HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma

hccTAAb Atlas: An Integrated Knowledge Database for Tumor-Associated Autoantibodies in Hepatocellular Carcinoma

Tumor-associated autoantibodies (TAAbs) have demonstrated potential as biomarkers for cancer detection. However, the understanding of their role in hepatocellular carcinoma (HCC) remains limited. In this study, we aimed to systematically collect and standardize information about these TAAbs and establish a comprehensive database as a platform for in-depth research. A total of 170 TAAbs were identified from published papers retrieved from PubMed, Web of Science, and Embase. Following normative reannotation, these TAAbs were referred to as 162 official symbols. The hccTAAb (tumor-associated autoantibodies in hepatocellular carcinoma) atlas was developed using the R Shiny framework and incorporating literature-based and multiomics data sets. This comprehensive online resource provides key information such as sensitivity, specificity, and additional details such as official symbols, official full names, UniProt, NCBI, HPA, neXtProt, and aliases through hyperlinks. Additionally, hccTAAb offers six analytical modules for visualizing expression profiles, survival analysis, immune infiltration, similarity analysis, DNA methylation, and DNA mutation analysis. Overall, the hccTAAb Atlas provides valuable insights into the mechanisms underlying TAAb and has the potential to enhance the diagnosis and treatment of HCC using autoantibodies. The hccTAAb Atlas is freely accessible at https://nscc.v.zzu.edu.cn/hccTAAb/.

Anti-hepatoma Effect of DC2.4 Cells Transfected with Tumor-Associated Antigen Cdc25C In Vitro

OBJECTIVE

Cell division cyclin 25 homolog C (Cdc25C) is a tumor-associated antigen candidate gene, and this may be used as an effective target in cancer treatment. The present study aims to evaluate the lysis effect of cytotoxic T lymphocytes (CTLs) induced by dendritic cell line DC2.4 overexpressing Cdc25C, and the feasibility of Cdc25C as a component in hepatoma immunotherapy.

METHODS

The mouse Cdc25C gene was ligated into a lentiviral vector, and transfected into DC2.4 cells. The DC2.4 cell phenotype and cytokine secretion were determined by flow cytometry and ELISA, respectively. CD8⁺ T cells were sorted from the spleens of C57BL/6 mice using a magnetic bead sorting kit obtained from Miltenyi Biotech, Germany, and co-cultured with DC2.4 cells for one week as effector cells. Then, IL-2, granzyme B and perforin were detected in the CTL culture medium by ELISA. Next, time-resolved fluorescence immunoassay was used to detect the immune killing effect of Cdc25C-specific CTLs on target cells. Meanwhile, the effect of blocking MHC-I sites on target cells with a monoclonal anti-MHC-I antibody was evaluated.

RESULTS

The results revealed that Cdc25C could be stably overexpressed in DC2.4 cells by LV-Cdc25C infection. DC2.4 cells transfected with LV-Cdc25C secreted more IL-6, IL-12, TNF-α and IFN-γ, and had higher expression levels of CD40, CD86, CCR7 and MHC-II than unaltered DC2.4 cells. The elevated Cdc25C in dendritic cells also further increased the secretion of IL-2, granzyme B and perforin to elicit Cdc25C-specific CTLs, and induced the higher cytotoxicity in Hepa1-6 cell lines (P<0.05), but this had no effect on the target cells when MHC-I monoclonal antibodies were blocked.

CONCLUSION

DC2.4 cells transfected with LV-Cdc25C can induce specific CTLs, and result in a strong cellular immune response. The dendritic cells that overexpress Cdc25C may be useful for hepatoma immunotherapy.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

AAgMarker 1.0: a resource of serological autoantigen biomarkers for clinical diagnosis and prognosis of various human diseases

Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.

C/EBPβ promotes the viability of human bladder cancer cell by contributing to the transcription of bladder cancer specific lncRNA UCA1

Urothelial Carcinoma Antigen 1 (UCA1) is a cell and tissue specific long non-coding RNA (lncRNA) associated with the tumorigenesis and invasion of bladder cancer. However, the mechanism driving the over-transcription of UCA1 in bladder cancer cells remains unclear. It has been reported that C/EBPβ has a significant role of regulation in tumorigenesis. Here we report that the expression of UCA1 was dramatically inhibited in 5637 cells with C/EBPβ down-regulation. Additionally, the function tests indicated that C/EBPβ could promote 5637 cells growth and colony formation by inducing the expression level of UCA1. These data suggest that C/EBPβ was involved in transcriptional regulation of UCA1 and contributed substantially to its high expression and proliferation promoting in bladder cancer cells.

AAgAtlas 1.0: a human autoantigen database

Autoantibodies refer to antibodies that target self-antigens, which can play pivotal roles in maintaining homeostasis, distinguishing normal from tumor tissue and trigger autoimmune diseases. In the last three decades, tremendous efforts have been devoted to elucidate the generation, evolution and functions of autoantibodies, as well as their target autoantigens. However, reports of these countless previously identified autoantigens are randomly dispersed in the literature. Here, we constructed an AAgAtlas database 1.0 using text-mining and manual curation. We extracted 45 830 autoantigen-related abstracts and 94 313 sentences from PubMed using the keywords of either ‘autoantigen’ or ‘autoantibody’ or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by our bio-entity recognizer based on the Protein Ontology. Finally, we identified 1126 genes as human autoantigens and 1071 related human diseases, with which we constructed a human autoantigen database (AAgAtlas database 1.0). The database provides a user-friendly interface to conveniently browse, retrieve and download human autoantigens as well as their associated diseases. The database is freely accessible at http://biokb.ncpsb.org/aagatlas/. We believe this database will be a valuable resource to track and understand human autoantigens as well as to investigate their functions in basic and translational research.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

HEPA and PARSE: Systematic discovery of clinically relevant tumor-specific antigens

The effective discovery of tumor-specific antigens (TSAs) holds the key for the development of new diagnostic assays and immunotherapeutic approaches against cancer. Here, we discuss our recently developed technologies, HEPA and PARSE, which allow for the systematic identification of TSAs, generating a reservoir of immunologically and clinically relevant targets.

Cloning and prokaryotic expression of human Cdc25C

AIM: To clone the human Cdc25C gene and construct a recombinant prokaryotic system to express this protein.

METHODS: Total RNA were isolated from human hepatocellular carcinoma Bel-7404 cells and reverse transcribed, and the resulting cDNA was used as a template to amplify the human Cdc25C gene by RT-PCR. The amplified PCR product was cloned into pMD18-T and pET-32a (+) vectors and sequenced. Next, pET-32a(+)-Cdc25C was transformed into chemically competent E. coli strains, including BL21 (DE3), BL21 (DE3) pLysS and Transetta (DE3), to express the protein after induction with 0.25 mmol/L IPTG and ArtMediaTM protein expression, respectively. The fusion protein was identified by Coomassie staining and mass spectrometry analysis.

RESULTS: The Cdc25C gene and pMD18-T-Cdc25C and pET-32a(+)-Cdc25C vectors were obtained successfully. Three strains of E. coli which harbored the recombinant plasmid could express the TRx-His-Cdc25C fusion protein. The expressed protein was identical to the Cdc25C protein as revealed by Coomassie staining and mass spectrometry.

CONCLUSION: The recombinant protein of tumor-associated antigen Cdc25C has been successfully obtained.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

An integrated genome-wide approach to discover tumor-specific antigens as potential immunologic and clinical targets in cancer

Tumor-specific antigens (TSA) are central elements in the immune control of cancers. To systematically explore the TSA genome, we developed a computational technology called heterogeneous expression profile analysis (HEPA), which can identify genes relatively uniquely expressed in cancer cells in contrast to normal somatic tissues. Rating human genes by their HEPA score enriched for clinically useful TSA genes, nominating candidate targets whose tumor-specific expression was verified by reverse transcription PCR (RT-PCR). Coupled with HEPA, we designed a novel assay termed protein A/G-based reverse serological evaluation (PARSE) for quick detection of serum autoantibodies against an array of putative TSA genes. Remarkably, highly tumor-specific autoantibody responses against seven candidate targets were detected in 4% to 11% of patients, resulting in distinctive autoantibody signatures in lung and stomach cancers. Interrogation of a larger cohort of 149 patients and 123 healthy individuals validated the predictive value of the autoantibody signature for lung cancer. Together, our results establish an integrated technology to uncover a cancer-specific antigen genome offering a reservoir of novel immunologic and clinical targets.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

The role and clinical implications of microRNAs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is common and one of the most aggressive of all human cancers. Recent studies have indicated that miRNAs, a class of small noncoding RNAs that regulate gene expression post-transcriptionally, directly contribute to HCC by targeting many critical regulatory genes. Several miRNAs are involved in hepatitis B or hepatitis C virus replication and virus-induced changes, whereas others participate in multiple intracellular signaling pathways that modulate apoptosis, cell cycle checkpoints, and growth-factor-stimulated responses. When disturbed, these pathways appear to result in malignant transformation and ultimately HCC development. Recently, miRNAs circulating in the blood have acted as possible early diagnostic markers for HCC. These miRNA also could serve as indicators with respect to drug efficacy and be prognostic in HCC patients. Such biomarkers would assist stratification of HCC patients and help direct personalized therapy. Here, we summarize recent advances regarding the role of miRNAs in HCC development and progression. Our expectation is that these and ongoing studies will contribute to the understanding of the multiple roles of these small noncoding RNAs in liver tumorigenesis.

dbDEMC: a database of differentially expressed miRNAs in human cancers

Background

MicroRNAs (miRNAs) are small noncoding RNAs about 22 nt long that negatively regulate gene expression at the post-transcriptional level. Their key effects on various biological processes, e.g., embryonic development, cell division, differentiation and apoptosis, are widely recognized. Evidence suggests that aberrant expression of miRNAs may contribute to many types of human diseases, including cancer. Here we present a database of differentially expressed miRNAs in human cancers (dbDEMC), to explore aberrantly expressed miRNAs among different cancers.

Results

We collected the miRNA expression profiles of 14 cancer types, curated from 48 microarray data sets in peer-reviewed publications. The Significance Analysis of Microarrays method was used to retrieve the miRNAs that have dramatically different expression levels in cancers when compared to normal tissues. This database provides statistical results for differentially expressed miRNAs in each data set. A total of 607 differentially expressed miRNAs (590 mature miRNAs and 17 precursor miRNAs) were obtained in the current version of dbDEMC. Furthermore, low-throughput data from the same literature were also included in the database for validation. An easy-to-use web interface was designed for users. Annotations about each miRNA can be queried through miRNA ID or miRBase accession numbers, or can be browsed by different cancer types.

Conclusions

This database is expected to be a valuable source for identification of cancer-related miRNAs, thereby helping with the improvement of classification, diagnosis and treatment of human cancers. All the information is freely available through http://159.226.118.44/dbDEMC/index.html.

Developing effective tumor vaccines: basis, challenges and perspectives

A remarkable advance in tumor immunology during the last decade is the elucidation of the antigenic basis of tumor recognition and destruction. A variety of tumor antigens have been identified using several strategies including conventional experiments and newly developed bioinformatics. Among these antigens, cancer/testis antigen (CT antigen) is considered to be the most promising target for immunotherapy by vaccination. Successful immunotherapy of tumors requires understanding of the natural relationship between the immune system and tumor in the status of differentiation, invasion and maturation. Continued progress in development of effective cancer vaccines depends on the identification of appropriate target antigens, the establishment of optimal immunization strategies without harmful autoimmune responses and the ability of manipulating tumor microenvironment to circumvent immune suppression and to augment the anti-tumor immune response.

Rapid identification of UCA1 as a very sensitive and specific unique marker for human bladder carcinoma

PURPOSE

The most common genitourinary malignancy in China is bladder transitional cell carcinoma (TCC). Early diagnosis of new and recurrent bladder cancers, followed by timely treatment, will help decrease mortality. There are currently no satisfactory markers for bladder cancer available in clinics. Better diagnostic methods are highly demanded.

EXPERIMENTAL DESIGN

In this research, we have used comprehensive expressed sequence tag analysis, serial analysis of gene expression, and microarray analysis and quickly discovered a candidate marker, urothelial carcinoma associated 1 (UCA1). The UCA1 gene was characterized and its performance as a urine marker was analyzed by reverse transcription-PCR with urine sediments. A total of 212 individuals were included in this study, 94 having bladder cancers, 33 ureter/pelvic cancers, and 85 normal and other urinary tract disease controls.

RESULTS

UCA1 was identified as a novel noncoding RNA gene dramatically up-regulated in TCC and it is the most TCC-specific gene yet identified. The full-length cDNA was 1,439 bp, and sequence analysis showed that it belonged to the human endogenous retrovirus H family. Clinical tests showed that UCA1 assay was highly specific (91.8%, 78 of 85) and very sensitive (80.9%, 76 of 94) in the diagnosis of bladder cancer and was especially valuable for superficial G2-G3 patients (sensitivity 91.1%, 41 of 45). It showed excellent differential diagnostic performance in various urinary tract diseases without TCC.

CONCLUSIONS

UCA1 is a very sensitive and specific unique marker for bladder cancer. It could have important implications in postoperative noninvasive follow-up. This research also highlights a shortcut to new cancer diagnostic assays through integration of in silico isolation methods with translational clinical tests based on RNA detection protocols.