Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

Digestive tract diseases are presently the hotspot of clinical diagnosis and treatment, and the incidence of digestive tract tumor is increasing annually. Surgery remains the main therapeutic schedule for digestive tract tumor. Though benefits were brought by neoadjuvant chemotherapy, a part of patients lose the chance of surgery because of late detection or inappropriate intervention. Therefore, the treatment of inoperable patients has become an urgent need. At the same time, tumor metabolism is an extremely complex and diverse process. Natural products are confirmed effective to inhibit the development of tumors in vitro and in vitro. There are many kinds of natural products and their functions remain not clear. However, some natural products such as polyphenols have been proven to have definite anti-cancer effects, and some terpenoids have definite anti-inflammatory, anti-ulcer, anti-tumor, and other effects. Therefore, the anti-tumor characteristics of natural products should arouse our high attention. Although there are many obstacles to study the activities of natural products in tumor, including the difficulty in detection or distinguishing each component due to their low levels in tumor tissue, etc., the emergence of highly sensitive and locatable spatial metabolomics make the research and application of natural products a big step forward. In this review, natural products such as phenols, terpenoids and biotinoids were summarized to further discuss the development and therapeutic properties of natural metabolites on digestive tract tumors.

Background: Primary central nervous system lymphoma (PCNSL) is characterized by a lack of specificity and poor prognosis. Further understanding of the tumor heterogeneity and molecular phenotype of PCNSL is of great significance for improving the diagnosis and treatment of this disease. Methods: To explore the distinct phenotypic states of PCNSL, transcriptome-wide single-cell RNA sequencing was performed on 34,851 PCNSL cells from patients. The cell types, heterogeneity, and gene subset enrichment of PCNSL were identified. A comparison of the PCNSL cells with 21,250 normal human fetal brain (nHFB) cells was further analyzed to reveal the differences between PCNSL and normal sample. Results: Six cell populations were mainly identified in the PCNSL tissue, including four types of immune cells—B cell, T cell, macrophage and dendritic cell—and two types of stromal cells: oligodendrocyte and meningeal cell. There are significant cellular interactions between B cells and several other cells. Three subpopulations of B cells indicating diffident functions were identified, as well as a small number of plasma cells. Different subtypes of T cells and dendritic cells also showed significant heterogeneity. It should be noted that, compared with normal, the gene expression and immune function of macrophages in PCNSL were significantly downregulated, which may be another important feature of PCNSL in addition to B cell lesions and may be a potential target for PCNSL therapy.

Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.

Prostate cancer (PCa) is the fifth leading cause of death from cancer in men worldwide. Its treatment remains challenging due to the heterogeneity of the tumor, mainly because of the lack of effective and targeted prognostic markers at the system biology level. First, the data were retrieved from TCGA dataset, and valid samples were obtained by consistent clustering and principal component analysis; next, key genes were analyzed for prognosis of PCa using WGCNA, MEGENA, and LASSO Cox regression model analysis, while key genes were screened based on disease-free survival significance. Finally, TIMER data were selected to explore the relationship between genes and tumor immune infiltration, and GSCAlite was used to explore the small-molecule targeted drugs that act with them. Here, we used tumor subtype analysis and an energetic co-expression network algorithm of WGCNA and MEGENA to identify a signal dominated by the ROMO1 to predict PCa prognosis. Cox regression analysis of ROMO1 was an independent influence, and the prognostic value of this biomarker was validated in the training set, the validated data itself, and external data, respectively. This biomarker correlates with tumor immune infiltration and has a high degree of infiltration, poor prognosis, and strong correlation with CD8+T cells. Gene function annotation and other analyses also implied a potential molecular mechanism for ROMO1. In conclusion, we putative ROMO1 as a portal key prognostic gene for the diagnosis and prognosis of PCa, which provides new insights into the diagnosis and treatment of PCa.