Comprehensive Pan-cancer Analysis Identified ZCCHC3 as an Immunological and Prognostic Biomarker
by Xuehan Gao1# Pinzhi Dong1#, Linna Wei1, Jin Chen1, Haiyan Wang2, Ming Qin1*, Junmin Luo1*, Jihong Feng3*
1Department of Immunology, Zunyi Medical University, Zunyi 563000, China
2School of Public Health, Zunyi Medical University, Zunyi 563000, China
3Department of oncology, Lishui People’s Hospital, Sixth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China # XHG and PZD contributed equally to this work.
*Corresponding authors: Ming Qin, Department of immunology, Zunyi Medical University, GuiZhou Province, 563000 P.R.China.
Junmin Luo, Department of immunology, Zunyi Medical University, GuiZhou Province, 563000 P.R.China.
Jihong Feng, Department of oncology, Lishui People’s Hospital, Zhejiang Province, 323000 P.R.China.
Received Date: 29 August, 2023
Accepted Date: 08 September, 2023
Published Date: 11 September, 2023
Citation: Gao X, Dong P, Wei L, Chen J, Wang H, et al. (2023) Comprehensive Pan-cancer Analysis Identified ZCCHC3 as an Immunological and Prognostic Biomarker. Ann med clin Oncol 5: 154. https://doi.org/10.29011/2833-3497.000154
Purpose: ZCCHC3 may be closely associated with the development of tumors. However, the detailed function and role of ZCCHC3 in pan-cancer are largely unknown and require further in-depth investigation. Consequently, this study aims to investigate the biological functions of ZCCHC3, and its potential to predict prognosis and immunotherapy response in pancancer. Methods: We applied multiple public databases such as TCGA, TIMER, GTEx, CCLE, and HPA to explore ZCCHC3 expression in tumors. Univariate Cox regression analysis were used to detect the effects of ZCCHC3 on OS, DSS, DFI, and PFI in these patients. Subsequently, the correlation coefficient between ZCCHC3 levels and immune infiltration in different cancer types was used using TIMER2.0. Finally, the relationship between ZCCHC3 and tumor immune regulatory genes, immune checkpoint, TMB, and MSI were investigated. Results: ZCCHC3 expression was elevated in most tumor tissues compared to normal tissues, and was positively or negatively correlated with the prognosis of different tumors, positively correlated with immune cell infiltration and immune checkpoint gene expression in various tumors. Conclusion:Comprehensive pan-cancer analysis identified ZCCHC3 as an immunological and prognostic biomarker.
Keywords: ZCCHC3; Prognosis; Immune analysis; Pan- cancer; The Cancer Genome Atlas
Cancer development is a complex process that involves the participation of many signaling pathways and genes [1-2]. Pan-cancer analysis refers to the cross-sectional comparison of certain features across multiple tumour types through the use of bioinformatics analysis tools, with the implication of applying diagnosis and treatment to a wider range of tumours through cross-tumour similarity . In recent years, with the rapid development of sequencing technologies and the establishment of online databases, a large influx of data has provided the basis for comprehensive pan-cancer analysis to guide the diagnosis and treatment of tumours. Consequently, the identification of key genes between different cancer types can help in the diagnosis and treatment of cancer.
Zinc finger proteins are a series of proteins with a fingerlike shape, which have a relatively short sequence and need to be presented in a more stable condition by binding to zinc ions, and contain an 18-residue structural domain with the sequence CX2CX4HX4C . A variety of zinc finger-containing eukaryotic proteins are involved in many aspects of nucleic acid metabolism, ranging from DNA transcription to RNA degradation, posttranscriptional gene silencing, and the biogenesis of small RNAs, which, in turn, regulate gene expression . Zinc finger CCHCtype (ZCCHC) superfamily proteins are thought to bind with high affinity to single-stranded nucleic acids. In humans, 25 ZCCHC proteins are annotated in the HGNC database, and most members of the ZCCHC family of hyperproteins are involved in multiple steps of RNA transcription, biogenesis, splicing, and translation and degradation. Thus, zinc finger proteins are an important class of proteins that regulate gene expression and play important roles in life activities.
The CCHC-type zinc-finger protein ZCCHC3 is a CCHCtype zinc-finger protein, which was recently discovered to involve in antiviral innate immune responses . Lian et al. . revealed ZCCHC3 to be a co-sensor of cyclic GMP-AMP (cGAMP) synthase (cGAS) for the recognition of cytosolic dsDNA; cGAS catalyzes synthesis of the second messenger molecule cGAMP, which in turn binds and activates the adaptor STING to initiate an innate antiviral response. Furthermore, ZCCHC3 was shown to bind dsRNA and act as a positive regulator of RIG-I-like receptor (RLR), including RIG-I (retinoic acid-inducible gene-I) and MDA5, and Toll-like receptor 3 (TLR3) signaling. In addition, ZCCHC3 has been shown to be part of the SARS-CoV-2 virus protein interactome and to inhibit avian H9N2 virus and pseudorabies virus through type I IFN signaling. Briefly, ZCCHC3 promotes interactions between viral nucleic acids and pattern recognition receptors, including cGAS, RIG-I-like receptor and Toll-like receptor 3, thereby positively regulating RNA and DNA virus-triggered IFN signalling, suggesting a multifunctional role in antiviral innate immunity . Notably, ZCCHC3 may play an important role in tumour development. Wang et al. found that FOXD3 Antisense RNA 1 (FOXD3-AS1) sponges miR-296-5p to elevate ZCCHC3 to facilitate malignancy in osteosarcoma, this may provide potential guidance for finding effective targets for the treatment of osteosarcoma . Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Marco et al. . have identified 201 altered chromosomal bands and 3,300 altered genes in human ovarian cancer samples. Then, the 3,300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression, Interestingly, ZCCHC3 is among the highly expressed genes. The above findings suggest that ZCCHC3 may have broad and diverse regulatory roles in cancer. Up to now, no studies have performed pan-cancer analyses of ZCCHC3. Therefore, we aimed to elucidate the role of ZCCHC3 in tumour immunomodulation and immunotherapy through a comprehensive pan-cancer analysis.
Materials and methods
ZCCHC3 Expression Pattern in Human Pan-Cancer
TCGA, a cornerstone of the cancer genomics projects, had characterized more than 20,000 primary cancer samples and corresponding non‐carcinoma samples from 33 types of cancers. In the present study, the TCGA‐processed level 3 RNA‐sequencing data sets, along with the corresponding clinical annotations, were obtained using the University of California Santa Cruz (UCSC) cancer genome browser (https:// tcga.xenahubs.net, accessed April 2020). The CCLE public project has comprehensively characterized a tremendous number of human tumour models both genetically and pharmacologically (https://portals.broadinstitute. org/ccle). To examine differential gene expression in cancers on a larger scale, the CCLE database, which contains RNA‐sequencing data sets for over 1,000 cell lines, was used. RNA sequencing data and clinical follow-up information for patients with 33 types of cancers.
Protein level analysis
The Human Protein Atlas (HPA: https://www.proteinatlas. org/) database was used to explore the protein level of ZCCHC3 in human tumor and normal tissues. GeneCards (https://www. genecards.org/) was used to visualise the subcellular locations of ZCCHC3. String (https://string-db.org/) database was used to construct the protein-protein interaction network (PPI) of ZCCHC3.
Pathological staging analysis
The expression data of ZCCHC3 gene in each sample were extracted from TCGA database, and the samples from Solid Tissue Normal, primary blood derived cancer-peripheral blood and Primary Tumor were further screened. Log2 (x+0.001) transformation was performed on each expression value, and finally, the cancer species with less than 3 samples in a single cancer species were eliminated, and finally the expression data of 26 cancer species were obtained. The expression difference between normal samples and tumor samples in each tumor was calculated by R software (version 3.6.4), and the difference significance was analyzed by unpaired Wilcoxon rank sum and signed rank tests.
The connection between the ZCCHC3 expression and the prognosis of patients, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in 33 types of cancer was examined using forest plots.
ZCCHC3 and tumor Immune infiltration
We used the “Immune-Gene” module of the TIMER2.0 datebase to explore the association between ZCCHC3 expression and immune infiltrates across all TCGA tumors. The immune cells of Tregs, cancer-associated fibroblast, DC, Macrophage, and T cell CD8+ were selected.
We downloaded the uniformly normalized pan-cancer dataset: TCGA TARGET GTEx (PANCAN, N=19,131, G=60,499) from the UCSC (https://xenabrowser.net/) database, from which we further extracted the ZCCHC3 gene and 60 genes of two types of immune checkpoint pathways (Inhibitory, Stimulatory, screened samples from: Primary Solid Tumor, Primary Tumor, Primary Blood Derived Cancer - Bone Marrow, Primary Blood Derived Cancer-Peripheral Blood, we also filtered all normal samples and furthermore log2(x+0.001) transformed each expression value, next we calculated the spearman correlation between ZCCHC3 and marker genes of the five immune pathways. Tumor-Immune System Interaction DataBase (TISIDB) (http://cis.hku.hk/ TISIDB/) was utilized to explore the relationships of ZCCHC3 and immune modulators in pan-cancer.
Analysis of single-cell sequencing results from the TISCH database
The tumor immune single-cell Hub (TISCH) (http://tisch. comp-genomics. org/documentation/) is a scRNA-seq database that integrates the single-cell transcriptome profiles of nearly 2 million cells from 76 high-quality tumour datasets for 27 cancers. Single-cell sequencing aims to characterize the similarities and differences between different tumors or within the same tumor at the cellular level. Characterising the tumour microenvironment at single-cell resolution.
The expression levels of ZCCHC3 in immunotherapy patients were compared in TISMO database
The TISMO database (http://tismo.cistrome.org/) contains a large number of homologous mouse model data, including RNAseq data from 605 extracontogenetic samples of 49 homologous cancer cell lines from 23 cancers, 195 of which received cytoplasmic therapy; In addition, we included RNA-seq data from 1,518 in-person samples from 68 homologous mouse models of 19 cancers, 832 of which were from the immunecheckpoint blocking (ICB) study.
TIDE database predicts immunotherapy response
Tumor Immune Dysfunction and Exclusion (TIDE) database (http：//tide. dfci. harvard. edu) integrating large-scale omics data and biomarkers from published ICB trials, non-immunotherapy tumor profiles, and CRISPR screenings. Select the “Biomarker Evaluation” module to compare ZCCHC3 with other published biomarkers. The area undercurve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the predictive performance of the biomarker to the ICB response state.
Pan-Cancer Analysis of the Relationship between the ZCCHC3 Expression and TMB or MSI
The TMB and MSI scores were obtained from TCGA. Correlation analysis between the ZCCHC3 expression and TMB or MSI was performed using Spearman’s method. The horizontal axis in the figure represents the correlation coefficient between ZCCHC3 and TMB or MSI, the ordinate is different types of cancer, the size of the dots in the figure represents the size of the correlation coefficient, and the different colors represent the significance of the P value.
ZCCHC3 expression Analysis in Pan-Cancer
Firstly, we evaluated the expression of ZCCHC3 in TCGA. The results showed that ZCCHC3 was highly expressed in COAD, COADREAD, ESCA, STES, STAD, HNSC, KIRC, LIHC, READ, PCPG, and CHOL, Down-regulated in 4 kinds of tumors, such as LUAD, PRAD, UCEC, and THCA (Figure 1A). On this basis, combining TCGA and GTEx databases revealed that ZCCHC3 was highly expressed in GBM, GBMLGG, LGG, BRCA, LUAD, ESCA, STES, KIPAN, COAD, COADREAD, PRAD, STAD, HNSC, KIRC, LUSC, LIHC, WT, SKCM, BLCA, THCA, READ, OV, PAAD, UCS, ALL, and CHOL 29 tumors, but low in UCEC (Figure 1B). The results of CCLE analysis showed that ZCCHC3 showed inconsistent gene expression levels in various cancer cell lines, SCLC, DLBC, NB, ALL showed relatively high gene expression (Figure 1C). Finally, the TIMER database was used to evaluate the expression of ZCCHC3 in pan-cancer. The results showed that the expression of ZCCHC3 in CHOL, COAD, HNSC, KIRC, LIHC, LUAD, LUSC, PCPG, PRAD, SKCM, and STAD was significantly higher than that in normal tissues (Figure 1D).
Figure 1: Pan-cancer ZCCHC3 expression (A) ZCCHC3 expression in tumor tissues from TCGA database; (B) Pan-cancer expression of ZCCHC3 between tumor tissues from TCGA database and normal tissues from TCGA and GTEx database; (C) mRNA expression levels of ZCCHC3 in various tumor cell lines from CCLE database; (D) ZCCHC3 expression in tumor tissues from TIMER database. The red and blue boxes represent tumor tissues and normal tissues, respectively.*p < 0.05; **p < 0.01; ***p < 0.001 and ****p < 0.0001; ns, not significant.
To assess gene expression levels for all tumor stages, we compared ZCCHC3 expression in patients with stage I, II, III, or IV tumors. Significantly different in LUAD, COAD, COADREAD, KIPAN, KIRC, THYM, PAAD, and KICH (Figure 2A-H).