2019. Studies exploring the associations of recommended CVD risk assessment methods (e.g., risk scores, carotid artery plaque, and coronary artery calcium score [CACS]) with risk of CRN were included. Meta-analyses were conducted to determine the overall association of CVD risk with the CRN. A total of 12 studies were finally included. The association of carotid artery plaque with the risk of colorectal adenoma (AD) was weakest (pooled odds ratio [OR)] 1.27, 95% confidence interval [CI), 1.12, 1.45]. Participants with CACS> 100 had about 2-fold increased risk of AD than those with CACS=0. The pooled ORs were 3.36 (95% CI, 2.15, 5.27) and 2.30 (95% CI, 1.69, 3.13) for the risk of advanced colorectal neoplasia (AN) and AD, respectively, in participants with Framingham risk score (FRS)> 20%, when compared to participants at low risk (FRS< 10%). FRS might help identify subgroups at increased risk for AN, but further studies are needed.

Inflammatory bowel disease (IBD) presents significant therapeutic challenges. The immune regulatory protein TIPE2 is a risk factor for inflammatory diseases including IBD, but its potential as a therapeutic target remains undefined. Here, we developed a replication-defective adenovirus encoding TIPE2-specific short hairpin RNA (shRNA) (AdV-shTIPE2) to knock down TIPE2 gene expression in mice and tested its therapeutic potential for treating dextran sulfate sodium (DSS)-induced colitis. Intravenous AdV-shTIPE2 administration conferred profound protection against colitis as judged by body weight loss, fecal bleeding, and colon length changes. Histological analyses also revealed significant preservation of epithelial integrity and diminished pathological damage in AdV-shTIPE2-treated mice. Mechanistically, TIPE2 knockdown limited infiltration of CD45⁺ leukocytes, particularly CD11b⁺Ly6G⁻ myeloid cells, and reduced colonic inflammatory cytokine gene expression. Analysis of human IBD bulk and single-cell transcriptomic data confirmed TIPE2 upregulation and identified a distinct TIPE2-expressing monocyte subpopulation highly enriched in human intestinal lesions of IBD patients, demonstrating a conserved pathogenic TIPE2-myeloid axis across species. Together, these results validate TIPE2-driven inflammatory cell infiltration as a key driver of IBD and establish targeted TIPE2 silencing as a promising therapeutic strategy worthy of future clinical investigation.