Background: Hepatocellular carcinoma is the most common type of primary liver cancer, and it is associated with poor prognosis. It often fails to respond to immunotherapy, highlighting the need to identify genes that are associated with the tumor microenvironment and may be good therapeutic targets. We and others have shown that the Holliday cross-recognition protein HJURP can promote the proliferation, migration, and invasion by hepatocellular carcinoma cells, and that HJURP overexpression is associated with poor survival. Here we explored the potential relationship between HJURP and the tumor microenvironment in hepatocellular carcinoma.Methods: We used the Immuno-Oncology-Biological-Research (IOBR) software package to analyze the potential roles of HJURP in the tumor microenvironment. Using single-cell RNA sequencing data, we identified the cell clusters expressing abundant HJURP, then linked some of these clusters to certain bioprocesses using Gene Set Enrichment Analysis (GSEA). We validated the differential expression of HJURP in tumor-infiltrating CD8+ T cells, sorted by flow cytometry into populations based on the expression level of PD-1. We used weighted gene co-expression network analysis (WGCNA) to identify immunity-related genes whose expression strongly correlated with that of HJURP. The function of these genes was validated based on enrichment in Gene Ontology (GO) terms, and they were used to establish a prognosis prediction model.Results: IOBR analysis suggested that HJURP is significantly related to the immunosuppressive tumor microenvironment and was significantly related to T cells, dendritic cells, and B cells. Based on single-cell RNA sequencing, HJURP was strongly expressed in T cells, erythrocytes, and B cells from normal liver tissues, as well as in CD8+ T cells, dendritic cells, and one cluster of hepatocytes in hepatocellular carcinoma tissues. Malignant hepatocytes strongly expressing HJURP were associated with the downregulation of immune bioprocesses. HJURP expression was significantly higher in CD8+ T cells strongly expressing PD-1 than in those expressing no or intermediate levels of PD1. WGCNA identified two module eigengenes (comprising 397 and 84 genes) related to the tumor microenvironment. We identified 24 hub genes and confirmed that they were related to immune regulation. A prognostic risk score model based on expression of HJURP, PPT1, PML, and CLEC7A showed moderate ability to predict survival.Conclusion:HJURP is associated with tumor-infiltrating immune cells, immune checkpoints, and immune suppression in hepatocellular carcinoma. HJURP-related genes involved in immune responses may be useful for predicting patient prognosis.

Titled the “most destructive of all cancers”, pancreatic cancer is a malignant tumor with a very poor prognosis and has a poor response to systemic therapy. At present, several studies have shown that tegafur-gimeracil-oteracil potassium (hereinafter referred to as TS-1) is no less superior to gemcitabine in the treatment of advanced pancreatic cancer. In addition, a number of current clinical studies have shown that targeted therapy combined with chemotherapy reflects therapeutic advantages in pancreatic cancer. Moreover, in vitro and in vivo experiments have also demonstrated that anlotinib can curb the proliferation of pancreatic cancer cells and induce their apoptosis. Here, we report for the first time that a patient with locally advanced pancreatic cancer achieved good efficacy after switching to TS-1 chemotherapy combined with anlotinib targeted therapy. Previously, the disease of the patient still rapidly progressed without control following the first switch to abraxane combined with gemcitabine chemotherapy (AG regimen) due to the progression after chemo-radiotherapy. In this case, the patient achieved progression-free survival (PFS) of over 14 months via the treatment with anlotinib targeted therapy combined with TS-1 chemotherapy and secondary radiotherapy (prior to secondary radiotherapy, the patient achieved a PFS of nearly 12 months via the treatment with oral anlotinib combined with TS-1). Up to now, the progress of the disease is ceased. The oral administration of targeted therapy and chemotherapy are still in progress and the general condition of the patient is good. This suggests that patients with advanced pancreatic cancer may benefit from treatment with the anlotinib targeted therapy combined with TS-1 chemotherapy.

BACKGROUND Recent studies have found a relationship between gut microbes and the primary location of colorectal cancer (CRC). However, most of these studies had limitations in sample size or sequencing methods. In this study, we collected metagenomic data from three studies and meta-analyzed the microbiological features according to the grouping of right-side colon cancer (RCC), left-side colon cancer (LCC), and rectal cancer (RC). METHODS We first identified confounding factors (except for tumor location) by two-way ANOVA and comparing species diversity. Subsequently, the microbial compositions were compared between different tumor locations. Microbial co-occurrence networks were established based on samples with different tumor locations. A prediction model for primary tumor location was constructed using a random forest algorithm based on microbial abundance features. Finally, tumor location and confounding factors were entered in the MAASLIN2 to identify differential species. Linear discriminant analysis (LDA) also identified the differential species. RESULTS Different study sources and BMI influenced gut microbiome and significantly altered α-diversity and β-diversity, bringing the confounding effect when analyzing gut microbial features in different tumor locations. However, α-diversity and β-diversity of gut microbiome had no significant difference in tumor locations. Species belonging to the Phylum of Actinobacteria, Firmicutes, and Proteobacteria played essential linkages in the three microbial networks, while Bacteroidetes were more critical in the microbial network of RCC. There are both the same hub species and different hub species among the three networks. The random forest classification model performed well in predicting RC (class error = 0.217) but poorly classified the RCC and LCC, with an overall classification error of 0.613. In comparing colon cancer (CC) with RC, MAASLIN2 and LDA identified six species significantly enriched in RC and thirteen in CC. In comparing RCC with LCC, MAASLIN2 identified nine species significantly enriched in RCC and six significantly enriched in LCC. Some of the differential species were reported to be associated with CRC location-related Molecular and immune features. CONCLUSION This study elucidated the relationship between gut microbiome and CRC location and confirmed that RCC, LCC, and RC had different enrich patterns of microbiota.