DOI: https://doi.org/10.21203/rs.3.rs-2361702/v1
Background: Monkeypox is a zoonotic disease caused by monkeypox virus, and most infections cause systemic disease. Tumor patients are susceptible to various viruses, but there are few reports on the effects of genes related to monkeypox virus infection on the tumor microenvironment. Therefore, we need to further explore the expression of genes associated with monkeypox infection in tumor patients and the potential immune mechanisms in order to improve the survival and prognosis of tumor patients.
Methods: The gene expression, genetic variation, mRNA sequencing samples, clinical and methylation datas were from 33 cancer types of TCGG database. Single-cell transcriptome sequencing (scRNA-seq) was used to analyze the activation of monkeypox related genes in the tumor microenvironment. Then, Single sample gene set enrichment analysis (ssGSEA) and immunohistochemistry (IHC) were used in our research. The relationship between monkeypox and gene expression, clinical features, immune microenvironment, TMB and MSI was comprehensively evaluated.
Results: In this study, monkeypox-related genes were found to be closely associated with most tumorigenesis as well as drug sensitivity. Additionally, cellular enrichment pathways suggest that monkeypox is closely associated with invasion, cell cycle, DNA damage and repair. The prognostic value on monkeypox-related genes was evaluated, and it was found that monkeypox is a risk factor. Moreover,monkeypox-related genes are positively associated with immune cells as well as immune checkpoints in most tumors. Analysis of scRNA-seq indicates that monkeypox differs significantly among cells of the tumor microenvironment.
Conclusion: The current study explored the monkeypox-related genes in pan-cancer for the first time and provides new insights. We found that monkeypox-related genes are closely associated with the formation of immune microenvironments and immunotherapeutic efficacy of most tumor, which provides new ideas for the prevention and treatment of monkeypox and tumors.
Monkeypox is a zoonotic disease caused by infection with monkeypox virus. It belongs to the genus orthpoxvirus in the Poxviridae family1. Among them, smallpox and monkeypox are common members of the genus orthopoxvirus. The symptoms of monkeypox infection are similar to those of smallpox but are mild2. Since the outbreak of monkeypox in May 2022, monkeypox epidemic statistics released by the World Health Organization as of August 31, 2022, show that the cumulative number of confirmed cases worldwide has reached 50,496, and the cumulative number of deaths is 16. Monkeypox virus has been designated a Public Health Emergency of International Concern (PHEIC) by WHO on 23 July 20223. Monkeypox has become a serious global health concern after COVID-19.
Monkeypox is mainly transmitted to humans through animal reservoirs. Sporadic outbreaks of infection with a history of contact with wildlife have previously been reported in Africa4, but travel-related cases of human-to-human transmission without a history of contact with wildlife are rare5,6. However, the current monkeypox outbreak has shown some unusual transmission features, such as sustained human-to-human transmission among men who have sex with men7. In a prospective study of 181 cases of monkeypox in Spain, clinical presentation and virology were evaluated and 92%(166) of the patients were gay or bisexual men or other men who have sex with men (MSM), in contrast to only 8% (15) of heterosexual patients8. Even 18% of infected patients had been vaccinated against smallpox, suggesting that the smallpox vaccine does not completely protect against monkeypox infection. In addition, a study of monkeypox infections in 16 countries from April to June 2022 also showed that 95 percent of the 528 reported infections were suspected to have been transmitted through sexual activity9. Among them, the finding of primary genital, anal, and oral mucosal lesions support the strong possibility of sexual transmission, which may represent the earliest invasion site of monkeypox virus. The emergence of human-to-human transmission has threatened patients with low immunity living with cancer and other diseases in different degrees around the world. Although it is known that most people infected with monkeypox develop fever, and lymphadal enlargement, skin rashes on the face, arms, legs, and less frequently on the palms, soles of the feet, or genitals10–12. With some complications, our knowledge of monkeypox remains limited, and different viral branches cause different patterns of disease in individuals. What are the most severe symptoms it causes, the duration of the infection and its potential influencing factors, potential complications, and the mechanisms that induce it? These are things that we need to continue to explore.
Cancer patients are susceptible to a variety of viruses, and tend to develop into severe disease after infection, with poor prognosis. In addition, cancer patients have a low immune system, both the tumor itself and the treatment can weaken the immune system. Cancer patients are susceptible to a variety of viruses, and tend to develop into severe disease after infection, with poor prognosis. The association between monkeypox and tumors has not been reported. Little is known about the effect of genes associated with monkeypox virus infection on the tumor microenvironment and the correlation between tumor characteristics. Therefore, it is of great clinical significance to identify the gene expression and clinical characteristics associated with monkeypox infection in 33 cases of solid tumors for survival of patients with monkeypox.
Collection of Kidney renal clear cell carcinoma Samples.
Two KIRC samples were collected from patients undergoing radical nephrectomy at The First Affiliated Hospital and Affiliated Tumor Hospital of Guangxi Medical University. This study was approved by the Institutional Review Board (IRB) of The First Affiliated Hospital Guangxi Medical University, and all the patients signed the informed consent.
Sample Procurement and Single-Cell Isolation.
The preparation for single-cell suspension was described in the previous study. Fresh samples were washed with Dulbecco's phosphate-buffered saline (DPBS; WISENT, 311-425-CL) at 4°C and cut into 2–4 mm pieces using sterile scissors. The tissue pieces were resuspended in pre-chilled DPBS and washed twice. The tissue pieces were resuspended in pre-chilled DPBS, washed twice and digested in HBSS digestion solution with gentle agitation for 30 min. Subsequently, the suspended cells and tissue fragments were passed through a 70 µm cell filter (Falcon) with 5 ml of red blood cell lysis buffer (10X diluted to 1X; BioLegend, 420301) on ice for 5 min to remove red blood cells (RBCs), followed by filtering the cells through a 40 µm cell filter. Subsequently, the cells were centrifuged at 300g for 5 minutes and washed twice with DPBS. Finally, the cells were resuspended in DPBS containing 1% FBS. Single cell suspensions were obtained and viability was calculated by staining with trypan blue (Gibco, 15250-061). If cell viability was higher than 80%, 10x genomics samples were processed.
ScRNA-Seq Processing and data analysis.
All samples were sequenced using Hiseq X10 (Illumina, San Diego, CA) with standard parameters. Preliminary sequencing files (.bcl) were converted to FASTQ files on CellRanger (version 3.0.2) R (version 3.5.2) and Seurat R package (version 3.1.1) were used for QC and secondary analysis.
We performed gene expression analysis of tissues from healthy people by the Genotype-Tissue Expression (GTEx) dataset (V7.0)( Genotype-Tissue Expression Project (GTEx) (genome.gov))13. Based on 33 human cancers, the TCGA (https://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas) database holds clinical and genetic data on more than 10,000 tumor samples. Through the TCGA database, we collected CNV, SNV, single-cell sequencing, methylation and other tumor-related data. Selecting differentially expressed genes from TCCG, we selected FOS genes for the study.
The R package "limma"( limma package - RDocumentation) was used to normalize FOS gene expression data obtained from the TCCG database14. We used median RSEM value to classify tumor samples into high and low gene expression groups. Using the R package "survival"(https://www.rdocumentation.org/packages/survival), we performed an analysis of differences in survival across different cancers, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI)15. We also built the Cox proportional hazards model through the R package16. Gene data with Kaplan-Meier log-rank test p-values < 0.05 were retained.
Through the TCCG database, we acquired and studied SNV data for 33 cancers. The SNV waterfall chart was generated by maftools(maftools : Summarize, Analyze and Visualize MAF Files (bioconductor.org))17. The percentage of SNVs in each gene is equal to the number of mutation samples divided by the number of cancer samples. Then we combined the SNV data and clinical sample data to analyze the survival difference between mutant and non-mutated genes through the R package.
Using GISTICS 2.0(www.genepattern.org/modules/docs/GISTIC_2.0), we collected raw data from 33 tumors and divided these data into homozygous and heterozygous subtypes. Homozygous and heterozygous CNV profiles represent the percentage of homozygous, heterozygous, and CNV amplifications, deletions in each gene for each cancer. Next, we used GISTIC-processed CNV data for percentage calculations of CNV subtypes, and only genes with > 5% CNV were considered significant. In addition, we merged mRNA expression data with CNV data via a sample’s TCGA barcodes18.
We analyzed methylation data from corresponding tumor and adjacent normal tissue samples from 14 cancers. mRNA expression and methylation data were combined with TCGA barcodes, and their associations were tested based on a Pearson product–moment correlation coefficient and t-distribution19. We then combined gene methylation and clinical overall survival data and divided them into two groups based on intermediate methylation. In addition, we used the Cox coefficient to estimate the risk of death, and Hyper-worse was defined as high risk if the Cox coefficient was less than 0 and the hypermethylated group exhibited poor survival prognosis.
We calculated the monkeypox score based on the single-sample gene-set enrichment analysis (ssGSEA) using the monkeypox-related gene set downloaded from the GSEA database to quantify the expression levels of monkeypox-related genes for each cancer20.
We performed immune cell integration and evaluation of mRNA sample sequences from TCCG via the TIMER (timer.cistrome.org) and GEPIA database(gepia.cancer-pku.cn)21,22. Additionally, we analyzed the association of monkeypox-related genes with tumor stromal cells and tumor-infiltrating immune cells using Immune Cell Infiltration Analysis TIMER2.
To explore more deeply the functions and pathways of monkeypox in pan-cancer, we searched different databases. To avoid the limitation of tumor heterogeneity, CancerSEA database (CancerSEA - Database Commons (cncb.ac.cn)) was used to searched distinct functional states of specific genes in different cancer types of the single-cell level. In addition, the CancerSEA database was used to analyze the correlations between the monkeypox and functional states in various cancers22.
Through the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, we predicted potential ICB response23. TIDE can be used to model and evaluate two mechanisms of tumor immune escape. The higher the TIDE score, the higher the probability that tumor cells evade recognition and attack by the body's immune system through various mechanisms.
Spearman correlation test was used to evaluate the correlation of monkeypox-related gene expression. We estimated the prognostic significance of the indexes by Kaplan-Meier survival curves and compared by a log-rank test24. The hazard ratio (AHR) was calculated using the Cox proportional hazards model and the R (version 3.4.4) was used to perform all statistical analyses. And it was considered statistically significant when P < 0.05.
Overall, FOX and EGR1 were significantly low expressed in 14 tumors. In particular, all genes showed significant low expression in BRCA. HMGA2 was significantly highly expressed in LUSC, LUAD, HNSC and THCA (Fig. 1A). Investigation of polygenic expression in different cancers revealed that SMOX, ARC and GFAP were highly expressed in ESCA and LGG (Fig. 1B).
To detection of frequency and type of variation for each cancer subtype, the monkeypok related genes SNP data were analyzed. This analysis included 1507 tumor samples, and reveal that the SNV frequency of related genes were 100% (1507cases). The SNV frequency of DUOX1, IL4R, ZC3H12C, PTGS2, SPRED1, EGR2, ETV5, PLK3, EGR1, and SMOX were 12, 10, 9, 8, 7,7,7,6,6 and 6%, respectively (Fig. 2A). SNV percentage analysis showed that higher frequency of mutations in multiple monkeypox-related genes in UCEC, SKCM and COAD. DUOX1, EGR2, SPRED1, SMOX, ZC3H12C, IL4R, EGR1, PLK3, USP12 were 41, 34, 32, 30, 29, 26, 25, 25, 25% in UCEC respectively. IL4R, DUOX1, TRIM15, PTGS2, HS3ST1 were 28, 26, 23, 22% in SKCM respectively. DUOX1, EGR2, ZC3H12C, ETV5 were 18, 16, 14, 12% in COAD respectively (Fig. 2B). By evaluating CNV mutation data for monkeypox-related genes in 33 tumors, we found heterozygous amplification or heterozygous deletions were the main mutations. We also observed ARC, RGS16, PTGS16, SMOS and ETVS heterogeneous amplification of the CNV in most tumors. Heterozygous amplification of ARC, RGS16, PTGS16, SMOS was greater than or equal to 50% in UCS and LUSC. In addition, heterozygous deletion of USP12, AREG, ZC3H12C, EGR2, CXCL1, CXCL2, H3S3HT1, SPRY2, SPRY4 and EGR1 was observed more than 50% in TGCA. The amplification and deletion of PRAD, LAML, THCA and THYM tumors were less in 33 tumors (Fig. 2C).
To investigate the epigenetic methylation levels of monkeypox-related genes, we performed an analysis of methylation levels. DUSP5, CXCL2, IL4R were hypomethylated in KIRC. And HMGA2, TRIM15 methylation levels were low in UCEC. In addition, DNAJA4, HBEJF, PHLDA1, SPRY2, PTGS2 methylation levels were high in PRAD. PHLDA2, SPRY2, PTGS2 were hypermethylated in BRCA. PHLDA2, SPRY2 and PTGS2 methylation levels were significantly higher in BRCA (Fig. 3A). As a whole, monkeypox-related genes had a negative correlation between methylation level and mRNA expression (Fig. 3B).
We performed analysis of drug sensitivity and mRNA expression of monkeypox-related genes. Almost all genes were resistant to FK866, GSK1070916, MP470, CX-5461, 5-Fluorouracil and MPS-1-IN-1, except RGS16 and ARC which were sensitive to these drugs. In addition, CXCL1 and CXCL2 were sensitive to XAV939 (Fig. 4).
FOS and EGR1, HBEGF, EGR2, EEGR1 and HUEGF, EGR2, CACL1 and CXCL2, EREG, AREG and EREG, IE3, LIF and IER3, were significantly and positively correlated overall (Fig. 5A). Monkeypox scores were higher in BLCA, CESC, COAD, ESCA, GBM, KIRC, LGG, OV, PAAD, READ, SKCM, STAD, TGCT, THCA, UCEC, UCS and lower in ACC, BRCA, KICH, LAML, LUAD, LUSC, PRAD (Fig. 5B). The monkeypox score of stage Ⅲ-Ⅳ was significantly higher than stage Ⅰ-Ⅱin TGCT and THCA. Except for TGCT and THCA, there was no significant difference in monkeypox score at different stages in other tumors (Supplementary Fig. 1).
By analyzing monkeypox-related genes and OS, we found it is a risk factor in ACC (P = .014), ESCA (P = .003), LGG (P < .001), THCA (P = .027), BLCA (P = .029), GBM (P = .002), LIHC (P = .01), THYM (P = .018), HNSC (P = .001), LUAD (P = .003), CESC (P < .001), LUSC (P < .001), UVM (P = .004), STAD (P = .012), LAML (P = .002) and PAAD (P = .001) (Supplementary Fig. 2). For DSS, the monkeypox-related genes score was considered as a risk factor in ACC (P = .018), ESCA (P < .001), GBM (P = .002), LIHC (P = .02), HNSC (P = .009), LUAD (P = .026), UCEC (P = .01), CESC (P < .001), LUSC (P < .001), CHOL (P = .042) and UVM (P = .005) (Supplementary Fig. 3). By analyzing monkeypox-related genes and PFI, we found it is a risk factor in ACC(P = .005), LGG(P < .001), GBM(P < .001), THYM(P = .049), HNSC(P = .012), LUAD(P = .013), CESC(P = .002), LUSC(P < .001), SARC(P < .012), UVM(P = .039), OV(P = .02), and PAAD(P < .001), TGCT(P < .016) (Supplementary Fig. 4). By OS analysis, we found that monkeypox scores had significantly positive relation with OS in BRCA, COAD, PCPG, PRAD, READ (Fig. 6A). The prognostic value of disease-specific survival(DSS)on monkeypox-related genes was evaluated, and it was found that DSS was significantly associated with poor prognosis in 10 of 33 tumors (Fig. 6B). Besides, monkeypox scores were also better significantly related with better PFI in PCPG, PRAD, READ (Fig. 6C).
By functional analysis at the single cell level, we found that Monkeypox scores were associated with inflammation, quiescence, and hypoxia in most tumors, with a strong correlation in UM, suggesting that Monkeypox methylation scores may be an important factor in tumor metastasis and invasion. We observed that stemness, cell cycle, DNA repair, DNA damage, invasion, proliferation, inflammation, quiescence, EMT, hypoxia, apoptosis, differentiation, angiogenesis, and metastasis in UM promoted Monkeypox function. Stemness, proliferation, inflammation, quiescence, EMT, hypoxia, apoptosis, differentiation, angiogenesis, and metastasis that in RB promote Monkeypox function. Monkeypox function is promoted in most tumors in different cellular functions, however still inhibited in some aspects, such as stemness, cell cycle, DNA repair, DNA damage, invasion, proliferation inhibit Monkeypox function in NSCLC (Fig. 7).
By calculating the abundance of 22 immune cells, we explored the relationship between Monkeypox and tumor immune cells and found that Monkeypox was significantly positively correlated with mast cell resting, macrophage M0, myeloid dendritic cell activated, neutrophil, macrophage M2, T cell CD4 memory resting, and monocyte. In addition, Monkeypox was significantly negatively correlated with activated T cell CD4 naive, B cell memory, and mast cell activated (Fig. 8A). The monkeypox score has significantly positive relationship with monkeypox immune score in GBM, KICH, ACC, PCPG, LGG, LAML, SARC, KIRP, LIHC, LIAD, PRAD, THCA (Fig. 8B). However, monkeypox score was significantly negatively correlated with microenvironment score in STAD, THYM, CESC, HNSC, TGCT and CHOL (Fig. 8C). Meanwhile, monkeypox score was significantly negatively correlated with stroma score in HNSC and THCA (Fig. 8D).
Most immune checkpoints were positively correlated with monkeypox-related genes in most tumors, particularly TNFSF9, TNFSF15, CD276, CD44, CD274, CTLA4, TNFRSF8, NRP1, C10orf54 (Fig. 9A). Monkeypox related genes were positively correlated with most of Immune activating genes especially in IL6, TMEM173, NT5E, PVR and CD276 in most tumors (Fig. 9B). In the immunosuppressive gene correlation analysis PVRL2, IL10RB, TGFBR1, PDCD1LG2, KDR, TGFB1, IL10 and CD274 were found to be positively correlation with monkeypox related genes. But negative correlation with LAG3, CA160, BTLA, BTLA (Fig. 9C). The expression of chemokine was positively correlated with monkeypox related genes in general. And chemokines CXCL3, CXCL2, and CXCL8 were significantly positively correlated with monkeypox related genes in most tumors (Fig. 9D). We observed that chemokine receptors were positively correlated with monkeypox related genes in most tumors overall, and CXCR2, CXCR1, CXCR4, CCR4, CCR1 expression significantly correlated with in most tumor types in testing chemokine receptor expression in tumors (Fig. 9E).
Monkeypox was significantly negatively correlated with TMB in DLBC only, and significantly positively correlated with TMB in most tumors (Fig. 10A). The expression of monkeypox related genes was positively correlated with MSI in PCPG, LAML, KIRC, COAD, THYM, BRCA, KIRP and negatively correlated with GBM, CESC, LIHC, ESCA, TGCT, PRAD, LUSC (Fig. 10B). The monkeypox score has significantly positive relationship with monkeypox infection related genes expressing in CESC, OV.M, KIRC, PAAD, STAD, BRCA, HNSC and LUSC (Fig. 10C). TIDE correlation analysis show that, the expression of monkeypox infection related genes was significantly and positively associate with TIDE score in OV.R, BRCA, HNSC, PAAD, CESC, KIRC, LUSC, OV.M and STAD nine cancers ( Supplementary Fig. 5).
The results obtained reveal the diversity of KIRC cell types. To investigate the Monkeypox fraction in KIRC tumor microenvironment cells, we used ssGSEA to calculate and compare the differences in Monkeypox fraction in different cell types (Fig. 11A). We observed a significant difference in the expression of marker genes in different cell clusters (Supplementary Fig. 7). We used ScRNA-seq to detect 2 KIRC samples. Then, Seurat was analyzed by containing 13124 high quality single cell transcriptome information after quality control. Cell clustering analysis based on the tSNE algorithm revealed 11 clusters of monocyte 1, monocyte 2, KIRC1, macrophage, KIRC2, KIRC3, CD4 + T cells, mast cells, CD8 + T cells, NK cells and endothelial cells (Fig. 11B). The results obtained reveal the diversity of KIRC cell types. To investigate the Monkeypox fraction in KIRC tumor microenvironment cells, we used ssGSEA to calculate and compare the differences in Monkeypox fraction in different cell types (Fig. 11C). By analyzing the Monkeypox scores of the above 11 cells, we found significant differences between them. The results showed that Monkeypox scores were significantly higher in macrophage within KIRC2 and KIRC3 and significantly lower in CD4 + T cells, and Monkeypox scores were significantly different within different KIRC cell clusters, suggesting that Monkeypox scores reveal potential features of KIRC. Also, this suggests that Monkeypox is significantly different in different cells of the KIRC tumor microenvironment (Fig. 11D).
PPI network construction showed FOS is the hub gene of monkeypox related gene (Supplementary Fig. 8A). Prognostic analysis revealed monkeypox-related genes as risk factors. Therefore, to verify that similar results existed at the protein level, we performed a protein level analysis. Data from the human protein atlas also showed low expression of the FOS gene in colon adenocarcinoma (COAD), stomach adenocarcinoma (STAD) and Lung squamous cell carcinoma (LUSC), which is consistent with our previous expression analysis (Supplementary Fig. 8B-G), which was consistent with our expression analysis.
Monkeypox is an important orthopox virus that emerged after smallpox virus was radically eliminated, but its clinical symptoms are milder than those of smallpox. Monkeypox has a high risk because it has a mortality rate of about 10% and can cause major public health diseases25.In a review by John G Rizk et al, it was mentioned that monkeypox can be transmitted by direct transmission or indirect transmission of infectious agents, sexual transmission is the route of transmission found in many cases, and in sexual transmission males with homosexual sex account for the majority, and animals infected with monkeypox have the possibility of transmitting the virus to humans26, thus showing that monkeypox is highly infectious and can be prevalent in different countries and regions. The characteristic rash and swollen lymph nodes can be observed to varying degrees when infected with monkeypox, with papules and blisters as symptoms of further development of the rash, which eventually crusts over and heals. Other symptoms of monkeypox include fever, muscle pain, and sore throat, and its onset has a high rate of similarity to certain orthopox viruses, such as chickenpox. Most patients infected by the monkeypox virus recover on their own, but younger children and patients with immune system disorders or cancer may need to involve a series of targeted treatments. Complications of monkeypox treatment, such as bronchopneumonia, encephalitis and corneal infections, can be serious and life-threatening27.
In recent years, there has been an increasing trend in the geographic area and rate of transmission of monkeypox, which is accompanied by chronic death of cancer patients, with varying degrees of altered morbidity, mortality, and clinical symptoms during transmission. Therefore, when conducting treatment for monkeypox, health care providers need to consider the impact of cancer in terms of prognosis and survival of monkeypox, and implement into the prevention and treatment of monkeypox in combination with cancer interventions and related research methods. In this study, we explored the relationship between monkeypox-related genes and cancer and tumor immunity through prognostic survival analysis, immune analysis, single-cell level analysis, and construction of monkeypox scores.
First, we analyzed the differential expression of monkeypox-related genes in cancerous and paracancerous tissues in combination with prognostic analysis, which showed that one or more related genes were differentially expressed in 14 tumor types, with HMGA2 significantly highly expressed in LUSC, LUAD, HNSC and THCA, and SMOX, ARC and GFAP in both ESCA and LGG were highly expressed. Analyzing genetic variation by CNV and SNP data of monkeypox-related genes, we found that heterozygous amplification and heterozygous deletion were the main types of mutations in CNV. Methylation distinguishes cell types by gene expression and genomic stability, and imbalance of this mechanism may lead to cancer28. Therefore, we analyzed the methylation levels of monkeypox-associated genes and their correlation with expression. Monkeypox-associated genes had significantly higher methylation levels in most tumors, and most methylation levels were negatively correlated with mRNA expression. According to a new review published by Nature Reviews Immunology on September 5, 2022, there is no specific treatment for monkeypox, except for Tecovirimat and Brincidofovir, which are currently being developed for smallpox. And the efficacy of these two therapies against monkeypox is based only on preclinical trials29. Finding drugs that are sensitive to genes associated with monkeypox infection is important for people with low immunity, such as cancer patients. By drug sensitivity analysis, we found that almost all genes were resistant to FK866, GSK1070916, MP470, CX-5461, 5-fluorouracil and MPS-1-IN-1, and only RGS16 and ARC were sensitive to these drugs. Most monkeypox infection genes were sensitive to XAV-939, which is an effective tankyrase inhibitor targeting Wnt/β-catenin pathway. This pathway has been confirmed to play an important role in rectal cancer and breast cancer, and TRPM8 and FAM96A/FAM96B are important regulators. Overexpression of TRPM8 and overactivation of the Wnt/β-catenin pathway are associated with lower survival in colorectal cancer patients30. On the contrary, overexpression of FAM96A/B inhibited the activation of Wnt/β-catenin pathway, inhibited the proliferation, invasion and migration of breast cancer cells, arrested cell cycle and promoted cell apoptosis31. The drug also has good sensitivity to FOX, the core gene of monkeypox infection, and has great potential as a potential effective drug for the treatment of cancer patients infected with monkeypox.
In addition, single-sample gene set enrichment analysis (ssGSEA) was used to construct the monkeypox score, and its relationship with tumor stage, immunization and prognosis of related treatments was discussed. In the analysis of the difference of monkeypox score in different stages of tumor, it only showed the difference in TGCT and THCA. In the correlation analysis of immune-related genes, The correlation analysis of monkeypox score with immune cell and stromal cell score and tumor infiltrating immune cells showed that the monkeypox score was inversely correlated with the two scores and tumor microenvironment score, tumor NK.Cell.active, T.cel.CD 8, B.Cel.Menmory and other immune activated cells infiltration in HNSC. Tumor microenvironment plays an important role in tumor growth. The growth of fibroblasts, stromal cells and endothelial cells can promote the growth of tumor-associated capillaries and lymphatics, thereby supporting tumor growth32. At the same time, the tumor microenvironment impairs the antitumor function of immune cells through a variety of mechanisms, including destabilizing the innate cell compartment composed of NK cells, macrophages, neutrophils, and dendritic cells (DC)33. Studies have shown that immune cells and stromal cells, as two major non-tumor components in the tumor microenvironment, are of great value in the diagnosis and prognosis evaluation of tumors. In addition, high monkeypox infection gene expression was positively correlated with high TIDE, MSI and TMB in KIRC and BRCA immunotherapy evaluation index analysis. Combined with the results, high monkeypox score may be an indicator of poor prognosis in HNSC immunotherapy and good prognosis in KIRC and BRCA.
We first performed a comprehensive analysis of genes associated with monkeypox infection and demonstrated their effects in pan-cancer by data stream analysis. As part of the data in this study are derived from open source tumor data sets, the reliability of data of various cancer types cannot be completely consistent. Although this study demonstrated that some monkeypox infector-associated genes were associated with tumor progression, unfortunately, we did not find clear evidence that monkeypox infector-associated genes act as protective or risk factors in pan-cancer. Further mechanistic studies are needed to elucidate the effect of monkeypox infection-related gene expression on specific tumor progression.
In conclusion, we present the first comprehensive analysis of the role of monkeypox-associated genes in pan-cancer. The role of monkeypox-associated genes in pancreatic cancer was revealed by establishing survival models, immune infiltration correlation analysis, as well as methylation and drug sensitivity analyses. The results showed that monkeypox-associated genes were highly expressed in several tumours with significantly higher methylation levels and higher sensitivity to XAV-939. In addition, we used ssGSEA to construct monkeypox scores to investigate their relationship with prognosis and immunity in pancytopenia, and the results suggest that monkeypox-related genes have a relationship with tumor immunity. Also, higher monkeypox scores may serve as markers of prognosis for different tumor immunotherapy. This study explored the relationship between monkeypox and tumors at the genetic level, providing new ideas for the prevention and diagnosis of both.
Author Contributions
Xiaoliang Huang, Xingqing Long, Yanling Liu, Zuyuan Chen, Xiaoyun Xiang, Xianwei Mo, Jungang Liu, Weizhong Tang: conceived and designed the experiments; Xiaoliang Huang, Xingqing Long, Yanling Liu, Zuyuan Chen, Xiaoyun Xiang, Xianwei Mo, Jungang Liu, Weizhong Tang: analyzed the data; Xiaoliang Huang, Xingqing Long, Yanling Liu, Zuyuan Chen, Xiaoyun Xiang, Xianwei Mo, Jungang Liu, Weizhong Tang: helped with reagents/materials/analysis tools; Xiaoliang Huang, Xingqing Long, Yanling Liu, Zuyuan Chen, Xiaoyun Xiang, Xianwei Mo, Jungang Liu, Weizhong Tang: contributed to the writing of the manuscript. All authors reviewed the manuscript.
Funding
Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project(S2021016); Guangxi Natural Science Foundation (2021JJA140081); Basic Research Skills Enhancement Project for Young and Middle-aged Teachers in Universities in Guangxi (2021KY0087)
Data availability
The data supporting the findings of this study are deposited in TCGA and GEO databases. The Single cell sequencing datasets can be found in online repositories of GEO (GSE152938).
Code availability
The analyses methods and used packages are illustrated in the “Materials and methods” section. All other R and Python code and analyses are available from the corresponding author upon request.
Ethics approval and consent to participate
This study was approved by the Ethics and Human Subject Committee of Guangxi Medical University Cancer Hospital
Consent for publication
Not applicable
Conflict of interests
The author reports no conflicts of interest in this work.
Acknowledgments
None