Targeting AURKA in treatment of peritoneal tumor dissemination in gastrointestinal cancer

Targeting SNCA in the treatment of malignant ascites in gastrointestinal cancer

Peritoneal tumor dissemination and subsequent malignant tumor ascites (MTA) occur unexpectedly and repeatedly in patients with gastrointestinal (GI) cancers, and worsen quality of life and prognosis of the patients. Various treatments have been clinically developed for these patients, while most of the MTA cases are refractory to the treatments. Thus, effective treatments are urgently needed to improve the clinical outcomes. In this study, we identified α-synuclein (SNCA) as an immunological determinant of MTA progression in GI cancer through translational research using mouse tumor models and clinical specimens collected from gastric cancer patients. We found that the SNCA+ subsets were significantly increased in CD3+ T cells, CD56+ NK cells, and CD11b+ myeloid cells within MTA and peripheral blood cells (PBCs) of MTA cases, albeit almost absent in PBCs of healthy donors, and spleen of naive mice. Of note, the SNCA+ T-cell subset was rarely seen in patients that intraperitoneal lavage fluid without tumor cells was collected before surgery as a tumor-free control, suggesting a possible cancer-induced product, especially within the peritoneal cavity. In vivo treatment with anti-SNCA blocking mAb significantly induced anti-tumor effects in mouse MTA models, and synergistically improved anti-PD1 therapeutic efficacy, providing a significantly better prognosis. These suggest that SNCA is involved in severe immunosuppression in the MTA cases, and that blocking SNCA is effective in dramatically improving the immune status in the hosts. Targeting SNCA will be a promising strategy to improve clinical outcomes in the treatment of GI cancer patients, especially with MTA.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis

BACKGROUND

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis.

METHODS

The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes.

RESULTS

We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle.

CONCLUSION

Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

IL25+ macrophages are a key determinant of treatment resistance of IL17RB+ breast cancer

Recurrence and metastasis are resistant to multimodal treatments, and are the major causes of death in breast cancer. Accumulating evidence suggests that the IL17RB signaling pathway plays a key role in progression and metastasis of breast cancer. Clinical significance of the IL17RB positivity in tumor tissues has been also reported as a poor prognostic factor in breast cancer. However, the molecular mechanisms underlying the poor prognosis of patients with IL17RB+ breast cancer, particularly the immunological aspects, remain to be fully elucidated, and elimination of the IL17RB+ tumors has not been practically achieved in clinical settings. In this study, we identified a distinct molecular mechanism underlying the intractability of the IL17RB+ tumors through tumor biological and immunological investigation using mouse and human breast cancer cells transduced with il17rb gene. IL17RB overexpression in tumor cells confers cancer stemness, including high invasive and self-renewal abilities, and high resistance to CDK4/6 inhibitors that have been considered as a promising agent for treating breast cancer despite the limited efficacy. In the mice implanted with the IL17RB+ tumors, IL25+ macrophages (Møs) are expanded locally in tumor tissues and systemically in spleen, and promote the IL17RB+ tumor progression directly by intensifying the tumor functions, and indirectly via impairment of anti-tumor effector CTLs and NK cells utilizing the secreted IL25. Blocking IL25 with the specific mAb, however, interferes the adverse events, and successfully elicits significant anti-tumor efficacy in combination with CDK4/6 inhibitors providing better survival in murine mammary tumor models. These results suggest that the IL25+ Mø is a key determinant of building the solid treatment resistance of the IL17RB+ breast cancer. Targeting the IL17RB-IL25 axis may be a promising strategy to improve clinical outcomes in the treatment of breast cancer patients, particularly with IL17RB+ tumors.

Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers

BACKGROUND

Tumor endothelial cells (TECs) play a significant role in regulating the tumor microenvironment, drug response, and immune cell activities in various cancers. However, the association between TEC gene expression signature and patient prognosis or therapeutic response remains poorly understood.

METHODS

We analyzed transcriptomics data of normal and tumor endothelial cells obtained from the GEO database to identify differentially expressed genes (DEGs) associated with TECs. We then compared these DEGs with those commonly found across five different tumor types from the TCGA database to determine their prognostic relevance. Using these genes, we constructed a prognostic risk model integrated with clinical features to develop a nomogram model, which we validated through biological experiments.

RESULTS

We identified 12 TEC-related prognostic genes across multiple tumor types, of which five genes were sufficient to construct a prognostic risk model with an AUC of 0.682. The risk scores effectively predicted patient prognosis and immunotherapeutic response. Our newly developed nomogram model provided more accurate prognostic estimates of cancer patients than the TNM staging method (AUC = 0.735) and was validated using external patient cohorts. Finally, RT-PCR and immunohistochemical analyses indicated that the expression of these 5 TEC-related prognostic genes was up-regulated in both patient-derived tumors and cancer cell lines, while depletion of the hub genes reduced cancer cell growth, migration and invasion, and enhanced their sensitivity to gemcitabine or cytarabine.

CONCLUSIONS

Our study discovered the first TEC-related gene expression signature that can be used to construct a prognostic risk model for guiding treatment options in multiple cancers.

Identification, characterization, and prognosis investigation of pivotal genes shared in different stages of breast cancer

One of the leading causes of death (20.1 per 100,000 women per year), breast cancer is the most prevalent cancer in females. Statistically, 95% of breast cancer are categorized as adenocarcinomas, and 55% of all patients may go into invasive phases; however, it can be successfully treated in approximately 70-80% of cases if diagnosed in the nascent stages. The emergence of breast tumor cells which are intensely resistant to conventional therapies, along with the high rate of metastasis occurrence, has highlighted the importance of finding novel strategies and treatments. One of the most advantageous schemes to alleviate this complication is to identify the common differentially expressed genes (DEGs) among primary and metastatic cancerous cells to use resultants for designing new therapeutic agents which are able to target both primary and metastatic breast tumor cells. In this study, the gene expression dataset with accession number GSE55715 was analyzed containing two primary tumor samples, three bone-metastatic samples, and three normal samples to distinguish the up- and down regulated genes in each stage compared to normal cells as control. In the next step, the common upregulated genes between the two experimental groups were detected by Venny online tool. Moreover, gene ontology, functions and pathways, gene-targeting microRNA, and influential metabolites were determined using EnrichR 2021 GO, KEGG pathways miRTarbase 2017, and HMDB 2021, respectively. Furthermore, elicited from STRING protein-protein interaction networks were imported to Cytoscape software to identify the hub genes. Then, identified hub genes were checked to validate the study using oncological databases. The results of the present article disclosed 1263 critical common DEGs (573 upregulated + 690 downregulated), including 35 hub genes that can be broadly used as new targets for cancer treatment and as biomarkers for cancer detection by evaluation of expression level. Besides, this study opens a new horizon to reveal unknown aspects of cancer signaling pathways by providing raw data evoked from in silico experiments. This study's outcomes can also be widely utilized in further lab research since it contains diverse information on common DEGs of varied stages and metastases of breast cancer, their functions, structures, interactions, and associations.

Targeting myeloid villains in the treatment with immune checkpoint inhibitors in gastrointestinal cancer

Despite the clinical outcomes being extremely limited, blocking immune inhibitory checkpoint pathways has been in the spotlight as a promising strategy for treating gastrointestinal cancer. However, a distinct strategy for the successful treatment is obviously needed in the clinical settings. Myeloid cells, such as neutrophils, macrophages, dendritic cells, and mast cells, are the majority of cellular components in the human immune system, but have received relatively less attention for the practical implementation than T cells and NK cells in cancer therapy because of concentration of the interest in development of the immune checkpoint blocking antibody inhibitors (ICIs). Abnormality of myeloid cells must impact on the entire host, including immune responses, stromagenesis, and cancer cells, leading to refractory cancer. This implies that elimination and reprogramming of the tumor-supportive myeloid villains may be a breakthrough to efficiently induce potent anti-tumor immunity in cancer patients. In this review, we provide an overview of current situation of the IC-blocking therapy of gastrointestinal cancer, including gastric, colorectal, and esophageal cancers. Also, we highlight the possible oncoimmunological components involved in the mechanisms underlying the resistance to the ICI therapy, particularly focusing on myeloid cells, including unique subsets expressing IC molecules. A deeper understanding of the molecular and cellular determinants may facilitate its practical implementation of targeting myeloid villains, and improve the clinical outcomes in the ICI therapy of gastrointestinal cancer.