Bioinformatics for Cancer Immunotherapy

Cross-disease drug discovery based on bioinformatics and virtual screening: Study of key genes in Alzheimer's disease and ovarian cancer

BACKGROUND

Alzheimer's disease (AD) and cancer, both age-related diseases, are characterized by abnormal cellular behavior. Epidemiological data indicate an inverse relationship between AD and various cancers. Accordingly, this study seeks to analyze the negatively correlated genes between AD and ovarian cancer and identify closely related compounds through virtual screening technology to explore potential therapeutic drugs.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus database, and negatively correlated genes between AD and ovarian cancer were identified using bioinformatics analysis. Clinical prognostic and survival analyses were performed to identify genes most negatively associated with these diseases. The top ten compounds with the strongest binding to the target genes were screened from the ChemDiv database using virtual screening technology, considering the blood-brain barrier. Molecular dynamics simulations were used to identify potential sites for the binding of these compounds to the target protein MX1. Additionally, point mutation analysis of the target protein was performed. Finally, the binding site was verified in vitro.

RESULTS

The MX1 gene was most significantly negatively associated with AD and ovarian cancer. Molecular dynamics simulations revealed intersection sites at Glu-227 and Gly-188, where MX1 binds tightly to the head compound.

CONCLUSION

This study successfully identified MX1 as being negatively associated with AD and ovarian cancer and assessed the potential drug compounds that bind most closely to it. Our findings provide important rationale and candidate targets for the development of novel therapeutic strategies for AD and ovarian cancer.

Prospects and challenges of neoantigen applications in oncology

Neoantigen, unique peptides resulting from tumor-specific mutations, represent a promising frontier in oncology for personalized cancer immunotherapy. Their unique features allow for the development of highly specific and effective cancer treatments, which can potentially overcome the limitations of conventional therapies. This paper explores the current prospects and challenges associated with the application of neoantigens in oncology. We examine the latest advances in neoantigen identification, vaccine development, and adoptive T cell therapy. Additionally, we discuss the obstacles related to neoantigen heterogeneity, immunogenicity prediction, and the tumor microenvironment. Through a comprehensive analysis of current research and clinical trials, this paper aims to provide a detailed overview of how neoantigens could revolutionize cancer treatment and the hurdles that must be overcome to realize their full potential.

Similarity-based metric analysis approach for predicting osteogenic differentiation correlation coefficients and discovering the novel osteogenic-related gene FOXA1 in BMSCs

Background

As a powerful tool, bioinformatics analysis is playing an increasingly important role in many fields. Osteogenic differentiation is a complex biological process involving the fine regulation of numerous genes and signaling pathways.

Method

Osteogenic differentiation-related genes are collected from the online databases. Then, we proposed two indexes Jaccard similarity and Sorensen-Dice similarity to measure the topological relevance of genes in the human PPI network. Furthermore, we selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation. Subsequently, we performed functional a enrichment analysis of these top-ranked genes to check whether these candidate genes identified by similarity-based metrics are enriched in some specific biological functions and states. we performed a permutation test to investigate the similarity score with four well-known osteogenic differentiation-related pathways including hedgehog signaling pathway, BMP signaling, ERK pathway, and Wnt signaling pathway to check whether these osteogenic differentiation-related pathways can be regulated by FOXA1. Lentiviral transfection was used to knockdown and overexpress gene FOXA1 in human bone mesenchymal stem cells (hBMSCs). Alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were employed to investigate osteogenic differentiation of hBMSCs.

Result

After data collection, human PPI network involving 19,344 genes is included in our analysis. After simplifying, we used Jaccard and Sorensen-Dice similarity to identify osteogenic differentiation-related genes and integrated into a final similarity matrix. Furthermore, we calculated the sum of similarity scores with these osteogenic differentiation-related genes for each gene and found 337 osteogenic differentiation-related genes are involved in our analysis. We selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation and performed functional enrichment analysis of these top-ranked 50 genes. The results collectively demonstrate that these candidate genes can indeed capture osteogenic differentiation-related features of hBSMCs. According to the novel analyzing method, we found that these four pathways have significantly higher similarity with FOXA1 than random noise. Moreover, knockdown FOXA1 significantly increased the ALP activity and mineral deposits. Furthermore, overexpression of FOXA1 dramatically decreased the ALP activity and mineral deposits.

Conclusion

In summary, this study showed that FOXA1 is a novel significant osteogenic differentiation-related transcription factor. Moreover, our study has tightly integrated bioinformatics analysis with biological knowledge, and developed a novel method for analyzing the osteogenic differentiation regulatory network.

Exploration of biomarkers for nursing physical examination early screening of multiple tumors

Nursing and physical examination early screening of multiple tumors is helpful to find tumors early, so as to improve the cure rate. Studying its molecular mechanisms is urgent. By logging into gene expression omnibus database, we found laryngeal cancer dataset GSE127165, bladder cancer dataset GSE65635, oral cancer dataset GSE146483, obtain differentially expressed genes, subsequently, weighted gene co-expression network analysis, protein-protein interaction networks, functional enrichment analysis, immune infiltration analysis, survival analysis, comparative toxicogenomics database analysis were conducted. Draw a heatmap of gene expression. Use targetScan to search for miRNA information about core DEG. Got 53 differentially expressed genes. In GOKEGG analysis, they were clustered in cell cycle processes, spindle poles, and protein serine/threonine/tyrosine kinase activity cell cycle, transcriptional dysregulation in cancer, RIG-I-like receptor signaling pathway, P53 signaling pathway. Protein-protein interaction analysis screened out 5 genes (NEK2, BUB1, HMMR, TTK, CCNB2). Cyclin B2 (CCNB2) and budding uninhibited by benzimidazole 1 (BUB1) were highly expressed in laryngeal cancer, bladder cancer, oral cancer. Comparative toxicogenomics database analysis found that core genes (CCNB2, BUB1) are associated with tumors, necrosis, and inflammation. Related miRNA of CCNB2 gene is hsa-miR-670-3p; related miRNAs of BUB1 gene are hsa-miR-5688, hsa-miR-495-3p. CCNB2 and BUB1 exhibit high expression in laryngeal cancer, bladder cancer, and oral cancer, suggesting their potential as molecular targets for precision therapy in these cancers.

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer

Gastric cancer (GC), the third most lethal cancer worldwide, is often diagnosed at an advanced stage, leaving limited therapeutic options. Given the diverse outcomes among GC patients with similar AJCC/UICC-TNM characteristics, there is a pressing need for more reliable prognostic tools. Recent advances in targeted therapy and immunotherapy have underscored this necessity. In this context, our study focused on a novel stress response state of T cells, termed TSTR, identified across multiple cancers, which is associated with resistance to immunotherapy. We aimed to develop a predictive gene signature for the TSTR phenotype within the tumor microenvironment (TME) of GC patients. By categorizing GC patients into high and low TSTR groups based on the infiltration states of TME TSTR cells, we observed significant differences in clinical prognosis and characteristics between the groups. Through a multi-step bioinformatics approach, we established an eight-gene signature based on genes differentially expressed between these groups. We conducted functional validations for the signature gene PDGFRL in GC cells. This gene signature effectively stratifies GC patients into high and low-risk categories, demonstrating robustness in predicting clinical outcomes. Furthermore, these risk groups exhibited distinct immune profiles, somatic mutations, and drug susceptibilities, highlighting the potential of our gene signature to enhance personalized treatment strategies in clinical practice.

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

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.

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

CDK1 and CCNA2 play important roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in oral cavity and is dominated by squamous cells. The relationship between CDK1, CCNA2, and OSCC is still unclear. The OSCC datasets GSE74530 and GSE85195 configuration files were downloaded from the Gene Expression Omnibus (GEO) database and were derived from platforms GPL570 and GPL6480. Differentially expressed genes (DEGs) were screened. The weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEGs. A total of 1756 DEGs were identified. According to Gene Ontology (GO) analysis, they were predominantly enriched in processes related to organic acid catabolic metabolism, centromeric, and chromosomal region condensation, and oxidoreductase activity. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DEGs were mainly concentrated in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. Weighted gene co-expression network analysis was performed with a soft-thresholding power set at 9, leading to the identification of 6 core genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1). The gene expression heatmap revealed that core genes (CDK1, CCNA2) were highly expressed in OSCC samples. Comparative Toxicogenomics Database analysis demonstrated associations between the 6 genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1) and oral tumors, precancerous lesions, inflammation, immune system disorders, and tongue tumors. The associated miRNAs for CDK1 gene were hsa-miR-203a-3p.2, while for CCNA2 gene, they were hsa-miR-6766-3p, hsa-miR-4782-3p, and hsa-miR-219a-5p. CDK1 and CCNA2 are highly expressed in OSCC. The higher the expression of CDK1 and CCNA2, the worse the prognosis.

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.

Nanomaterial Delivery Vehicles for the Development of Neoantigen Tumor Vaccines for Personalized Treatment

Tumor vaccines have been considered a promising therapeutic approach for treating cancer in recent years. With the development of sequencing technologies, tumor vaccines based on neoantigens or genomes specifically expressed in tumor cells, mainly in the form of peptides, nucleic acids, and dendritic cells, are beginning to receive widespread attention. Therefore, in this review, we have introduced different forms of neoantigen vaccines and discussed the development of these vaccines in treating cancer. Furthermore, neoantigen vaccines are influenced by factors such as antigen stability, weak immunogenicity, and biosafety in addition to sequencing technology. Hence, the biological nanomaterials, polymeric nanomaterials, inorganic nanomaterials, etc., used as vaccine carriers are principally summarized here, which may contribute to the design of neoantigen vaccines for improved stability and better efficacy.

GLIPR2: a potential biomarker and therapeutic target unveiled - Insights from extensive pan-cancer analyses, with a spotlight on lung adenocarcinoma

Background

Glioma pathogenesis related-2 (GLIPR2), an emerging Golgi membrane protein implicated in autophagy, has received limited attention in current scholarly discourse.

Methods

Leveraging extensive datasets, including The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Human Protein Atlas (HPA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC), we conducted a comprehensive investigation into GLIPR2 expression across diverse human malignancies. Utilizing UALCAN, OncoDB, MEXPRESS and cBioPortal databases, we scrutinized GLIPR2 mutation patterns and methylation landscapes. The integration of bulk and single-cell RNA sequencing facilitated elucidation of relationships among cellular heterogeneity, immune infiltration, and GLIPR2 levels in pan-cancer. Employing ROC and KM analyses, we unveiled the diagnostic and prognostic potential of GLIPR2 across diverse cancers. Immunohistochemistry provided insights into GLIPR2 expression patterns in a multicenter cohort spanning various cancer types. In vitro functional experiments, including transwell assays, wound healing analyses, and drug sensitivity testing, were employed to delineate the tumor suppressive role of GLIPR2.

Results

GLIPR2 expression was significantly reduced in neoplastic tissues compared to its prevalence in healthy tissues. Copy number variations (CNV) and alterations in methylation patterns exhibited discernible correlations with GLIPR2 expression within tumor tissues. Moreover, GLIPR2 demonstrated diagnostic and prognostic implications, showing pronounced associations with the expression profiles of numerous immune checkpoint genes and the relative abundance of immune cells in the neoplastic microenvironment. This multifaceted influence was evident across various cancer types, with lung adenocarcinoma (LUAD) being particularly prominent. Notably, patients with LUAD exhibited a significant decrease in GLIPR2 expression within practical clinical settings. Elevated GLIPR2 expression correlated with improved prognostic outcomes specifically in LUAD. Following radiotherapy, LUAD cases displayed an increased presence of GLIPR2+ infiltrating cellular constituents, indicating a notable correlation with heightened sensitivity to radiation-induced therapeutic modalities. A battery of experiments validated the functional role of GLIPR2 in suppressing the malignant phenotype and enhancing treatment sensitivity.

Conclusion

In pan-cancer, particularly in LUAD, GLIPR2 emerges as a promising novel biomarker and tumor suppressor. Its involvement in immune cell infiltration suggests potential as an immunotherapeutic target.

Complementary biomarkers of computed tomography for diagnostic grading of gastric cancer: DSCC1 and GINS1

OBJECTIVE

Computed tomography (CT) is an important tool for grading gastric cancer. Gastric cancer typically originates from epithelial cells of gastric mucosa. However, complementary markers for gastric cancer, relationship between DSCC1, GINS1 and gastric cancer remain unclear.

METHODS

Gastric cancer data were obtained from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified, weighted gene co-expression network analysis (WGCNA) was conducted. Protein-protein interaction (PPI) network was constructed and analyzed. Functional enrichment analysis, gene set enrichment analysis (GSEA), gene expression heatmaps, immune infiltration analysis were performed. The most relevant diseases related to core genes were identified using Comparative Toxicogenomics Database (CTD). TargetScan was used to screen miRNAs. Validation was carried out using Western blotting (WB) and reverse transcription-polymerase chain reaction (RT-PCR).

RESULTS

1243 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses revealed significant enrichment in cell cycle regulation, macrophage migration control, basement membrane, extracellular regions, growth factor binding, protein complex binding, P53 signaling pathway, protein digestion and absorption, metabolic pathways. Immune infiltration analysis indicated that high expression of activated Mast cells and Neutrophils, with a strong positive correlation between them, may influence progression of gastric cancer. CTD analysis revealed associations between DSCC1, GINS1 and gastric tumors, gastrointestinal diseases, tumors, gastritis, inflammation, necrosis. WB and RT-PCR results demonstrated high expression of DSCC1 and GINS1 in gastric cancer.

CONCLUSION

The expressions of DSCC1 and GINS1 are up-regulated in gastric cancer, which can be used as supplementary markers for CT diagnostic grading of gastric cancer.

Bioinformatics and Experimental Validation for Identifying Biomarkers Associated with AMG510 (Sotorasib) Resistance in KRASG12C-Mutated Lung Adenocarcinoma

The Kirsten rat sarcoma viral oncogene homolog (KRAS)G12C mutation is prevalent in lung adenocarcinoma (LUAD), driving tumor progression and indicating a poor prognosis. While the FDA-approved AMG510 (Sotorasib) initially demonstrated efficacy in treating KRASG12C-mutated LUAD, resistance emerged within months. Data from AMG510 treatment-resistant LUAD (GSE204753) and single-cell datasets (GSE149655) were analyzed. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to explore enriched signaling pathways, nomogram models were constructed, and transcription factors predicting resistance biomarkers were predicted. CIBERSORT identified immune cell subpopulations, and their association with resistance biomarkers was assessed through single-cell analysis. AMG510-resistant LUAD cells (H358-AR) were constructed, and proliferative changes were evaluated using a CCK-8 assay. Key molecules for AMG510 resistance, including SLC2A1, TLE1, FAM83A, HMGA2, FBXO44, and MTRNR2L12, were recognized. These molecules impacted multiple signaling pathways and the tumor microenvironment and were co-regulated by various transcription factors. Single-cell analysis revealed a dampening effect on immune cell function, with associations with programmed cell death ligand 1 (PDL1) expression, cytokine factors, and failure factors. The findings indicate that these newly identified biomarkers are linked to the abnormal expression of PDL1 and have the potential to induce resistance through immunosuppression. These results highlight the need for further research and therapeutic intervention to address this issue effectively.

NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data

Human leukocyte antigen (HLA) typing is of great importance in clinical applications such as organ transplantation, blood transfusion, disease diagnosis and treatment, and forensic analysis. In recent years, nanopore sequencing technology has emerged as a rapid and cost-effective option for HLA typing. However, due to the principles and data characteristics of nanopore sequencing, there was a scarcity of robust and generalizable bioinformatics tools for its downstream analysis, posing a significant challenge in deciphering the thousands of HLA alleles present in the human population. To address this challenge, we developed NanoHLA as a tool for high-resolution typing of HLA class I genes without error correction based on nanopore sequencing. The method integrated the concepts of HLA type coverage analysis and the data conversion techniques employed in Nano2NGS, which was characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. In validation with public nanopore sequencing datasets, NanoHLA showed an overall concordance rate of 84.34% for HLA-A, HLA-B, and HLA-C, and demonstrated superior performance in comparison to existing tools such as HLA-LA. NanoHLA provides tools and solutions for use in HLA typing related fields, and look forward to further expanding the application of nanopore sequencing technology in both research and clinical settings. The code is available at https://github.com/langjidong/NanoHLA .

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Background

Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy.

Methods

In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis.

Results

Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD.

Conclusions

Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

EVI2B may be a novel prognostic marker for lung adenocarcinoma

Objective: This study intended to unravel the relationship of EVI2B expression with lung adenocarcinoma (LUAD). Methods: TIMER1.0, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases, as well as the University of Alabama at Birmingham Cancer website, were used to analyze the expression of EVI2B and its relationship with clinical features. The relationship between survival curve analysis and prognosis was analyzed. The role of EVI2B in LUAD was verified by wet experiments. Results: EVI2B was markedly downregulated in LUAD. There was a relationship between the expression of EVI2B and clinical features. Low EVI2B level was substantially implicated in low survival in LUAD. EVI2B overexpression constrained LUAD cell viability, migration and invasion. Conclusion: EVI2B was related to prognosis and immune microenvironment in LUAD, suggesting that EVI2B may be a novel prognostic marker for LUAD.

Single cell sequencing analysis constructed the N7-methylguanosine (m7G)-related prognostic signature in uveal melanoma

BACKGROUND

Uveal melanoma is a highly malignant tumor in the eye. Its recurrence and metastasis are common, and the prognosis is poor.

METHODS

The transcriptome data of UVM were downloaded from TCGA database, and the single cell sequencing dataset GSE139829 was downloaded from GEO database. Weighted co-expression network analysis was used to explore the modules associated with m7G. Lasso regression was used to construct M⁷G-related prognostic signature. Immune infiltration analysis was used to explore the significance of the model in the tumor immune microenvironment. Finally, cell assays were used to explore the function of key genes in the MUM-2B and OCM-1 cell lines of UVM.

RESULTS

The prognostic signature was constructed by Cox regression and Lasso regression. Patients could be divided into high-risk group and low-risk group by this signature, and the high-risk group had worse prognosis (P<0.05). Cell experiments showed that the proliferation, invasion and migration ability of UVM cell lines were significantly decreased after the knockdown of PAG1, a key gene in signature, which proved that PAG1 might be a potential target of UVM.

CONCLUSIONS

Our study explored the significance of m⁷G in UVM, provided biomarkers for its diagnosis and treatment.

Efficacy, safety and biomarker analysis of durvalumab in patients with mismatch-repair deficient or microsatellite instability-high solid tumours

BACKGROUND

In this study we aimed to evaluate the efficacy and safety of the PD-L1 inhibitor durvalumab across various mismatch repair deficient (dMMR) or microsatellite instability-high (MSI-H) tumours in the Drug Rediscovery Protocol (DRUP). This is a clinical study in which patients are treated with drugs outside their labeled indication, based on their tumour molecular profile.

PATIENTS AND METHODS

Patients with dMMR/MSI-H solid tumours who had exhausted all standard of care options were eligible. Patients were treated with durvalumab. The primary endpoints were clinical benefit ((CB): objective response (OR) or stable disease ≥16 weeks) and safety. Patients were enrolled using a Simon like 2-stage model, with 8 patients in stage 1, up to 24 patients in stage 2 if at least 1/8 patients had CB in stage 1. At baseline, fresh frozen biopsies were obtained for biomarker analyses.

RESULTS

Twenty-six patients with 10 different cancer types were included. Two patients (2/26, 8%) were considered as non-evaluable for the primary endpoint. CB was observed in 13 patients (13/26, 50%) with an OR in 7 patients (7/26, 27%). The remaining 11 patients (11/26, 42%) had progressive disease. Median progression-free survival and median overall survival were 5 months (95% CI, 2-not reached) and 14 months (95% CI, 5-not reached), respectively. No unexpected toxicity was observed. We found a significantly higher structural variant (SV) burden in patients without CB. Additionally, we observed a significant enrichment of JAK1 frameshift mutations and a significantly lower IFN-γ expression in patients without CB.

CONCLUSION

Durvalumab was generally well-tolerated and provided durable responses in pre-treated patients with dMMR/MSI-H solid tumours. High SV burden, JAK1 frameshift mutations and low IFN-γ expression were associated with a lack of CB; this provides a rationale for larger studies to validate these findings.

TRIAL REGISTRATION

Clinical trial registration: NCT02925234. First registration date: 05/10/2016.

Biomaterials-assisted construction of neoantigen vaccines for personalized cancer immunotherapy

INTRODUCTION

Cancer vaccine represents a promising strategy toward personalized immunotherapy, and its therapeutic potency highly relies on the specificity of tumor antigens. Among these extensively studied tumor antigens, neoantigens, a type of short synthetic peptides derived from random somatic mutations, have been shown to be able to elicit tumor-specific antitumor immune response for tumor suppression. However, challenges remain in the efficient and safe delivery of neoantigens to antigen-presenting cells inside lymph nodes for eliciting potent and sustained antitumor immune responses. The rapid advance of biomaterials including various nanomaterials, injectable hydrogels, and macroscopic scaffolds has been found to hold great promises to facilitate the construction of efficient cancer vaccines attributing to their high loading and controllable release capacities.

AREAS COVERED

In this review, we will summarize and discuss the recent advances in the utilization of different types of biomaterials to construct neoantigen-based cancer vaccines, followed by a simple perspective on the future development of such biomaterial-assisted cancer neoantigen vaccination and personalized immunotherapy.

EXPERT OPINION

These latest progresses in biomaterial-assisted cancer vaccinations have shown great promises in boosting substantially potentiated tumor-specific antitumor immunity to suppress tumor growth, thus preventing tumor metastasis and recurrence.

Mutual connected IL-6, EGFR and LIN28/Let7-related mechanisms modulate PD-L1 and IGF upregulation in HNSCC using immunotherapy

The development of techniques and immunotherapies are widely applied in cancer treatment such as checkpoint inhibitors, adoptive cell therapy, and cancer vaccines apart from radiation therapy, surgery, and chemotherapy give enduring anti-tumor effects. Minority people utilize single-agent immunotherapy, and most people adopt multiple-agent immunotherapy. The difficulties are resolved by including the biomarkers to choose the non-responders' and responders' potentials. The possibility of the potential complications and side effects are examined to improve cancer therapy effects. The Head and Neck Squamous Cell Carcinoma (HNSCC) is analyzed with the help of programmed cell death ligand 1 (PD-L1) and Insulin-like growth factor (IGF). But how IGF and PD-L1 upregulation depends on IL-6, EGFR, and LIN28/Let7-related mechanisms are poorly understood. Briefly, IL-6 stimulates gene expressions of IGF-1/2, and IL-6 cross-activates IGF-1R signaling, NF-κB, and STAT3. NF-κB, up-regulating PD-L1 expressions. IL-6/JAK1 primes PD-L1 for STT3-mediated PD-L1 glycosylation, stabilizes PD-L1 and trafficks it to the cell surface. Moreover, ΔNp63 is predominantly overexpressed over TAp63 in HNSCC, elevates circulating IGF-1 levels by repressing IGFBP3, and activates insulin receptor substrate 1 (IRS1).TP63 and SOX2 form a complex with CCAT1 to promote EGFR expression. EGFR activation through EGF binding extends STAT3 activation, and EGFR and its downstream signaling prolong PD-L1 mRNA half-life. PLC-γ1 binding to a cytoplasmic motif of elevated PD-L1 improves EGF-induced activation of inositol 1,4,5-tri-phosphate (IP3), and diacylglycerol (DAG) subsequently elevates RAC1-GTP. RAC1-GTP was convincingly demonstrated to induce the autocrine production and action of IL-6/IL-6R, forming a feedback loop for IGF and PD-L1 upregulation. Furthermore, the LIN28-Let7 axis mediates the NF-κB-IL-6-STAT3 amplification loop, activated LIN28-Let7 axis up-regulates RAS, AKT, IL-6, IGF-1/2, IGF-1R, Myc, and PD-L1, plays pivotal roles in IGF-1R activation and Myc, NF-κB, STAT3 concomitant activation. Therefore, based on a detailed mechanisms review, our article firstly reveals that IL-6, EGFR, and LIN28/Let7-related mechanisms mediate PD-L1 and IGF upregulation in HNSCC, which comprehensively influences immunity, inflammation, metabolism, and metastasis in the tumor microenvironment, and might be fundamental for overcoming therapy resistance.

Intratumoral immune heterogeneity of prostate cancer characterized by typing and hub genes

Discordant abundances of different immune cell subtypes is regarded to be an essential feature of tumour tissue. Direct studies in Prostate cancer (PC) of intratumoral immune heterogeneity characterized by immune cell subtype, are still lacking. Using the single sample gene set enrichment analysis (ssGSEA) algorithm, the abundance of 28 immune cells infiltration (ICI) were determined for PC. A NMF was performed to determine tumour-sample clustering based on the abundance of ICI and PFS information. Hub genes of clusters were identified via weighted gene co-expression network analysis (WGCNA). The multivariate dimensionality reduction analysis of hub genes expression matrix was carried out via principal component analysis (PCA) to obtain immune score (IS). We analysed the correlation between clustering, IS and clinical phenotype. We divided the 495 patients into clusterA (n = 193) and clusterB (n = 302) on the basis of ICI and PFS via NMF. The progression-free survival (PFS) were better for clusterA than for clusterB (p < 0.001). Each immune cell subtypes was more abundant in clusterA than in clusterB (p < 0.001). The expression levels of CTAL-4 and PD-L1 were lower in clusterB than in clusterA (p < 0.001 and p = 0.006). We obtained 103 hub genes via WGCNA. In the training and validation cohorts, the prognosis of high IS group was worse than that of the low IS group (p < 0.05). IS had good predictive effect on 5-year PFS. The expression of immune checkpoint genes was higher in the low IS group than in the high IS group (p < 0.01). Patients with low IS and receiving hormone therapy had better prognosis than other groups. The combination of IS and clinical characteristics including lymph node metastasis and gleason score can better differentiate patient outcomes than using it alone. IS was a practical algorithm to predict the prognosis of patients. Advanced PC patients with low IS may be more sensitive to hormone therapy. CXCL10, CXCL5, MMP1, CXCL12, CXCL11, CXCL2, STAT1, IL-6 and TLR2 were hub genes, which may drive the homing of immune cells in tumours and promote immune cell differentiation.

The role of neoantigens in tumor immunotherapy

Tumor neoantigens are aberrant polypeptides produced by tumor cells as a result of genomic mutations. They are also tumor-specific antigens (TSA). Neoantigens are more immunogenic than tumor-related antigens and do not induce autoimmunity. Based on the rapid development of bioinformatics and the continuous update of sequencing technology, cancer immunotherapy with tumor neoantigens has made promising breakthroughs and progress. In this review, the generation, prediction, and identification of novel antigens, as well as the individualized treatments of neoantigens, were first introduced. Secondly, the mechanism of Chimeric Antigen Receptor T-Cell Immunotherapy (CAR-T) therapy and immune checkpoint blockade therapy in the treatment of tumors were outlined, and the three treatment methods were compared. Thirdly, the application of neoantigens in CAR-T therapy and PD-1/PD-L1 blockade therapy was briefly described. The benefits of the neoantigen vaccines over common vaccines were summarized as well. Finally, the prospect of neoantigen therapy was presented.

EF-Hand Domain-Containing Protein D2 (EFHD2) Correlates with Immune Infiltration and Predicts the Prognosis of Patients: A Pan-Cancer Analysis

Background

EF-hand domain-containing protein D2 (EFHD2) has recently been reported to participate in initiation of cancer. More evidence indicates that EFHD2 plays an important role in tumors, but the pan-cancer analysis of EFHD2 is still very limited.

Methods

In this study, we downloaded the original mRNA expression data and SNP data of 33 kinds of tumor data. The gene expression data of different tissues were downloaded from the GTEX database, combined with TCGA data and corrected to calculate the difference of gene expression. The data of total survival time (OS) and progression-free survival (PFS) of TCGA patients were downloaded from the Xena database to further survey the relationship between the EFHD2 expression and prognosis. The CIBERSORT algorithm was used to analyze the RNA-seq data of 33 kinds of cancer patients in different subgroups. In this study, NCI-60 drug sensitivity data and RNA-seq data were downloaded to explore the relationship between genes and common antineoplastic drug sensitivity through correlation analysis. In this study, GSEA analysis was carried out from the Molecular Signature database through the packages of "clusterprofiler" and "enrichplot." By comparing the differences of signal pathways between high and low gene expression groups, the possible molecular mechanism of prognostic differences among 33 kinds of tumors was determined.

Results

Our results indicated that EFHD2 was highly expressed in 23 kinds of tumors. In addition, EFHD2 was associated with stage in many kinds of tumors. The expression of EFHD2 was closely related to the OS of 12 kinds of cancer patients. In addition, Kaplan-Meier- (KM-) plot survival analysis indicated that the high expression of EFHD2 was related to the poor OS of 5 kinds of cancer, and the expression of EFHD2 was closely related to the PFI of 5 kinds of cancer patients. The expression of EFHD2 was closely related to immune infiltration, among which 18 cancers were significantly correlated with CD8T cells, 14 cancers were significantly correlated with T regulatory (Tregs) cells, 15 cancers were significantly correlated with CD4 memory activated Tcells, and EFHD2 was significantly correlated with common tumor-related regulatory genes such as TGF beta signaling, TNFA signaling, hypoxia, scorch death, DNA repair, autophagy, and iron death-related genes. The expression level of EFHD2 was significantly correlated with each tumor of TMB, including STAD, SARC, ACC, THYM, KICH, THCA, and TGCT. In MSI, there were significant differences in THYM, STAD, THCA, and TGCT. We used the CellMiner database to explore the sensitivity between EFHD2 gene and common antineoplastic drugs and found that the prediction of high expression of EFHD2 was related to the resistance of many antineoplastic drugs. In renal cell carcinoma, the high expression of EFHD2 is mainly concentrated in ALLOGRAFT_REJECTION, REACTIVE_OXYGEN_SPECIES_PATHWAY, INTERFERON_GAMMA_RESPONSE, IL6_JAK_STAT3_SIGNALING, INTERFERON_ALPHA_RESPONSE, and other signal pathways. GO results showed that the genes were mainly enriched in response to interferon-gamma, antigen processing and presentation, cellular response to interferon-gamma, and other pathways. KEGG results demonstrated that EFHD2 was mainly rich in phagosome, Epstein-Barr virus infection, Staphylococcus aureus infection, and other pathways. The results of Kaplan-Meier survival analysis demonstrated that the high expression of EFHD2 was significantly related to the poor prognosis.

Conclusion

Our findings highlight the predictive value of EFHD2 in cancer and provide a potential research direction for elucidating the role of EFHD2 in tumorigenesis and drug resistance.

Applying artificial intelligence for cancer immunotherapy

Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

Safety and Efficacy of Personalized Cancer Vaccines in Combination With Immune Checkpoint Inhibitors in Cancer Treatment

Cancer immunotherapy can induce sustained responses in patients with cancers in a broad range of tissues, however, these treatments require the optimized combined therapeutic strategies. Despite immune checkpoint inhibitors (ICIs) have lasting clinical benefit, researchers are trying to combine them with other treatment modalities, and among them the combination with personalized cancer vaccines is attractive. Neoantigens, arising from mutations in cancer cells, can elicit strong immune response without central tolerance and out-target effects, which is a truly personalized method. Growing studies show that the combination can elevate the antitumor efficacy with acceptable safety and minimal additional toxicity compared with single agent vaccine or ICI. Herein, we have searched these preclinical and clinical trials and summarized safety and efficacy of personalized cancer vaccines combined with ICIs in several malignancies. Meanwhile, we discuss the rationale of the combination and future challenges.

Nano-Enhanced Cancer Immunotherapy: Immunology Encounters Nanotechnology

Cancer immunotherapy utilizes the immune system to fight cancer and has already moved from the laboratory to clinical application. However, and despite excellent therapeutic outcomes in some hematological and solid cancers, the regular clinical use of cancer immunotherapies reveals major limitations. These include the lack of effective immune therapy options for some cancer types, unresponsiveness to treatment by many patients, evolving therapy resistance, the inaccessible and immunosuppressive nature of the tumor microenvironment (TME), and the risk of potentially life-threatening immune toxicities. Given the potential of nanotechnology to deliver, enhance, and fine-tune cancer immunotherapeutic agents, the combination of cancer immunotherapy with nanotechnology can overcome some of these limitations. In this review, we summarize innovative reports and novel strategies that successfully combine nanotechnology and cancer immunotherapy. We also provide insight into how nanoparticular combination therapies can be used to improve therapy responsiveness, to reduce unwanted toxicity, and to overcome adverse effects of the TME.