Scinderin is a potential prognostic biomarker and correlated with immunological regulation: from pan-cancer analysis to liver hepatocellular carcinoma.

Aim: This study aimed to systematically dissect the role of Scinderin (SCIN) in tumorigenesis. Methods: Bioinformatics techniques were employed based on cancer data from TCGA, ENCORI, HPA, GEPIA2, UALCAN, Kaplan-Meier plotter, TIMER, TISIDB, cBioPortal, HCCDB, GeneMANIA and LinkedOmics database. Experiments in vitro and in vivo were conducted to dissect the role of SCIN in liver hepatocellular carcinoma (LIHC). Results: Significantly differential expression of SCIN was found in nine types of cancers, including LIHC. Through pan-cancer analysis, the correlations between SCIN expression with prognosis and immune cell infiltration were proven, especially in LIHC, ovarian serous cystadenocarcinoma and lung adenocarcinoma. The highest frequency of alteration in SCIN (6.81%) was seen in patients with uterine corpus endometrial carcinoma, in which "mutation" was the predominant type, with a frequency of about 5.29%; meanwhile, S673F and S381Y were the two most frequent mutation sites. Furthermore, the abnormal expression of SCIN exhibited a strong relationship with immune cell subtypes, immune checkpoint genes, tumor mutation burden, microsatellite instability, neoantigen, molecular subtypes, mismatch repair signatures and DNA methyl-transferase in different cancer types. Through comparative analysis, we discovered that SCIN was dramatically up-regulated in LIHC, and associated with poor survival. Experiments in vitro and in vivo suggested the knockdown of SCIN could suppress tumor cell proliferation and improve the survival rate partly in animal models. Conclusion: This study reveals SCIN may be a promising biomarker for prognosis and treatment in certain cancers, especially in LIHC.

Pan-cancer analysis of SERPINE1 with a concentration on immune therapeutic and prognostic in gastric cancer.

The serine protease inhibitor clade E member 1 (SERPINE1) is a key modulator of the plasminogen/plasminase system and has been demonstrated to promote tumor progression and metastasis in various tumours. However, although much literature has explored the cancer-promoting mechanism of SERPINE1, the pan-cancer analyses of its predictive value and immune response remain unexplored. The differential expression, and survival analysis of SERPINE1 expression in multiple cancers were analysed using The Cancer Genome Atlas and Genotype-Tissue Expression database. Kaplan-Meier (K-M) plotter and survival data analysis were used to analyze the prognostic value of SERPINE1 expression, including overall survival (OS), disease-specific survival, disease-free interval and progression-free interval and investigated the relationship of SERPINE1 expression with microsatellite instability. We further analysed the correlation between the expression of SERPINE1 and immune infiltration. The Kyoto Encyclopaedia of Genes and Genomes pathway was used for enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) database was used to perform pathway analysis. Finally, in vitro experiments demonstrated that knockdown or overexpression of SERPINE1 could alter the proliferation and migration of gastric cancer (GC) cells. The results indicated that SERPINE1 expression levels different significantly between cancer and normal tissues, meanwhile, it was highly expressed in various cancers. By analysing online data, it has been observed that the gene SERPINE1 exhibits heightened expression levels across a variety of human cancers, significantly impacting patient survival rates. Notably, the presence of SERPINE1 was strongly associated with decrease OS and disease-free survival in individuals diagnosed with GC. Furthermore, an observed link indicates that higher levels of SERPINE expression are associated with increased infiltration of immune cells in GC. Finally, in vitro experiments showed that knockdown or overexpression of SERPINE1 inhibited the growth, and migration, of GC cells. SERPINE1expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in GC. This study shows that SERPINE1 is an oncogene that participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in GC. These findings underscore the importance of further investigating the role of SERPINE1 in cancer progression and offer a promising direction for the development of new therapeutic strategies.

Pan­cancer analysis on the role of KMT2C expression in tumor progression and immunotherapy.

Histone lysine N-methyltransferase 2C (KMT2C) is involved in transcriptional regulation and DNA damage repair. Mutations in KMT2C have been implicated in the progression, metastasis, and drug resistance of multiple cancer types. However, the roles of KMT2C in the regulation of tumor prognosis, immune cell infiltration and the immune microenvironment in these multiple cancer types remain unclear. Therefore, in the present study, data from The Cancer Genome Atlas and Genotype-Tissue Expression databases were used for KMT2C expression analyses. Kaplan-Meier and univariate Cox regression analyses were also performed to investigate the prognostic role of KMT2C. In addition, Gene Set Enrichment Analysis (GSEA) was conducted to study the KMT2C-related signaling pathways. Tumor immune estimation resource 2 and single-sample GSEA were conducted to investigate the correlation between KMT2C expression and immune cell infiltrations, and Spearman's analysis was conducted to study the correlations among KMT2C, tumor mutational burden, microsatellite instability, immune regulators, chemokines and immune receptors. Immunohistochemistry of patient kidney tumor samples was performed to verify the correlation between KMT2C and programmed death-ligand 1 (PD-L1) expression. Finally, RNA interference, wound healing and colony formation assays were conducted to evaluate the effects of KMT2C expression on cell proliferation and metastasis. The results of the present study demonstrated that KMT2C was highly expressed in multiple cancer types, was a protective factor in kidney renal clear cell carcinoma and ovarian serous cystadenocarcinoma, and a risk factor for lung squamous cell carcinoma and uveal melanoma. In addition, KMT2C levels were negatively correlated with immune-activated pathways and the infiltration of immune cells, and positively correlated with inhibitory immune factors and tumor angiogenesis. Patients with low KMT2C expression had higher objective response rates to immunotherapy, and drug sensitivity analysis indicated that topoisomerase, histone deacetylase, DOT1-like histone H3K79 methyltransferase and G9A nuclear histone lysine methyltransferase inhibitors could potentially be used to treat tumors with high KMT2C expression levels. Finally, the KMT2C and PD-L1 expression levels were shown to be positively correlated, and KMT2C knockdown markedly promoted the proliferation and invasion capacities of A549 cells. In conclusion, the present study revealed that low KMT2C expression may be a promising biomarker for predicting the response of patients with cancer to immunotherapy. Conversely, high KMT2C expression was shown to promote tumor angiogenesis, which may contribute to the formation of the immunosuppressive tumor microenvironment.

Comprehensively prognostic and immunological analyses of GLP-1 signaling-related genes in pan-cancer and validation in colorectal cancer.

Background: Glucagon-like peptide-1 (GLP-1) has crucial impact on glycemic control and weight loss physiologically. GLP-1 receptor agonists have been approved for treatment of diabetes and obesity. Emerging evidence suggests that GLP-1 receptor agonists exert anticancer effect in tumorigenesis and development. However, the role and mechanism of GLP-1 signaling-related genes in pan-cancer still need further study. Methods: We comprehensively investigated the aberrant expression and genetic alterations of GLP-1 signaling-related genes in 33 cancer types. Next, GLP-1 signaling score of each patient in The Cancer Genome Atlas were established by the single-sample gene set enrichment analysis. In addition, we explored the association of GLP-1 signaling score with prognostic significance and immune characteristics. Furthermore, qRT-PCR and immunohistochemistry staining were applied to verify the expression profiling of GLP-1 signaling-related genes in colorectal cancer (CRC) tissues. Wound-healing assays and migration assays were carried out to validate the role of GLP-1 receptor agonist in CRC cell lines. Results: The expression profiling of GLP-1 signaling-related genes is commonly altered in pan-cancer. The score was decreased in cancer tissues compared with normal tissues and the lower expression score was associated with worse survival in most of cancer types. Notably, GLP-1 signaling score was strongly correlated with immune cell infiltration, including T cells, neutrophils, dendritic cells and macrophages. In addition, GLP-1 signaling score exhibited close association with tumor mutation burden, microsatellite instability and immunotherapy response in patients with cancer. Moreover, we found that the expression of GLP-1 signaling-related genes ITPR1 and ADCY5 were significantly reduced in CRC tissues, and GLP-1 receptor agonist semaglutide impaired the migration capacity of CRC cells, indicating its protective role. Conclusion: This study provided a preliminary understanding of the GLP-1 signaling-related genes in pan-cancer, showing the prognosis significance and potential immunotherapeutic values in most cancer types, and verified the potential anticancer effect of GLP-1 receptor agonist in CRC.

Pan-cancer analyses reveal the molecular and clinical characteristics of TET family members and suggests that TET3 maybe a potential therapeutic target.

The Ten-Eleven Translocation (TET) family genes are implicated in a wide array of biological functions across various human cancers. Nonetheless, there is a scarcity of studies that comprehensively analyze the correlation between TET family members and the molecular phenotypes and clinical characteristics of different cancers. Leveraging updated public databases and employing several bioinformatics analysis methods, we assessed the expression levels, somatic variations, methylation levels, and prognostic values of TET family genes. Additionally, we explored the association between the expression of TET family genes and pathway activity, tumor microenvironment (TME), stemness score, immune subtype, clinical staging, and drug sensitivity in pan-cancer. Molecular biology and cytology experiments were conducted to validate the potential role of TET3 in tumor progression. Each TET family gene displayed distinct expression patterns across at least ten detected tumors. The frequency of Single Nucleotide Variant (SNV) in TET genes was found to be 91.24%, primarily comprising missense mutation types, with the main types of copy number variant (CNV) being heterozygous amplifications and deletions. TET1 gene exhibited high methylation levels, whereas TET2 and TET3 genes displayed hypomethylation in most cancers, which correlated closely with patient prognosis. Pathway activity analysis revealed the involvement of TET family genes in multiple signaling pathways, including cell cycle, apoptosis, DNA damage response, hormone AR, PI3K/AKT, and RTK. Furthermore, the expression levels of TET family genes were shown to impact the clinical staging of tumor patients, modulate the sensitivity of chemotherapy drugs, and thereby influence patient prognosis by participating in the regulation of the tumor microenvironment, cellular stemness potential, and immune subtype. Notably, TET3 was identified to promote cancer progression across various tumors, and its silencing was found to inhibit tumor malignancy and enhance chemotherapy sensitivity. These findings shed light on the role of TET family genes in cancer progression and offer insights for further research on TET3 as a potential therapeutic target for pan-cancer.

The role of cuproptosis-related genes in pan-cancer and the development of cuproptosis-related risk model in colon adenocarcinoma.

Cancer is widely regarded as a leading cause of death in humans, with colon adenocarcinoma (COAD) ranking among the most prevalent types. Cuproptosis is a novel form of cell death mediated by protein lipoylation. Cuproptosis-related genes (CRGs) participate in tumourigenesis and development. Their role in pan-cancer and COAD require further investigation. This study comprehensively evaluated the relationship among CRGs, pan-cancer, and COAD. Our research revealed the differential expression of CRGs and the cuproptosis potential index (CPI) between normal and tumour tissues, and further explored the correlation of CRGs or CPI with prognosis, immune infiltration, tumor mutant burden(TMB), microsatellite instability (MSI), and drug sensitivity in pan-cancer. Gene set enrichment analysis (GSEA) revealed that oxidative phosphorylation and fatty acid metabolism pathways were significantly enriched in the high CPI group of most tumours. FDX1 and CDKN2A were chosen for further exploration, and we found an independent association between FDX1 and CDKN2A and prognosis, immune infiltration, TMB, and MSI in pan-cancer. Furthermore, a prognostic risk model based on the association between CRGs and COAD was built, and the correlations between the risk score and prognosis, immune-related characteristics, and drug sensitivity were analysed. COAD was then divided into three subtypes using cluster analysis, and the differences among the subtypes in prognosis, CPI, immune-related characteristics, and drug sensitivity were determined. Due to the level of LIPT1 was notably positive related with the risk score, the cytological identification was carried out to identify the association of LIPT1 with proliferation and migration of colon cancer cells. In summary, CRGs can be used as potential prognostic biomarkers to predict immune infiltration levels in patients with pan-cancer. In addition, the risk model could more accurately predict the prognosis and immune infiltration levels of COAD and better guide the direction of clinical medication. Thus, FDX1, CDKN2A, and LIPT1 may serve as prospective new targets for cancer therapy.

RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance.

Background: RAB42 (Ras-related protein 42) is a new small GTPase that controls the vesicular trafficking from endosomes to trans-Golgi network in mammalian cells. However, the role of RAB42 in multiple cancers, especially in liver hepatocellular carcinoma (LIHC), has not been well investigated. Methods: A variety of cancer-related databases and online tools, including TCGA, GTEx, TARGET, QUANTISEQ, EPIC, RNAactDrug, CTR-DB, TIMER algorithms and Sangerbox, were applied to explore the correlation of RAB42 expression with prognosis, immune microenvironment, immune regulatory network, RNA modification, pathway activation and drug sensitivity in pan-cancer. The prognostic, immunomodulatory and tumor-promoting effects of RAB42 were verified in various malignancies and determined by a series of in vitro cellular experiments. Results: RAB42 is significantly overexpressed in most cancers with advanced pathological stages. Its overexpression is correlated with poor survival in pan-cancer. RAB42 overexpression has a high diagnostic accuracy of various cancers (AUC > 0.80). RAB42 overexpression not only correlates with distinct stromal immune infiltration and level of immune checkpoint molecules, but also associates with weak immune cell infiltration, immunomodulatory genes expression, and immunotherapeutic response to immune checkpoint inhibitors (ICIs). Additionally, RAB42 overexpression correlates with enhanced expression of m6A RNA methylation-related genes (MRGs) and its interactors. Moreover, overexpression of RAB42 serves as a drug-resistant marker to certain chemotherapies and acts as a potential biomarker for LIHC. Notably, RAB42 overexpression or activation promotes the cellular proliferation, migration and invasion of LIHC. Conclusion: Overexpressed RAB42 serves as a potential prognostic biomarker and therapeutic target in pan-cancer, especially in LIHC.

Multidimensional analysis of TMEM132A in pan-cancer: unveiling its potential as a biomarker for treatment response prediction.

Background: TMEM132A is a transmembrane protein that regulates gastric cancer cell malignancy and overall survival in bladder cancer patients. However, while some studies have investigated the involvement of TMEM132A in specific cancers, further systematic studies are required to elucidate its specific mechanisms of action in different cancer types. Methods: We investigated the pan-cancer role of TMEM132A using several databases. We analyzed TMEM132A expression and its correlation with clinical survival, immune checkpoints, tumor stemness score, prognostic value, immunomodulators, genomic profiles, immunological characteristics, immunotherapy and functional enrichment. Results: First, it was observed that TMEM132A expression levels were higher in the majority of tumors compared to non-tumor tissues. In addition, high TMEM132A expression may have a higher prognostic value in some cancers. Furthermore, TMEM132A was significantly associated with immune checkpoints, immunomodulators, prognosis, immunomodulatory genes, tumor stemness score, cell function status and immune infiltration in most tumors. Further analysis of TMEM132A-related gene enrichment, mutation sites and types, RNA modification and genomic heterogeneity showed that the major mutations of TMEM132A were missense mutations and that TMEM132A plays a very important role in UCEC, LUAD and LIHC. Finally, these results suggest that high TMEM132A expression may be associated with a better response to specific immunotherapies. Conclusion: This comprehensive study uncovers an important function for TMEM132A in different types of cancer. It also has the potential to identify TMEM132A as a potential biomarker for predicting treatment response. This may help us to better understand how TMEM132A plays a role in cancer and provide valuable insights for developing personalised treatments.

Exploring the role of cellular senescence in cancer prognosis across multiple tumor types.

Background: Cellular senescence is a common biological process with a well-established link to cancer. However, the impact of cellular senescence on tumor progression remains unclear. To investigate this relationship, we utilized transcriptomic data from a senescence gene set to explore the connection between senescence and cancer prognosis. Methods: We developed the senescence score by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. We obtained transcriptomic information of the senescence gene set from The Cancer Genome Atlas (TCGA) program. Additionally, we created a nomogram that integrates these senescence scores with clinical characteristics, providing a more comprehensive tool for prognosis evaluation. Results: We calculated the senescence score based on the expression level of 42 senescence-related genes. We established the nomogram based on the senescence score and clinical characteristics. The senescence score showed a positive correlation with epithelial-to-mesenchymal transition, cell cycle, and glycolysis, and a negative correlation with autophagy. Furthermore, we carried out Gene Ontology (GO) analysis to explore the signaling pathways and biological process in different senescence score groups. Conclusions: The senescence score, a novel tool constructed in this study, shows promise in predicting survival outcomes across various cancer types. These findings not only highlight the complex interplay between senescence and cancer but also indicate that cellular senescence might serve as a biomarker for tumor prognosis.

Rheumatoid arthritis reduces the risk of colorectal cancer through immune inflammation mediation.

There is a close relationship between immune-mediated inflammation and cancer, and there is still controversy over whether rheumatoid arthritis (RA) increases the risk of malignancy. We first used Mendelian randomization (MR) analysis to explore the potential causal relationship between RA and pan-cancer. And verify the effect of immune-mediated inflammation on cancer through intermediate MR analysis. Then we extracted the standardized incidence rate of malignancy in RA patients relative to the general population through large-scale meta-analysis. Finally, we performed pan-cancer analysis on the RA related genes obtained from MR analysis. And perform immune related analysis on key genes to reveal the association between RA and malignancy. The MR analysis demonstrated a negative correlation between RA and pan-cancer (p = 0.008). Autoimmune traits were the main mediating variable for the causal relationship between RA and pan-cancer. Based on the results of the meta-analysis, we validated that RA reduces the risk of developing colorectal cancer (SIR = 0.69, 95% CI 0.53-0.85). Pan-cancer analysis also showed that high expression of RA related genes was negatively correlated with colon adenocarcinoma. IL6R was the gene with the highest correlation among them, and its correlation with immune cells was higher in colorectal cancer than in other malignancy. Our MR study provides evidence that RA was associated with reduced risk of colorectal cancer. This effect is caused by immune-mediated inflammation, with IL6R being a key regulatory gene.

CDK1 Acts as a Prognostic Biomarker Associated with Immune Infiltration in Pan-Cancer, Especially in Gastrointestinal Tumors.

OBJECTIVE: Cyclin-dependent kinase 1 (CDK1) regulates the cell cycle and is highly expressed in most tumors. CDK1 expression has been associated with poor disease prognosis. This study aimed to identify the prognostic value of CDK1 in pan-cancer and investigate the association between CDK1 expression and immune cell infiltration. METHODS: CDK1 expression and its correlation with prognosis in pan-cancer were analyzed using online databases. Immune infiltration was assessed by ESTIMATE and CIBERSORT algorithms. We then evaluated the relationship between CDK1 expression and tumor mutational burden (TMB), microsatellite instability (MSI), or tumor-infiltrating immune cells. In addition, we performed the co-expression analysis of immune-related genes and GO analysis with CDK1 expression in pan-cancer. Finally, we compared the CDK1 expression profile with the immune-related genes in 30 pairs of clinical gastrointestinal tumor samples. RESULTS: Our analysis demonstrated overexpression of CDK1 in most tumor tissues, especially in gastrointestinal tumors. The high expression of CDK1 was associated with poor overall survival, disease-specific survival, disease-free interval, and progression-free interval in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), and sarcoma (SARC). Besides, CDK1 expression was significantly associated with TMB in 22 cancer types and MSI in 8 cancer types as well as greater frequencies of MSI-high (MSI-H) status and high tumor mutational burden (TMB-H) in uterine corpus endometrial carcinoma (UCEC), stomach adenocarcinoma (STAD), sarcoma (SARC), rectum adenocarcinoma (READ), mesothelioma (MESO), head and neck squamous cell carcinoma (HNSC), and colon adenocarcinoma (COAD). In addition, CDK1 expression correlated with immune cell infiltrating levels, such as M0, M1, or M2 macrophages, memory CD4 T cells, T follicular helper cells, and naive B cells. Our data showed that CDK1 was remarkably correlated with 47 immune-related and immune checkpoint genes in many cancer types. Furthermore, CDK1 was up-regulated in gastrointestinal tumor samples, especially in gastric cancer and intestinal cancer. CDK1 was positively correlated with IDO1 in gastric cancer and PD-1 in intestinal cancer. CONCLUSION: Taken together, our data demonstrated the roles of CDK1 in oncogenesis and metastasis in pan-cancer. Thus, CDK1 is a potential prognostic biomarker and a target for tumor immunotherapy.

A pan-cancer analysis unveiling the function of NR4A family genes in tumor immune microenvironment, prognosis, and drug response.

BACKGROUND: NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. OBJECTIVE: We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. METHODS: We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. RESULTS: Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-ß dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. CONCLUSION: The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.

Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection.

BACKGROUND: Cancer is a major public health concern and the second leading cause of death worldwide. Various studies have reported the use of serum microRNAs (miRNAs) as non-invasive biomarkers for cancer detection. However, large-scale pan-cancer studies based on serum miRNAs have been relatively scarce. METHODS: An optimized machine learning workflow, combining least absolute shrinkage and selection operator (LASSO) analyses, recursive feature elimination (RFE), and fourteen kinds of machine learning algorithms, was use to screen out candidate miRNAs from 2540 serum miRNAs and constructed a potent diagnostic signature (Cancer-related Serum miRNA Signatures) for pan-cancer detection, based on a serum miRNA expression dataset of 38,223 samples. RESULT: Cancer-related Serum miRNA Signatures performed well in pan-cancer detection with an area under curve (AUC) of 0.999, 94.51% sensitivity, and 99.49% specificity in the external validation cohort, and represented an acceptable diagnostic performance for identifying early-stage tumors. Furthermore, the ability of multi-classification of tumors by serum miRNAs in pancreatic, colorectal, and biliary tract cancers was lower than that in other cancers, which showed accuracies of 59%, 58.5%, and 28.9%, respectively, indicating that the difference in serum miRNA expression profiles among a small number of tumor subtypes was not as significant as that between cancer samples and non-cancer controls. CONCLUSION: We have developed a serum miRNA signature using machine learning that may be a cost-effective risk tool for pan-cancer detection. Our findings will benefit not only the predictive diagnosis of cancer but also a preventive and more personalized screening plan.

Pan-Cancer Bioinformatics Indicates ZNF207 is a Promising Prognostic Biomarker and Immunotherapeutic Target.

In the era of personalized cancer treatment, understanding the complexities of tumor biology and immune modulation is paramount. This comprehensive analysis delves into the multifaceted role of Zinc Finger Protein 207 (ZNF207) in pan-cancer, shedding light on its involvement in tumorigenesis, immune evasion, and therapeutic implications. Through integrated genomic and clinical data analysis, we reveal consistent upregulation of ZNF207 across diverse cancer types, highlighting its potential as a prognostic marker and therapeutic target, particularly for liver cancers. Notably, ZNF207 demonstrates intricate associations with clinical-pathological features, immune subtypes, and molecular pathways, indicating its pervasive influence in cancer biology. Furthermore, our study uncovers ZNF207's involvement in immune escape mechanisms, suggesting its potential as a modulator of immune responses within the tumor microenvironment. These findings underscore the significance of ZNF207 in shaping cancer progression and immune landscape, presenting promising avenues for targeted therapy and immunomodulation. Recognizing ZNF207's multifaceted contributions to cancer progression and immune evasion suggests its central role in understanding tumor immunology, beyond mere therapeutic targeting. Nevertheless, further mechanistic studies are imperative to elucidate ZNF207's precise molecular mechanisms and therapeutic implications in cancer treatment. This study primarily utilized various bioinformatics tools such as TIMER 2.0, cProSite, UALCAN, SangerBox, GEPIA2, TISIDB and TIDE to analyze the expression of ZNF207 in multiple cancer samples from the TCGA database.

Identification and Validation of JAM-A as a Novel Prognostic and Immune Factor in Human Tumors.

Junctional adhesion molecule-A (JAM-A), also known as F11 receptor (F11R), is a transmembrane glycoprotein that is involved in various biological processes, including cancer initiation and progression. However, the functional characteristics and significance of JAM-A in pan-cancer remain unexplored. In this study, we used multiple databases to gain a comprehensive understanding of JAM-A in human cancers. JAM-A was widely expressed in various tissues, mainly located on the microtubules and cell junctions. Aberrant expression of JAM-A was detected in multiple cancers at both mRNA and protein levels, which can be correlated with poorer prognosis and may be attributed to genetic alterations and down-regulated DNA methylation. JAM-A expression was also associated with immune infiltration and may affect immunotherapy responses in several cancers. Functional enrichment analysis indicated that JAM-A participated in tight junction and cancer-related pathways. In vitro experiments verified that JAM-A knockdown suppressed the proliferation and migration abilities of breast cancer cells and liver cancer cells. Overall, our study suggests that JAM-A is a pan-cancer regulator and a potential biomarker for predicting prognosis and immune-therapeutic responses for different tumors.

A Multi-Omics Analysis of an Exhausted T Cells' Molecular Signature in Pan-Cancer.

T cells are essential tumor suppressors in cancer immunology, but their dysfunction induced by cancer cells can result in T cell exhaustion. Exhausted T cells (Tex) significantly influence the tumor immune environment, and thus, there is a need for their thorough investigation across different types of cancer. Here, we address the role of Tex cells in pan-cancer, focusing on the expression, mutations, methylation, immune infiltration, and drug sensitivity of a molecular signature comprising of the genes HAVCR2, CXCL13, LAG3, LAYN, TIGIT, and PDCD1across multiple cancer types, using bioinformatics analysis of TCGA data. Our analysis revealed that the Tex signature genes are differentially expressed across 14 cancer types, being correlated with patient survival outcomes, with distinct survival trends. Pathway analysis indicated that the Tex genes influence key cancer-related pathways, such as apoptosis, EMT, and DNA damage pathways. Immune infiltration analysis highlighted a positive correlation between Tex gene expression and immune cell infiltration in bladder cancer, while mutations in these genes were associated with specific immune cell enrichments in UCEC and SKCM. CNVs in Tex genes were widespread across cancers. We also highlight high LAYN methylation in most tumors and a negative correlation between methylation levels and immune cell infiltration in various cancers. Drug sensitivity analysis identified numerous correlations, with CXCL13 and HAVCR2 expressions influencing sensitivity to several drugs, including Apitolisib, Belinostat, and Docetaxel. Overall, these findings highlight the importance of reviving exhausted T cells to enhance the treatment efficacy to significantly boost anti-tumor immunity and achieve better clinical outcomes.

Comprehensive investigation in oncogenic functions and immunological roles of NCBP2 and its validation in prostate cancer.

BACKGROUND: Nuclear cap-binding protein 2 (NCBP2), as the component of the cap-binding complex, participates in a number of biological processes, including pre-mRNA splicing, transcript export, translation regulation and other gene expression steps. However, the role of NCBP2 on the tumor cells and immune microenvironment remains unclear. To systematically analyze and validate functions of NCBP2, we performed a pan-cancer analysis using multiple approaches. METHODS: The data in this study were derived from sequencing, mutation, and methylation data in the TCGA cohort, normal sample sequencing data in the GTEx project, and cell line expression profile data in the CCLE database. RESULTS: Survival analyses including the Cox proportional-hazards model and log-rank test revealed the poor prognostic role of NCBP2 in multiple tumors. We further validated the oncogenic ability of NCBP2 in prostate cancer cell lines, organoids and tumor-bearing mice. A negative correlation was observed between NCBP2 expression and immune score by the ESTIMATE algorithm. Simultaneously, the NCBP2-induced immunosuppressive microenvironment might be related to the decline in CD8+T cells and the increase in regulatory T cells and neutrophils, examined by flow cytometry experiments for NCBP2 overexpressed tumor-bearing mice. CONCLUSION: This research offered strong proof supporting NCBP2 as the prognostic marker and the therapeutic target in the future.

[Exploration of cancer biological characteristics in people with different five movements and six climates constitution based on pan-cancer Bulk RNA-Seq].

According to the theory of five movements and six climates, the innate constitution plays a crucial role in determining the underlyingpa thological mechanisms of diseases later in life. Previous studies have demonstrated a close association between the constitution, as defined by the theory of five movements and six climates, and the development of various types of tumors. Furt hermore,the tumorsubtype determined by the constitution has prognostic implications. This highlights the potential of utilizing the fivemovements and six climates theory to guide the implementation of precision medicine strategies in thefield of oncology. However, no resear ch has yet been conducted to investigate the use of this theory in guiding the development of tumor molecular classification and precisi onmedicine strategies. The objective of this research is to uncover the biological characteristics of each constitution within a pancanc ercohort and identify potential anti-tumor drugs that are applicable to patients with different constitutional types. By doing so, we aimto c ontribute to the establishment of a precision medicine strategy for tumors derived from the original concepts of traditional Chi nesemedicine(TCM). In this study, we obtainedpan-cancer Bulk RNA-Seq data from UCSC Xena, GWAS cohort data from the UKBiobank, and cis-eQTLs data from eQ TLGen and GTEx V8. We employed machine learning methods to screen for hub genes associated with each constitution. Subsequently, we utilized informatics tools to explore the biological characteristics of each constitut iondefined by the theory of five movements and six bioclimates. Further, potential anti-tumor drugs suitable for patients with differen tconstitutional types were identified through mendelian randomization, molecular docking, and drug-like prediction techniques. Withinthe pan-cancer cohort, significant differences were observed among different constitutions in terms of progression-free interval, biological f unctions, immune cell abundance, tumor drug sensitivity, and immunotherapy response. These findings suggest that the five movements and six climates theory can guide tumor molecular classification and the development of precision medicine strategies. Moreover,the biological characteristics inherent to each constitution partially shed light on the scientific implications of Chinese medicinetheories, offering a fresh perspective towards clinical cancer treatment. Through molecular docking and drug-like prediction, several po tential anti-tumor drugs such as 17-beta-estradiol, serotonin, trans-resveratrol, and linoleic acid were identified. Overall, the util izationof multi-omics approaches pro vides a powerful tool to unravel the scientific foundations of TCM theories. The elucidation of themu lti-omics features associated witheach constitution in tumors serves as the basis for applying the five movements and six climates theoryto tumor molecular classification and the development of precision medicine strategies.

An integrated pan-cancer assessment of prognosis, immune infiltration, and immunotherapy response for B7 family using multi-omics data.

AIMS: B7 molecules (B7s) are crucial synergistic signals for effective immune surveillance against tumor cells. While previous studies have explored the association between the B7 family and cancer, most have been limited to specific genes or cancer subtypes. MAIN METHODS: Our study utilized multi-omics data to investigate potential correlations between B7s expression (B7s exp.) and prognosis, clinicopathological features, somatic mutations (SMs), copy number variations (CNVs), immune characteristics, tumor microenvironment (TME), microsatellite instability, tumor mutation burden, immune checkpoint gene (ICG), and drug responsiveness in TCGA tumors. Furthermore, the connection between B7s exp. and immunotherapy (IT) performance assessed in various validated datasets. Following this, immune infiltration analysis (IIA) was conducted based on B7s exp., CNVs, or SMs in bladder cancer (BLCA), complemented by real-time PCR (RT-PCR) and protein confirmation of B7-H3. KEY FINDINGS: Across most cancer types, B7s exp. was related to prognosis, clinicopathological characteristics, mutations, CNVs, ICG, TMB, TME. The examination of sensitivity to anticancer drugs unveiled correlations between B7 molecules and different drug sensitivities. Specific B7s exp. patterns were linked to the clinical effectiveness of IT. Using GSEA, several enriched immune-related functions and pathways were identified. Particularly in BLCA, IIA revealed significant connections between B7 CNVs, mutation status, and various immune cell infiltrates. RT-PCR confirmed elevated B7-H3 gene levels in BLCA tumor tissues. SIGNIFICANCE: This study confirmed the significance of B7s exp. and genomic changes in predicting outcomes and treatment across different cancer types. Moreover, they indicate a critical function of B7s in BLCA and their potential as IT biomarkers.

Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters.

Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8-12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.