A pan‐cancer analysis of the oncogenic and immunological roles of THOC3 in human cancer

doi:10.21203/rs.3.rs-5112729/v1

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A pan‐cancer analysis of the oncogenic and immunological roles of THOC3 in human cancer

https://doi.org/10.21203/rs.3.rs-5112729/v1

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Background: There is a limited number of studies on THO Complex Subunit 3 (THOC3) in tumors. The purpose of this study is to conduct a comprehensive analysis of various types of tumors to determine the role of THOC3 in tumor progression and to investigate its impact on immunity.

Methods. Retrieved THOC3 expression data from various cancers in the TCGA database and analyzed it using R software (version 3.6.4) and its related packages; explored the differential expression of THOC3 in tumors, its correlation with prognosis, functional enrichment, and its relationship with tumor heterogeneity. The study also aimed to uncover the correlation between THOC3 and tumor immunity.

Results. THOC3 is differentially expressed in various tumors and normal samples, and is correlated with overall survival and progression-free time. The study found that THOC3 expression is strongly associated with tumor mutational burden, microsatellite deletion, and immune response. The expression of THOC3 is significantly correlated with immune cell infiltration, and THOC3 can regulate transcription output and mRNA splicing. Therefore, we speculate that THOC3 could serve as a therapeutic target for future anticancer therapies.

Conclusions. THOC3 may serve as a novel and specific biomarker for prognosis and immunotherapy.

Biological sciences/Cancer/Cancer genomics

Biological sciences/Cancer/Lung cancer

Biological sciences/Cancer

Health sciences/Oncology

THOC3

Pan-cancer biomarker

prognosis

immunity

bioinformatics

Cancer is a leading cause of premature death worldwide, with a significant burden in low to middle-income countries ^1,2. Global cancer staistics for the year 2020 indicate 18.1 million new cancer cases diagnosed, a number projected to rise to 28.4 million by 2040 ³. This upward trend reflects issues of aging and growing populations, and also suggests that the risk factors people face are changing with socio-economic development. It is known that the dysregulation of transcription programs can make cancer cells highly dependent on certain gene expression regulators⁴. Recent research has found that post-transcriptional mRNA export plays an important role in cell proliferation and differentiation, and its dysfunction can lead to the onset of cancer ⁵. Therefore, investigating mRNA export factors in tumor cells can pave a new way for anti-cancer treatment.

In eukaryotes, gene expression begins with transcription, followed by RNA processing, and mature RNA is transported to the cytoplasm under the action of mRNA export factors through the NXF1-NXT1 receptor ⁶. mRNA export factors maintain the nuclear export dynamics of mRNA and the homeostasis of its nuclear decay, and their dysfunction can lead to the occurrence and development of cancer ⁷, which is consistent with the research results of Penghui Xu et al. ⁸. The Transcription-Export (TREX) complex is an important mRNA export factor that can bind to the NXF1 protein. The THO complex (THOC), which is part of the TREX complex, is composed of six subunits (THOC1~3 and THOC5~7) and is a conserved RNA-binding complex in the human body. It is closely related to the prevention of DNA damage, RNA transcription, mRNA processing and export, embryonic development, and cell differentiation in specific adult tissues ^9–11. The dysfunction of the THO complex is closely related to the regulation of cancer cell cycle, DNA repair, and replication, which can increase the invasiveness of cancer cells, therapeutic resistance, and lead to a shortened survival period for cancer patients ^12–14.Current studies have reported the role of THO Complex Subunit 3 (THOC3) in cancer, with research indicating that knocking down the expression of the THOC1 gene can inhibit the proliferation of triple-negative breast cancer and liver cancer cells ^15,16. THOC2 increases the invasion and progression of melanoma through the cAMP signaling pathway ¹⁷. THOC5 has become a tool for identifying cancer therapeutic target genes¹⁸. The loss of multiple THOC5 target genes synergistically induces the death of HCC cells ¹⁹. Moreover, the interaction between THOC2 and THOC5 can regulate the transcription of SOX2 and NANOG, thereby modulating the stemness and radiosensitivity of triple-negative breast cancer²⁰. The differential expression of THOC6 in glioma cells is associated with prognosis²¹; high expression of THOC7 promotes the progression of cutaneous squamous cell carcinoma ²². However, there is a lack of comprehensive research on the correlation between THOC3 expression and various types of tumors in the academic community. Recent studies have found that the binding of THOC3 to YBX1 can promote the transcription of PFKFB4 mRNA, increasing the growth and migration of LUSC cells ²³. To address this gap, we conducted a pan-cancer analysis using data from The Cancer Genome Atlas (TCGA). Our research results indicate that THOC3 shows significant prognostic value in a series of tumors and has the potential to become a biomarker.

In this study, we conducted a comprehensive assessment of THOC3 differential expression, prognostic value, tumor instability, and its immune correlation in various cancer types. This was achieved by leveraging data from The Cancer Genome Atlas (TCGA) and employing diverse bioinformatics methods. We also experimentally validated that THOC3 protein expression is significantly higher in lung adenocarcinoma cells compared to normal cells. The aim of this study is to explore the potential mechanisms of THOC3 in different cancers.

2.1 THOC3 Expression Analysis in Different Tumors

TIMER 2.0 (http://timer.cistrome.org/,accessed on October 1, 2024) is a resource primarily dedicated to analyzing gene expression differences in tumors²⁴. It permits users to compare gene expression levels across various tumor types or subtypes. We downloaded a unified, normalized pan-cancer dataset from the UCSC database (https://xenabrowser.net/,accessed on October 1, 2024), which includes TCGA, TARGET, and GTEx data. From this dataset, we extracted the expression data of the THOC3 gene in various samples. To further analyze each expression value, we performed a log2(x + 0.001) transformation and subsequently excluded cancer types with fewer than three samples per cancer type. Finally, we obtained expression data for 34 cancer types and conducted differential significance analysis using non-paired Wilcoxon Rank Sum and Signed Rank Tests.

Using the University of Alabama at Birmingham Cancer (UALCAN) (https://ualcan.path.uab.edu/analysis-prot.html)database, we analyzed the expression levels, phosphorylation status, and promoter methylation of THOC3 protein in various cancers²⁵. Subsequently, the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database (http://gepia2.cancer-pku.cn/#analysis,accessed on October 1, 2024) was utilized to investigate the correlation between THOC3 gene expression and tumor staging²⁶.

2.2 THOC3's Relationship with Survival in Different Tumors

Data from the unified and standardized TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499) were downloaded from the UCSC database. From this dataset, we extracted the expression data of the THOC3 gene across various samples. We further selected samples derived from Primary Blood Derived Cancer - Peripheral Blood (TCGA-LAML), primary tumors, and metastatic samples from TCGA-SKCM. Additionally, we obtained a high-quality TCGA prognosis dataset published previously in Cell and excluded samples with a follow-up time of less than 30 days ²⁷. Each expression value was further transformed using the log2(x + 0.001) function. Finally, we excluded cancer types with fewer than ten samples each, resulting in data from 39 cancer types. Survival data were obtained through the log-rank test.

2.3THOC3 Gene Alterations and Immune Infiltration Analysis

cBioPortal (https://www.cbioportal.org/, accessed on October 3, 2024) is a resource capable of analyzing gene alteration frequencies, types of alterations (mutations, amplifications, multiple alterations), and mutation sites²⁸. The ImmuneScore scores from the SangerBox website (http://sangerbox.com/Tool, accessed on October 3, 2024)²⁹ were used to calculate the expression of THOC3 in immune cell infiltration across 39 types of tumor stroma using Spearman's correlation coefficient. Additionally, the study explored the Pearson correlation between the expression of THOC3 and immune checkpoint genes (ICPGs), as well as TME biomarkers Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), and Homologous Recombination Deficiency (HRD).

2.4 THOC3 Analysis with Immune Cells

Using the Timer method on the SangerBox website (http://sangerbox.com/Tool, accessed on October 3, 2024)^29,30, we recalculated the Spearman's correlation coefficient between the THOC3 gene expression and immune cell infiltration scores, assessing the infiltration scores of B cells, T cells CD4, T cells CD8, Neutrophils, Macrophages, and DCs in each tumor for every patient.

2.5 Gene Enrichment Analysis of THOC3

GeneMANIA (http://genemania.org/) is a freely accessible database and analysis tool that predicts gene functional associations by integrating diverse data sources^31,32. This tool is utilized to identify the top 20 genes most correlated with THOC3 expression. The DAVID database (https://david.ncifcrf.gov/tools.jsp) is a bioinformatics resource offering comprehensive functional annotation for large-scale gene or protein lists³³. It facilitates functional enrichment analysis, including Gene Ontology (GO) analysis for biological processes (BP), cellular components (CC), and molecular functions (MF). Additionally, it conducts pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, aiding in the exploration of THOC3's functional enrichment.

2.6 Analysis of the THOC3 Expression and Drug Sensitivity

The CellMiner database (https://discover.nci.nih.gov/cellminer/loadDownload.do) is a resource dedicated to investigating the relationship between gene expression, including that of THOC3, and drug sensitivity³⁴. It provides RNA-seq expression data and drug activity information, and utilizes R software (version 4.3.2) along with R packages such as 'limma', 'impute', 'ggpubr', and 'ggplot2', facilitating the analysis of clinically tested and FDA-approved drugs.

2.7 Differential Analysis of THOC3 in Lung Adenocarcinoma

We downloaded standardized TCGA lung adenocarcinoma (LUAD) data from the UCSC database, obtaining 515 tumor samples and 59 normal tissue samples. We conducted differential analysis, univariate COX regression analysis, and survival analysis on 57 samples that had both tumor and normal tissues. Furthermore, we selected four sets of human lung adenocarcinoma and adjacent normal tissue specimens from the Department of Thoracic Surgery at the Fourth Hospital of Hebei Medical University for Western blot analysis and semi-quantitative evaluation of IHC. Using GAPDH protein as an internal reference, we verified the expression of THOC3 protein in LUAD and normal tissues.

3.1 Differential Expression of THOC3 in Cancers

This study meticulously analyzed THOC3 mRNA expression of the TCGA databases, revealing pronounced upregulation in 13 cancers: BLCA,BRCA, CESC, COAD, CHOL, ESCA, HNSC, LIHC, LUAD, LUSC, READ, STAD, UCEC. In contrast, it was downregulated in KICH(Fig. 1A). Due to the limited availability of normal samples in the TCGA database, we integrated data from both TCGA and GTEx databases. This allowed us to assess the variability in THOC3 expression across 34 cancer types, ensuring a comprehensive analysis(Fig. 1B). Moreover, the UALCAN database helped us determine THOC3 protein expression levels in LUAD、LUSC、OV、COAD、GBM、KIRC and LIHC, all of which showed high expression(Fig. 1C). Furthermore, we utilized the GEPIA2 database to investigate the correlation between THOC3 expression levels and disease staging, revealing significant associations in SKCM, KIRP, TGCT, and COAD (p < 0.05, Fig. 1D).

3.2 Prognostic Value of THOC3 in Cancer

This study comprehensively assessed the impact of THOC3 expression levels on the prognosis of 33 types of cancer. In the overall survival (OS) analysis, high expression of THOC3 was found to be associated with poor prognosis in GBMLGG, LGG, BRCA, KIRP, SKCM-M, SKCM, PAAD, UVM, and KICH, while low expression of THOC3 was associated with poor prognosis in KIRC and OV (p < 0.05, Fig.2A). The Progression-free interval (PFI) analysis showed that high expression of THOC3 was associated with poor prognosis in GBMLGG, KIRP, KIPAN, LIHC, MESO, UVM, and KICH (p < 0.05, Fig.2B).

3.3 Alteration of THOC3 Gene Analysis Data

Using the pan-cancer data from the cBioPortal database, amplification of the THOC3 gene was found to be the predominant form of mutation, occurring most frequently in Endometrial Carcinoma at a rate of 10%(Fig.3). Other cancer types with notable amplification rates include Pancreatic Adenocarcinoma at 9.09%, Lung Cancer at 7.89%, Renal Cell Carcinoma at 6.45%, Hepatobiliary Cancer at 5.59%, and Cervical Cancer at 5%. Notably, Head and Neck Cancer presented a elatively lower rate of deep deletions, at 3.57%. Collectively, these data indicate that amplification of the THOC3 gene is a common genetic alteration in the cancerous contexts analyzed.

3.4 The Correlation of THOC3 Expression with Immune Cell Infiltration and TME in Different Cancers

According to the results of Figure S1, the expression of THOC3 is closely related to stromal cell infiltration in 23 types of cancers. The expression of THOC3 is significantly negatively correlated with 22 types of cancer and significantly positively correlated with KIPAN. To assess anti-tumor immunity, the relationship between THOC3 and TMB, MSI, and HRD was explored in all cancers (Fig. 4). In GBM, GBMLGG, LUAD, STES, STAD, PRAD, MESO, READ, and PCPG, the expression of THOC3 is positively correlated with TMB, while in LUSC, the expression of THOC3 is negatively correlated with TMB. For STES, SARC, and STAD, the expression of THOC3 is positively correlated with MSI, while in GBMLGG, KIPAN, and PRAD, the expression of THOC3 is negatively correlated with MSI. Additionally, in GBM, LUAD, BRCA, ESCA, STES, SARC, KIRP, KIPAN, PRAD, HNSC, KIRC, LUSC, LIHC, OV, and KICH, the expression of THOC3 is positively correlated with HRD.

3.5 Association between THOC3 and immune checkpoint.

In a variety of cancer types, THOC3 expression consistently correlates positively with immune checkpoint (ICP) genes, observed in cancers including LAML, KICH, KIPAN, KIRC, OV, and PAAD; in contrast, a negative correlation is evident in LUSC and THCA. More than 90% of cases involving the ICP genes CD276, VEGFA, and HMGB1 show a correlation with THOC3 expression (Fig. 5). These observations imply that elevated THOC3 expression could predict the effectiveness of immune therapies targeting ICP genes.

3.6 Analysis of THOC3's Association with Immune Cell Profiles

The analysis of THOC3's impact on immune cells using the TIMER algorithm shows that THOC3 has varying effects on B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage, and DC cell infiltration in different tumors(Figure 6). In KIRC, PRAD, LIHC, and OV, THOC3 promotes the infiltration levels of B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage, and DC cell, while in LUSC, THOC3 significantly inhibits the infiltration levels of B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage, and DC cells(Figure S2).

3.7 Functional Enrichment Analysis of THOC3

We utilized the GeneMANIA database to extract the top 20 genes that exhibited the strongest correlation with THOC3 (Figure 7A). Following, we performed an in-depth analysis of the potential biological functions and pathways linked to THOC3 and these 20 interacting genes utilizing the DAVID database. The enrichment analysis for biological processes (BP) demonstrated that THOC3-related genes were predominantly involved in RNA splicing, mRNA processing, mRNA nuclear export, and the export of viral mRNA from the host cell nucleus (Figure 7B). The enrichment analysis for molecular functions (MF) indicated that THOC3 was involved in protein binding, RNA binding, and mRNA binding (Figure 7B). In the enrichment analysis for cellular components (CC), THOC3-related genes were found to be enriched in the nucleoplasm, cytoplasm, and nucleolus (Figure 7B). Additionally, KEGG pathway analysis revealed the involvement of THOC3 in nucleocytoplasmic transport and spliceosome pathways (Figure 7C).

3.8 Drug Sensitivity Analysis

Using the CellMiner database, we investigated the Pearson correlation between THOC3 expression levels and drug sensitivity. The results indicate that there is no significant correlation between the sensitivity to Allopurinol, Cladribine, Fludarabine, Mithramycin, Amuvatinib, Cpd-401, Econazole Nitrate, ARTENIMOL, artesunate, WORTMANNIN, Depsipeptide, and Cediranib and the expression level of THOC3(Figure S3). However, the sensitivity to Pluripotin and RAF-265 is negatively correlated with THOC3 expression, while the sensitivity to Bisacodyl (the active ingredient of Viraplex) is positively correlated with THOC3 expression(Figure 8).

3.9 Differential Analysis of THOC3 in Lung Adenocarcinoma

Figure 9A contrasts the expression of the THOC3 gene in tumor and normal tissues from the TCGA database. The results indicate that the expression of THOC3 in lung adenocarcinoma tissues is significantly higher than in normal tissues. Through univariate COX regression analysis, we determined that THOC3 significantly impacts lymph node metastasis in LUAD patients and has a significant effect on stage II and stage III LUAD (Figure 9B), but it does not affect the prognosis of LUAD (Figure 9C). To validate the differences in THOC3 expression between tumor and normal tissues, we analyzed four sets of human lung adenocarcinoma and adjacent normal specimens provided by the Department of Thoracic Surgery at the Fourth Affiliated Hospital of Hebei Medical University. Immunoblot analysis confirmed the significant overexpression of THOC3 in lung adenocarcinoma (Figure 9D, E).

In recent years, with the rapid development of personalized medicine, especially the rapid progress in targeted therapy and immunotherapy, significant advances have been made in the field of cancer treatment, which has markedly improved the therapeutic effects for patients with advanced or metastatic cancer³⁵. However, the overall clinical efficacy is still not satisfactory ³⁶. The challenge of treating advanced cancer has prompted researchers to delve into the underlying mechanisms of cancer growth to identify effective therapeutic targets. Research has found that the development of cancer is closely related to the regulation of mRNA export within cells, and the dysfunction of mRNA export factors is considered a potential breakthrough in cancer treatment ⁵. The THOC3 gene is located on chromosome 5 and encodes a protein composed of 351 amino acids. THOC3 plays a key regulatory role in the splicing and recruitment processes of mRNA export, and it is also an important factor in regulating RNA-binding proteins (RBPs), which has been confirmed by existing research ^37–39. In our study, we found that THOC3 can serve as a promising prognostic biomarker, especially in the context of future cancer immunotherapy, which holds potential significance.

This study identified THOC3 expression in 33 types of cancer through various databases, ultimately showing that expression of THOC3 differs between tumor and normal tissues in 14 types of cancer. Among these 14 types, THOC3 expression was upregulated in 13 types of cancer and downregulated in 1 type. Given the differences between protein and mRNA expression levels, we further carefully examined the expression of THOC3 protein in different types of cancer and found that significant increases in THOC3 protein levels were only observed in 7 types of cancer, which is consistent with the validation results of Tao Yu et al. in lung squamous cell carcinoma ²³. Additionally, our study found that THOC3 gene expression significantly affects the clinical staging of SKCM, KIRP, TGCT, and COAD and can serve as a potential predictive factor to guide cancer treatment. Overall survival (OS) and progression-free interval (PFI) analyses both demonstrated that THOC3 expression is significantly associated with the prognosis of DBMLGG, KICH, UVM, and KIRP, which is consistent with the research of Chen Z et al. ⁴⁰. These results indicate that THOC3 significantly impacts the occurrence, progression, and prognosis of cancer and can serve as one of the prognostic indicators.

Gene mutations and aberrant DNA methylation are well-recognized key factors affecting gene expression in cancer, significantly influencing the occurrence and progression of cancer⁴¹. Typically, heritable changes associated with cancer manifest as point mutations, deletions, or chromosomal rearrangements. However, in various types of cancer, the THOC3 gene primarily exerts its oncogenic potential through gene amplification. This amplification involves a significant increase in gene copy number, leading to overproduction of the encoded protein. This increased protein production significantly enhances the growth and survival capabilities of cancer cells⁴².

Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) have been established as powerful prognostic biomarkers for various cancers and as indicators for predicting the efficacy of various tumor immunotherapies ²⁵. High levels of TMB and MSI in tumors are associated with more favorable responses to immunotherapy ^43,44. Through our analysis, we found that THOC3 has a significant positive correlation with TMB in various tumor types, especially in cases of lung squamous cell carcinoma (LUSC). Furthermore, in STES and STAD, THOC3 expression is significantly correlated with both TMB and MSI, implying that in these cases, high THOC3 expression may lead to better survival benefits after immunotherapy. Additionally, Homologous Recombination Deficiency (HRD) is closely associated with increased sensitivity to platinum-based chemotherapy and PARP inhibitors ⁴⁵. THOC3 shows a significant correlation with HRD in 15 types of tumors. This observation suggests that gastric cancer patients exhibiting significant correlations with TMB, MSI, and HRD may achieve better outcomes from treatment strategies combining immunotherapy and PARP inhibitors. Therefore, THOC3 can emerge as a new and promising target in anticancer immunotherapy strategies.

Immunotherapy, while showing significant efficacy in various solid tumors, still has a relatively low clinical response rate, emphasizing the urgency of developing more refined and targeted immunotherapeutic strategies⁴⁶. In our study of THOC3 expression across pan-cancer types, we observed a strong association between THOC3 expression and immune cell subsets. In lung squamous cell carcinoma, THOC3 is significantly negatively correlated with immune cells, leading us to speculate that low expression of THOC3 may extend patient prognosis, a result that supports the findings of Tao Yu et al.²³. Changes in the tumor microenvironment can affect the prognosis of cancer immunotherapy. Infiltration of stromal cells in the tumor microenvironment can alter its composition, and their interactions with tumor cells determine tumor progression and growth⁴⁷. Additionally, stromal cell infiltration is closely related to tumor cell immune suppression and immune evasion⁴⁸. According to our results, THOC3 is significantly negatively correlated with stromal cell infiltration in the tumor microenvironment of 22 types of tumors and significantly positively correlated in one type, suggesting that high expression of THOC3 in various cancers is associated with poor immunotherapy prognosis. Furthermore, immune checkpoints are one of the important pathways for tumor cells to evade immunity, and the discovery of immune checkpoint inhibitors has significantly increased the body's anti-tumor immunity and improved cancer patient prognosis ⁴⁹. Our results show a significant positive correlation between THOC3 and key immune checkpoint genes (CD276, VEGFA, VEGFB, CD274, and HMGB1). These correlations provide a new perspective on the role of THOC3 in manipulating immune checkpoint pathways and pave the way for new immune checkpoint inhibitors.

GO functional enrichment analysis indicates that THOC3 is primarily involved in DNA transcription, RNA splicing, mRNA export, and protein stability in various types of cancer, which is consistent with previous studies^5,50. KEGG enrichment analysis shows that THOC3 is mainly involved in RNA transcription and splicing. Therefore, the pathways enriched by THOC3 may promote cancer progression ⁴. Drug sensitivity testing indicates that the sensitivity to Pluripotin and RAF-265 is significantly negatively correlated with THOC3 expression, while the sensitivity to Bisacodyl is significantly positively correlated with THOC3 expression. We speculate that patients with high expression of THOC3 are more resistant to Pluripotin and RAF-265 and may have better treatment outcomes with Bisacodyl. These insights confirm the role of THOC3 in cancer cell survival and metastasis and highlight its potential as a promising therapeutic target.

While the role of THOC3 in pan-cancer has been delineated, certain limitations should be noted. Firstly, all analyses are predicated on bioinformatics, and the aberrant expression of THOC3 has only been confirmed in lung adenocarcinoma through Western blot (WB). Secondly, our study did not explore the molecular mechanisms of THOC3 in cancer. Therefore, it is imperative to conduct further research to elucidate the expression mechanisms of THOC3 in tumors in future studies.

Based on this study and the relevant online data, THOC3 has been identified as playing a critical multifunctional role in tumor progression. It regulates key pathways such as gene expression stability, cell cycle, and DNA transcription, affecting the survival and migration of tumor cells. The immune modulation in which THOC3 is involved offers a new direction for cancer treatment. Therefore, we conclude that THOC3 has the potential to be a biomarker in cancer.

Author contributions

Jixin Zhang performed bioinformatics analysis and completed the manuscript. All authors gave final approval to the manuscript.

Acknowledgments

The datasets for this study can be found in the public databases TCGA, UCSC Xena, UALCAN, DAVID, GeneMANIA, CellMiner, GEPIA2 and cBioportal.

Funding: The present study was supported by the Hebei Medical Science Research Project (No. 20221226)..

Data Availability Statement: The datasets analyzed during the current study are available in the TIMER2.0(http://timer.cistrome.org/),

UCSC(https://xenabrowser.net/), UALCAN(https://ualcan.path.uab.edu/analysis-prot.html), GEPIA2(http://gepia2.cancer-pku.cn/#analysis), cBioPortal(https://www.cbioportal.org/), GeneMANIA(http://genemania.org/), DAVID database(https://david.ncifcrf.gov/tools.jsp) ,SangerBox website (http://sangerbox.com/Tool)and CellMiner database(https://discover.nci.nih.gov/cellminer/loadDownload.do).

Ethical approval：The study was reviewed and approved by the Medical Ethics Committee of The Fourth Hospital of Hebei Medical University (Approval No. 2024KY026) and complied with the Declaration of Helsinki.

Conflicts of Interest: The authors declare no conflict of interest.

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No competing interests reported.

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A pan‐cancer analysis of the oncogenic and immunological roles of THOC3 in human cancer

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Abstract

Figures

1.0 Introduction

2.0 Materials and Methods

2.1 THOC3 Expression Analysis in Different Tumors

2.2 THOC3's Relationship with Survival in Different Tumors

2.3THOC3 Gene Alterations and Immune Infiltration Analysis

2.4 THOC3 Analysis with Immune Cells

2.5 Gene Enrichment Analysis of THOC3

2.6 Analysis of the THOC3 Expression and Drug Sensitivity

2.7 Differential Analysis of THOC3 in Lung Adenocarcinoma

3.0 Results

4.0 Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1