NCAPG2 could be an immunological and prognostic biomarker: From pan-cancer analysis to pancreatic cancer validation

Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

Pan-Cancer Analysis and Experimental Validation of CEND1 as a Prognostic and Immune Infiltration-Associated Biomarker for Gliomas

Cell cycle exit and neuronal differentiation 1 (CEND1), highly expressed in the brain, is a specific transmembrane protein which plays a tumor suppressor role. This study is performed to investigate the role of CEND1 in various cancers through pan-cancer analysis, and further investigate its functions in gliomas by cell experiments. The expression and subcellular localization of CEND1 in different cancer types were analyzed utilizing the data from the GEPIA, UCSC, UALCAN and HPA databases. Relationships of CEND1 expression with prognosis, immunomodulation-related genes, immune checkpoint genes, microsatellite instability (MSI), tumor mutation burden (TMB) and RNA modifications were analyzed based on the TCGA database. The ESTIMATE algorithm was utilized to evaluate tumors' StromalScore, Immune Score, and ESTIMATES Score. The cBioPortal database was employed to analyze the categories and frequencies of CEND1 gene alterations. Biological functions and co-expression patterns of CEND1 in gliomas were explored using the LinkedOmics database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. The interactions between CEND1 and drugs were explored employing the Comparative Toxicogenomics Database and molecular docking technology. Cell experiments were conducted to analyze triptonide's effects on glioma cells through CCK-8, flow cytometry and qRT-PCR. CEND1 was lowly expressed in gliomas, and high CEND1 expression was correlated to better overall survival of glioma patients (HR = 0.65, P = 0.02). Deep deletion was the main type of hereditary change of CEND1 mutation. CEND1 expression was markedly associated with immune infiltration, TMB, MSI, and RNA modification in various tumors (r > 0.3, P < 0.05). CEND1 co-expressed genes in gliomas were markedly correlated with immune responses and cell cycle (FDR < 0.05). Triptonide could bind well to CEND1 (-5.0 kcal/mol), and triptonide could facilitate CEND1 expression in glioma cells and cell apoptosis, and block the cell cycle progression (P < 0.05). CEND1 serves as a potential biomarker for pan-cancer. Particularly in gliomas, CEND1 is a key regulator of cell apoptosis and cell cycle, and a potential target for glioma treatment.

Unveiling the role of RAC3 in the growth and invasion of cisplatin-resistant bladder cancer cells

Bladder cancer is one of the most prevalent cancers worldwide, and its morbidity and mortality rates have been increasing over the years. However, how RAC family small GTPase 3 (RAC3) affects the proliferation, migration and invasion of cisplatin-resistant bladder cancer cells remains unclear. Bioinformatics techniques were used to investigate the expression of RAC3 in bladder cancer tissues. Influences of RAC3 in the grade, stage, distant metastasis, and survival rate of bladder cancer were also examined. Analysis of the relationship between RAC3 expression and the immune microenvironment (TIME), genomic mutations, and stemness index. In normal bladder cancer cells (T24, 5637, and BIU-87) and cisplatin-resistant bladder cancer cells (BIU-87-DDP), the expression of RAC3 was detected separately with Western blotting. Plasmid transfection was used to overexpress or silence the expression of RAC3 in bladder cancer cells resistant to cisplatin (BIU-87-DDP). By adding activators and inhibitors, the activities of the JNK/MAPK signalling pathway were altered. Cell viability, invasion, and its level of apoptosis were measured in vitro using CCK-8, transwell, and flow cytometry. The bioinformatics analyses found RAC3 levels were elevated in bladder cancer tissues and were associated with a poor prognosis in bladder cancer. RAC3 in BIU-87-DDP cells expressed a higher level than normal bladder cancer cells. RAC3 overexpression promoted BIU-87-DDP proliferation. The growth of BIU-87-DDP cells slowed after the knockdown of RAC3, and RAC3 may have had an impact on the activation of the JNK/MAPK pathway.

MYC and NCAPG2 as molecular targets of colorectal cancer and gastric cancer in nursing

Colorectal cancer is a common malignant tumor in intestinal tract, the early symptoms are not obvious. Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium. However, the role of MYC and non-SMC condensin II complex subunit G2 (NCAPG2) in colorectal cancer and gastric cancer remains unclear. The colorectal cancer datasets GSE49355 and gastric cancer datasets GSE19826 were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Functional enrichment analysis, gene set enrichment analysis (GSEA) and immune infiltration analysis was performed. Construction and analysis of protein-protein interactions (PPI) network. Survival analysis and comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. A total of 751 DEGs were obtained. According to the gene ontology (GO) analysis, in Biological process (BP) analysis, they are mainly enriched in cell differentiation, cartilage development, and skeletal development. In cellular component (CC) analysis, they are mainly enriched in the cytoskeleton of muscle cells and actin filaments. In molecular function (MF) analysis, they are mainly concentrated in Rho GTPase binding, DNA binding, and fibronectin binding. In Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, they are mainly enriched in the MAPK signaling pathway, apoptosis, and cancer pathways. The soft threshold power for WGCNA analysis was set to 9, resulting in the generation of 40 modules. Ultimately, 2 core genes (MYC and NCAPG2) were identified. The heatmap of core gene expression showed high expression of MYC and NCAPG2 in colorectal cancer tissue samples and low expression in normal tissue samples, while they were core molecules in gastric cancer. Survival analysis indicated that MYC and NCAPG2 were risk factors, showing an upregulation trend with increasing risk scores. CTD analysis revealed associations of MYC and NCAPG2 with colorectal cancer, gastric cancer, inflammation, and immune system diseases. MYC and NCAPG2 are highly expressed in colorectal cancer. The higher the expression of MYC and NCAPG2, the worse the prognosis. MYC and NCAPG2 are core molecules in gastric cancer.

Mining and exploration of rehabilitation nursing targets for colorectal cancer

BACKGROUND

There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer.

METHOD

We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs.

RESULTS

1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis.

CONCLUSIONS

High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

Systematic analysis of the prognostic value and immunological function of LTBR in human cancer

Lymphotoxin beta receptor (LTBR) is a positive T cell proliferation regulator gene. It is closely associated with the tumor immune microenvironment. However, its role in cancer and immunotherapy is unclear. Firstly, the expression level and prognostic value of LTBR were analyzed. Secondly, the expression of LTBR in clinical stages, immune subtypes, and molecular subtypes was analyzed. The correlation between LTBR and immune regulatory genes, immune checkpoint genes, and RNA modification genes was then analyzed. Correlations between LTBR and immune cells, scores, cancer-related functional status, tumor stemness index, mismatch repair (MMR) genes, and DNA methyltransferase were also analyzed. In addition, we analyzed the role of LTBR in DNA methylation, mutational status, tumor mutation burden (TMB), and microsatellite instability (MSI). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the role of LTBR in pan-cancer. Finally, the drugs associated with LTBR were analyzed. The expression of LTBR was confirmed using quantitative real-time PCR and Western blot. LTBR is significantly overexpressed in most cancers and is associated with low patient survival. In addition, LTBR expression was strongly correlated with immune cells, score, cancer-related functional status, tumor stemness index, MMR genes, DNA methyltransferase, DNA methylation, mutational status, TMB, and MSI. Enrichment analysis revealed that LTBR was associated with apoptosis, necroptosis, and immune-related pathways. Finally, multiple drugs targeting LTBR were identified. LTBR is overexpressed in several tumors and is associated with a poor prognosis. It is related to immune-related genes and immune cell infiltration.

Constructing a prognostic model for head and neck squamous cell carcinoma based on glucose metabolism related genes

Background

Glucose metabolism (GM) plays a crucial role in cancer cell proliferation, tumor growth, and survival. However, the identification of glucose metabolism-related genes (GMRGs) for effective prediction of prognosis in head and neck squamous cell carcinoma (HNSC) is still lacking.

Methods

We conducted differential analysis between HNSC and Normal groups to identify differentially expressed genes (DEGs). Key module genes were obtained using weighted gene co-expression network analysis (WGCNA). Intersection analysis of DEGs, GMRGs, and key module genes identified GMRG-DEGs. Univariate and multivariate Cox regression analyses were performed to screen prognostic-associated genes. Independent prognostic analysis of clinical traits and risk scores was implemented using Cox regression. Gene set enrichment analysis (GSEA) was used to explore functional pathways and genes between high- and low-risk groups. Immune infiltration analysis compared immune cells between the two groups in HNSC samples. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Quantitative real-time fluorescence PCR (qRT-PCR) validated the expression levels of prognosis-related genes in HNSC patients.

Results

We identified 4973 DEGs between HNSC and Normal samples. Key gene modules, represented by black and brown module genes, were identified. Intersection analysis revealed 76 GMRG-DEGs. Five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) were identified. A nomogram incorporating age, lymph node status (N), and risk score was constructed for survival prediction in HNSC patients. Immune infiltration analysis showed significant differences in five immune cell types (Macrophages M0, memory B cells, Monocytes, Macrophages M2, and Dendritic resting cells) between the high- and low-risk groups. GDSC database analysis identified 53 drugs with remarkable differences between the groups, including A.443654 and AG.014699. DNMT1 and MTHFD2 were up-regulated, while MPZ was down-regulated in HNSC.

Conclusion

Our study highlights the significant association of five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) with HNSC. These findings provide further evidence of the crucial role of GMRGs in HNSC.

Integrative bioinformatics analysis of WDHD1: a potential biomarker for pan-cancer prognosis, diagnosis, and immunotherapy

BACKGROUND

Although WD repeat and high-mobility group box DNA binding protein 1 (WDHD1) played an essential role in DNA replication, chromosome stability, and DNA damage repair, the panoramic picture of WDHD1 in human tumors remains unclear. Hence, this study aims to comprehensively characterize WDHD1 across 33 human cancers.

METHODS

Based on publicly available databases such as TCGA, GTEx, and HPA, we used a bioinformatics approach to systematically explore the genomic features and biological functions of WDHD1 in pan-cancer.

RESULTS

WDHD1 mRNA levels were significantly increased in more than 20 types of tumor tissues. Elevated WDHD1 expression was associated with significantly shorter overall survival (OS) in 10 tumors. Furthermore, in uterine corpus endometrial carcinoma (UCEC) and liver hepatocellular carcinoma (LIHC), WDHD1 expression was significantly associated with higher histological grades and pathological stages. In addition, WDHD1 had a high diagnostic value among 16 tumors (area under the ROC curve [AUC] > 0.9). Functional enrichment analyses suggested that WDHD1 probably participated in many oncogenic pathways such as E2F and MYC targets (false discovery rate [FDR] < 0.05), and it was involved in the processes of DNA replication and DNA damage repair (p.adjust < 0.05). WDHD1 expression also correlated with the half-maximal inhibitory concentrations (IC50) of rapamycin (4 out of 10 cancers) and paclitaxel (10 out of 10 cancers). Overall, WDHD1 was negatively associated with immune cell infiltration and might promote tumor immune escape. Our analysis of genomic alterations suggested that WDHD1 was altered in 1.5% of pan-cancer cohorts and the "mutation" was the predominant type of alteration. Finally, through correlation analysis, we found that WDHD1 might be closely associated with tumor heterogeneity, tumor stemness, mismatch repair (MMR), and RNA methylation modification, which were all processes associated with the tumor progression.

CONCLUSIONS

Our pan-cancer analysis of WDHD1 provides valuable insights into the genomic characterization and biological functions of WDHD1 in human cancers and offers some theoretical support for the future use of WDHD1-targeted therapies, immunotherapies, and chemotherapeutic combinations for the management of tumors.

Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review

Regulatory T cells (Treg), as members of CD4+ T cells, have garnered extensive attention in the research of tumor progression. Treg cells have the function of inhibiting the immune effector cells, preventing tissue damage, and suppressing inflammation. Under the stimulation of the tumor inflammatory microenvironment (IM), the reprogramming of Treg cells enhances their suppression of immune responses, ultimately promoting tumor immune escape or tumor progression. Reducing the number of Treg cells in the IM or lowering the activity of Treg cells while preventing their reprogramming, can help promote the body's anti-tumor immune responses. This review introduces a reprogramming mechanism of Treg cells in the IM; and discusses the regulation of Treg cells on tumor progression. The control of Treg cells and the response to Treg inflammatory reprogramming in tumor immunotherapy are analyzed and countermeasures are proposed. This work will provide a foundation for downregulating the immunosuppressive role of Treg in the inflammatory environment in future tumor immunotherapy.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

Background

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.

Methods

This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.

Results

The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).

Conclusion

Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.