Unveiling Biomarkers: A Study on Mitocytosis-Related Genes Across Multiple Cancers

doi:10.21203/rs.3.rs-5049048/v1

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Unveiling Biomarkers: A Study on Mitocytosis-Related Genes Across Multiple Cancers

https://doi.org/10.21203/rs.3.rs-5049048/v1

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Mitochondria are crucial organelles in eukaryotic cells that maintain cellular homeostasis through various quality control mechanisms. One such mechanism, mitocytosis, is newly identified, yet its biological significance in cancer remains unclear. This study investigates the role of mitocytosis-related genes (MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, TSPAN9) in pan-cancer. We performed an extensive analysis of expression profiles and clinical data derived from samples in The Cancer Genome Atlas (TCGA) database. This study entailed a comparison of gene expression between malignant and normal tissues, an investigation into the correlation between gene expression and clinical prognosis through Kaplan-Meier and Cox regression analyses, and a functional enrichment analysis to elucidate the potential signaling pathways involved. We employed TIMER2.0 to assess the correlations between gene expression and immune infiltration levels in various cancers.Additionally, a nomogram predicted three- and five-year overall survival rates. The study also examined the influence of CTLA-4 and PD-1 statuses on Immunoscore (IPS) in KIRC patients, revealing that these genes are overexpressed in renal clear cell carcinoma and significantly associated with Clinical prognosis and drug response.

Mitocytosis

Mitochondrial quality control

prognostic model

kidney renal clear cell carcinoma

tumor immune microenvironment.

Cancer is a global health problem and the leading cause of death in most countries, seriously affecting the quality of life and health of people around the world. Identifying genes involved in various cancer types (pan-cancer) can help to more clearly understand the underlying mechanisms of cancer occurrence and progression and help future cancer treatment.[1].

Mitocytosis, or mitochondrial extrusion, is an observed mitochondrial self-regulation phenomenon where, following mild mitochondrial stress, damaged mitochondria are translocated to migrasomes and subsequently transported extracellularly[2]. This process, known as mitocytosis, serves as a cellular quality control mechanism for eliminating impaired mitochondria, thereby playing a crucial role in maintaining mitochondrial quality[3].Previous studies have indicated that MYO19, KIF5B, and DNM1L are essential genes for mitocytosis, each regulating mitochondrial transport, positioning, and fission, respectively, thus mediating mitochondrial extrusion. DYNLL2, TSPAN4, and TSPAN9 are regulatory genes of mitocytosis. DYNLL2 promotes mitochondrial extrusion in normal cells, while TSPAN4 and TSPAN9 serve as crucial regulatory factors of migrasomes involved in the regulation of mitocytosis[2][4]. Currently, mitochondria are recognized as dynamic organelles that, under physiological and pathological stimuli, achieve mitochondrial and protein quality control through a variety of mechanisms coordinated at both organelle and molecular levels. This control limits and delays the accumulation and proliferation of dysfunctional mitochondria, maintaining a dynamic equilibrium of mitochondrial number, morphology, function, and protein quality. Such regulation ensures the normal cellular activities, enabling cells to better adapt to their environment. If the homeostatic regulation capabilities of mitochondria and their proteins are reduced or disrupted, it can lead to the accumulation of impaired mitochondria, triggering intracellular environmental disorder. This affects the normal function of mitochondria and may induce the malignant transformation of normal cells[5]. While speculating on the plausible role of mitocytosis in cancer is reasonable, there is a lack of direct evidence supporting the direct involvement of mitocytosis in carcinogenesis. MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9, as critical mitocytosis-related genes, regulate the process of mitocytosis. To investigate the potential impact of mitocytosis in cancer, this study systematically analyzed the expression profiles of mitocytosis-related genes to elucidate their clinical and prognostic significance.

In this study, we systematically employed various bioinformatics methods to investigate the expression profiles, clinical prognostic values, methylation and mutation statuses of mitocytosis-associated genes Myo19, KIF5B, DNM1L, DYNLL2, TSPAN4 and TSPAN9 in pan-cancer, along with relevant enrichment analysis, aiming to thoroughly explore and elucidate the relationship between mitocytosis and cancer initiation.

Data collection and processing

All raw data were obtained from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/). Specific information regarding types of human cancers and individual sample sizes is presented. The raw data were subjected to integrative analysis using R version 4.2, in conjunction with results obtained from online analyses of samples sourced from the TCGA database. The specific online tools employed in this study will be detailed in the subsequent sections.

Comparison of the mitocytosis-related gene expression level

The differential mRNA expression levels of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9 between cancerous and normal tissues across pan-cancer were analyzed using samples from the TCGA database. The Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), founded on proteomic, transcriptomic, and systems biology data, provides comprehensive information on protein expression in both tumor and normal tissues. This resource is utilized to validate the protein expression levels of MYO19, KIF5B, DNM1L, DYNLL2, and TSPAN9 (with DYNLL2 and TSPAN4 not included in the HPA database) across various cancers through immunohistochemistry analysis. The validation targets include proteins expressed in renal carcinoma and gastric carcinoma.

Survival analysis and prognostic analysis

To explore the clinical value of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9, we constructed age gender and clinical stage plots. Based on different clinical characteristics, the survival and survminer packages were used to estimate the correlation between gene expression and survival rates. We built forest plots using the forestplot package and established ROC curves and nomogram model analyses utilizing the PROC and timeROC packages. Survival analysis was conducted using the Kaplan-Meier method and the log-rank test, with the optimal cutoff values determined by BEST (https://rookieutopia.com/app_direct/BEST/).Data on the prognostic values of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9, including overall survival (OS), were obtained from BEST.

CNV and methylation analysis

The GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) was used to conduct mitocytosis-related gene set analysis to determine their SNV, CNV, methylation, and other characteristics. The raw sample data and their details were obtained from the TCGA database. By entering the gene set into the GSCALite search box and selecting data from 33 types of cancer and normal tissues, we analyzed mRNA expression, SNV, CNV, methylation, and GTEx expression. Detailed methodology is available on the GSCALite website (http://bioinfo.life.hust.edu.cn/web/GSCALite/).

Immunological correlation analysis

The Tumor Immune Estimation Resource (TIMER) database (http://timer.comp-genomics.org/) was used to assess the relationship between mitocytosis-related gene expression and tumor-infiltrating immune cells, which collected 10,897 samples across 32 cancer types from TCGA.This study investigated the association between the expression of mitocytosis-related genes and the infiltration levels of various immune cells, including B cells, CD4 + T cells, and CD8 + T cells.

Database used to analyze the correlation between TMB, MSI of the TME in human cancers

We used the ESTIMATE algorithm to calculate the ESTIMATE, Immune, and Stromal scores for 33 tumors. ESTIMATE, developed by Yoshihara et al., is an algorithm that uses the transcriptional profiles of cancer samples to infer the content of tumor cells, infiltrating immune cells, and stromal cells. It can calculate the proportion or abundance of immune cells, stromal cells, and tumor cells related to the tumor microenvironment within the tumor tissue. Statistical significance was set at p < 0.05. The stromal score captures the presence of stromal cells in the tumor tissue, the immune score reflects the infiltration of immune cells in the tumor tissue, and the ESTIMATE score infers tumor purity.

Functional enrichment analysis

In this study, enrichment analysis was conducted using Gene Set Enrichment Analysis (GSEA) with the R package clusterProfiler. An adjusted p-value < 0.01 and false discovery rate (FDR) > 1.5 were considered statistically significant. GSEA evaluated potential mechanisms against the Molecular Signature Database of c5.all.v7.1.symbols and c2.cp.kegg.v7.1.symbols, which contains 100 random sample permutations. Statistical significance was set at p < 0.05.

Unsupervised clustering and drug sensitivity analysis for clusters

Unsupervised clustering analysis was performed using the R package ConsensusClusterPlus, which determines the number of clusters and their membership based on the stability evidence from 1,000 repetitions of unsupervised analysis. For gene-level clustering, samples were clustered based on differentially expressed genes (DEGs), defined as cluster A and cluster B. The R package oncoPredict was used to predict different drug responses in KIRC, and the Wilcox test was used to explore differences.

The upregulated expression of mitocytosis-related genes in several cancer

We analyzed the mRNA expression levels of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9 using data from TCGA. The analysis revealed that, compared to adjacent normal tissues (including UCEC and KICH), the expression of these genes is generally lower in some tumor types. However, certain cancers such as BRCA, CHOL, COAD, ESCA, HNSC, LIHC, and STAD exhibited a significant upregulation trend (Fig. 1A). Since TSPAN4 and DYNLL2 are not included in the HPA database, we used IHC results from the HPA database to confirm the protein-level expression of MYO19, KIF5B, DNM1L, and TSPAN9 (Fig. 1B).

IHC staining demonstrated that these genes exhibited weak or negative expression in tumor tissues from gastric cancer and renal cancer. Overall, we demonstrated decreased expression of MYO19, KIF5B, DNM1L, DYNLL2, and TSPAN9 in these tumors.

The prognostic value of mitocytosis-related genes in certain types of cancer.

Using raw sample data from the TCGA database, we investigated the relationship between the expression levels of MYO19, KIF5B, and DNM1L and overall survival (OS) in 33 cancers by univariate Cox regression analysis (Fig. 2A). MYO19 showed significant hazard ratios in LGG, MESO, PRAD, READ, and UVM. KIF5B showed significant association in ACC, KIRC, THYM, and UVM. DNM1L showed significant hazard ratios in ACC, BRCA, LUAD, MESO, and READ. Additionally, we evaluated the prognostic relevance of mitocytosis-related genes in cancer patients. Upregulated expression of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9 was associated with lower OS in ACC, LIHC, and MESO, but higher OS in CRC, KIRC, and SKCM (Figs. 2B and S-1). ROC curve analysis indicated that MYO19 (AUC = 0.845) and DNM1L (AUC = 0.842) may act as diagnostic markers, as demonstrated in HNSC. We also analyzed the expression levels of MYO19, KIF5B, and DNM1L in LIHC, MESO, and READ across different ages, genders, and WHO stages, revealing significant differences between groups (Figure S-2).

The positive correlation between mitcytosis-related gene CNV, SNV and methylation

Pearson correlation analysis showed a strong correlation between CNV and the mRNA RSEM of DNM1L, DYNLL2, and TSPAN9 in TCGA (Fig. 3A). However, BRCA and READ showed a poor correlation for KIF5B, MYO19, and TSPAN9. CNV patterns varied by gene and cancer type, with heterozygous amplification and deletion being the main types. TSPAN9 and DNM1L had low frequencies of heterozygous deletion and high frequencies of heterozygous amplification. Homozygous amplification was observed for TSPAN9 and DNM1L in OV and TGCT, and for MYO19 and TSPAN9 in CHOL, while TSPAN4 had homozygous deletions.

In methylation analysis, we compared tumor and normal tissues, revealing significant disparities. MYO19 showed low methylation levels in BRCA, HNSC, BLCA, LIHC, LUAD, PRAD, and UCEC, while DYNLL2, KIF5B, and DNM1L showed elevated levels in KIRC. Spearman correlation analysis indicated high methylation levels of TSPAN4 in ACC, PRAD, and LIHC, and for DYNLL2, TSPAN4, and TSPAN9 in THYM. When comparing OS between high and low methylation levels, TSPAN4 hypermethylation had a low effect on survival risk in SKCM, GBM, PAAD, and LGG, whereas TSPAN9 hypermethylation had a high effect on survival risk in ACC, LUAD, THCA, and UVM. In mutation analysis of 301 samples, KIF5B had the highest mutation count and frequency across all samples, particularly in UCEC, followed by MYO19 and DNM1L (Fig. 3C).

The innate mechanism unravelled through GSEA

We analyzed the expression levels of 1609 genes in KIRC and 5415 genes in MESO, significant differences were found between the high- and low-expression groups. In KIRC, 63% genes were upregulated, and 37% were downregulated, while in MESO, 66% genes were upregulated, and 34% were downregulated (with a p-value < 0.05, |Log2-FC| > 1) (Fig. 4A). The Enrichment Score results indicate that genes associated with the Intermediate Filament Cytoskeleton are predominantly highly expressed (Fig. 4B). A heatmap further illustrates this trend, showing a significant increase in log fold change (logFC) for these genes, while other genes appear randomly distributed.

Immune-related characteristics of mitocytosis-related gene in pan-cancer

To investigate the role of mitocytosis-related genes in immune mechanisms and immune response within the TME, we explored the potential correlation between the expression of these six genes and immune cell infiltration in human cancers. TMB can indirectly reflect the ability and extent of a tumor to produce new antigens, while microsatellite instability (MSI) is also related to cellular immunogenicity and can predict the efficacy of immunotherapy in various tumors. The results showed that the expression of MYO19 was significantly positively correlated with TMB in ACC, UCS, UCEC, STAD, SKCM, READ, PRAD, LUAD, KIRC, and COAD. KIF5B expression showed a negative correlation with COAD and positive correlations with GBM, LUAD, SKCM, and UCEC. DNM1L expression was negatively correlated with KIRP and THCA, and positively correlated with LUAD, READ, and UCEC. In terms of MSI, MYO19 expression demonstrated a positive correlation with UVM, UCEC, STAD, SARC, PRAD, LUSC, LUAD, LGG, KIRC, KICH, COAD, and BRCA, and a negative correlation with DLBC. KIF5B expression showed a negative correlation with COAD, DLBC, HNSC, PRAD, and THCA, and a positive correlation with CESC and KIRC. DNM1L expression exhibited a negative correlation with DLBC and PRAD, while being positively correlated with CESC, COAD, KIRC, LUSC, READ, STAD, TGCT, and UCEC (Fig. 5A).

In the tumor microenvironment, tumor-infiltrating immune cells (TIICs) are a critical component and influence the prognosis of cancer patients. We used the TIMER database to analyze the relationship between the expression levels of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, TSPAN9, and DYNLL2 and immune cell infiltration. We observed a significant correlation between the expression levels of these genes and CD4 + T cell, particularly demonstrating elevated levels collectively in BLCA, HNSC, KIRC, THCA, UCEC, and UVM. We found a significant positive correlation of KIRC between CD4 + T cell level and MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, TSPAN9 and DYNLL2 expression in MYO19 (Rho = 0.104), TSPAN4(Rho = 0.488) and TSPAN9 (Rho = 0.43) (Fig. 5B and Figure-S-3).

Next, we conducted an in-depth analysis of the correlation between mitocytosis-related gene expression and immune scores across all cancer types (Fig. 5C). The results indicated that MYO19 expression had a significant correlation with the three immune scores, showing particularly strong correlations in GBM, KIRP, LUSC, SARC, and TGCT. KIF5B exhibited a high correlation in ACC, CESC, SARC, STAD, and THYM. DNM1L showed high correlation in ACC, GBM, LGG, MESO, LUSC, SARC, and TGCT, all with significant statistical relevance.

Clinical prognostic value of mitocytosis-related gene

In order to explore the clinical prognostic value of mitocytosis-related genes, this study combined mitocytosis-related gene expression with clinical factors based on time ROC to construct a nomogram and calibration analysis. In the survival-related ROC curves, it is evident that only the 5-year duration exhibits a strong correlation (AUC = 0.709). We analyzed the clinical value by the ROC curve (Fig. 6A). Mitocytosis-related gene perform as a clinical marker in risk score (AUC = 0.709) and stage (AUC = 0.724). Subsequently, we compared the performance of high and low risk scores in survival probability (Fig. 6B), revealing significantly higher overall survival probability associated with low risk scores.

Subsequently, we investigated the interrelationships among these six genes (Fig. 6C). The strongest positive association was shown between KIF5B and DYNLL2, while the strongest negative association was shown between DNM1L and TSPAN4.Next, we combined clinical features with risk score in both multivariable and univariable analyses to construct forest plots (Fig. 6D). In the univariable forest plot, stage, T_stage, N_stage, M_stage, and risk score showed statistical significance. However, in the multivariable forest plot, only risk score demonstrated statistical significance.

The potential influence of mitocytosis-related genes on IPS scores and drug sensitivity.

We performed unsupervised clustering analysis on 529 KIRC samples from TCGA based on the expression levels of mitocytosis-related genes. We applied the "ESTIMATE" and "CIBERSORT" algorithms to determine the expression levels of mitocytosis-related genes in tumor samples. Using the R package "ConsensusClusterPlus," we clustered all samples based on these expression levels, identifying two distinct subtypes (Fig. 7A). The prognosis of these two subtypes was compared using Kaplan-Meier curves, and the results showed that the prognosis of group A was significantly better than that of group B (p = 0.001). Based on these findings, patient samples were classified into high-risk and low-risk prognostic groups (HSG and LSG, respectively). Previous studies have shown that immunogenicity-based IPS can play a role in predicting the efficacy of immunotherapy in KIRC patients. We analyzed the relationship of IPS between HSG and LSG. We used IPS- PD-L1 + CTLA4+, IPS- PD-L1-CTLA4+, IPS- PD-L1-CTLA4-, IPS- PD-L1 + CTLA4- to evaluate the potential of scores application (Fig. 7B). All 4 groups showed significant differences between HSG and LSG (all p < 0.05). Through overlap analysis of KIRC and MESO associated drugs, 22 overlapping drugs were identified by KIRC risk, MESO risk, KIRC 1vs2, MESO 1vs2 (Fig. 7C). Subsequently, we compared the sensitivity of the two clusters to a subset of treatment-related drugs in KIRC and MESO. The study demonstrated the predicted therapeutic responses of anti-tumor drugs commonly used clinically by KIRC and MESO, including telomerase inhibitor IX, vinblastine, and leflunomide, in two clusters of HSG and LSG samples, and significant group differences were observed. Differences between (all p < 0.05)

Previous studies have suggested that mitochondrial quality control mechanisms are involved in the occurrence of human cancers[5][6]. As a newly discovered mitochondrial control mechanism, mitocytosis can be reasonably hypothesized to be closely related to cancer development, although further research is needed[5]. In this study, we explored the potential impact of MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, and TSPAN9 as mitocytosis-related genes in pan-cancer. These six genes exhibited significantly high expression in certain types of cancer and demonstrated some clinical prognostic value.

In KIRC, mitocytosis-related genes showed significant enrichment in the intermediate filament cytoskeleton. In MESO, these genes showed significant enrichment in DNA binding, transcription factor activity, transcription regulator activity, and positive regulation of transcription by RNA polymerase II. Previous studies have indicated that abnormalities in the cytoskeleton increase cellular sensitivity to oncogene transformation, representing one of the potential pathways for malignant tumor development[7]. At the transcriptional level, changes in transcriptional regulation and transcription binding site status can potentially influence the generation and development of cancer[8]. This underscores the potential impact of mitocytosis-related genes at the molecular level. Further research is needed on marker genes associated with diagnosis and prognosis.

Immune system can recognize and eliminate tumor cells within the tumor microenvironment (TME) under normal circumstances[9]. However, tumor cells may use various methods to promote proliferation, escape the immune system, and decrease survival[10]. According to our findings, we found that mitocytosis-related gene expression was closely correlated with the infiltration of immune cells, including CD4 + T cells, suggesting that these genes likely affect tumor development and prognosis by impacting the TME[11]. Estimate is a method that uses gene expression features to infer the proportion of stromal and immune cells in tumor samples. The ESTIMATE algorithm can use expression data to estimate the content of stromal cells and immune cells in malignant tumor tissues and predict immune scores and stromal scores. The XCELL algorithm was utilized to compute the immune score, stromal score, and immune microenvironment score of 33 tumors. The relationship between the expression level of MYO19, KIF5B, and DNM1L and ESTIMATE immune and stromal scores were evaluated. Our results indicate that KIF5B expression has a strong correlation with tumors in DLBC, KICH, KIRC, and LAML, and this correlation is statistically significant. Additionally, MYO19 and DNM1L expression had a negative correlation with the ESTIMATE, immune score and stromal score in most tumors. Increasingly, research reports suggest that immune cells in the TME may play a critical role in blocking and reducing various tumors[12]. Therefore, it is necessary to study mitocytosis-related genes at the TME level. It is foreseeable that by detecting the expression levels of mitocytosis-related genes, the efficacy of immunotherapy can be predicted, allowing for personalized treatment. This approach, combined with traditional immunotherapy pathways, can lead to better clinical decision-making. In addition, TME status can serve as a predictor of response to immune checkpoint inhibitor (ICI) treatment and may even become a new treatment direction, as changes in the two states are often closely related[13]. Substantial evidence suggests that genes related to immune checkpoints contribute to immunotherapies. We compared the Immunophenoscore (IPS) for different PD-L1 and CTLA4 statuses between two groups of samples generated after unsupervised clustering, both of which exhibited significant intergroup differences. Consequently, mitocytosis-related genes can influence ICI therapy and impact the TME to some extent.

To further investigate the potential impact of mitocytosis-related genes on drug treatment, we conducted unsupervised clustering analysis on samples from the TCGA database. After identifying two clusters, we performed survival prognosis analysis, which revealed significant intergroup differences. Based on these results, we categorized the clusters into prognostic-related high- and low-score groups (HSG and LSG, respectively). Subsequently, we performed an overlap analysis, identifying 22 overlapping drugs. We compared the drug sensitivity differences between HSG and LSG and obtained several significant results. From these results, it can be concluded that mitocytosis-related genes are associated with poor prognosis in KIRC patients. Furthermore, the state of the TME may also affect the efficacy of chemotherapy. A previous study reported that intratumoral infiltration of interleukin-17-producing cells could improve the response of gastric cancer to chemotherapy[14]. However, the specific relationship between the TME and chemotherapy response remains further needed. Vinblastine interferes with the formation and assembly of microtubules, thereby preventing the formation of the mitotic spindle during cell division, rendering cancer cells unable to undergo normal mitocytosis. This inhibition of cell division and proliferation makes it effective in combination with other drugs for the treatment of various cancers[15]. A previous study using the Caki cell line model indicated that NVP-BEZ235 alone does not significantly induce apoptosis in Caki cells[16]. However, when combined with vinblastine, the proportion of apoptotic cells reached approximately 60%. This type of research indicates that KIRC patients show significant individual variations in chemotherapy efficacy and prognosis after different treatment regimens. We have reason to speculate that the expression levels of mitocytosis-related genes may serve as biomarkers for prognosis and chemotherapy efficacy in KIRC patients. Although we synthesized sample information from the TCGA database and conducted various investigations, this study still has some limitations. First, bioinformatics analysis showed a significant correlation between mitocytosis-related genes and pan-cancer, influencing prognosis, immune microenvironment, and drug response. However, further validation through biological and cellular experiments is necessary. Additionally, while the expression of mitocytosis-related genes is associated with the immune system and clinical survival in human malignancies, it remains unclear whether these genes affect clinical survival through immune pathways and their specific mechanisms. Further exploration in other areas is needed. Collectively, our study sheds new light on the possibility of mitocytosis-related genes to play an impact in tumorigenesis and metastasis, as well as their potential influence on tumor immunology, methylation, mutation, and drug sensitivity. Future prospective studies that continue to focus on the expression of mitocytosis-related genes and the tumor immune environment will help validate the results of this study and facilitate the development of immuno-based anticancer therapies.

TME tumor microenvironment

TMB tumor mutation burden

MSI microsatellite instability

GSEA gene set enrichment analysis

ROC receiver operating characteristic

LASSO least absolute shrinkage and selection operator

ICIs immune checkpoint inhibitors

CTLA4 cytotoxic lymphocyte antigen-4

PD-1 programmed cell death protein 1.

Compliance with ethics requirements

All Institutional and national guidelines for the care were followed.

Authorship contribution statement

YJ, PY, YZ, and MW designed the study. JX and YJ analyzed and interpreted the data. JX wrote the original draft. JX and CL wrote this manuscript. NX and TY edited and revised the manuscript. All authors have made substantial contributions to the conception or design of the work, the acquisition, analysis, or interpretation of data, or the drafting or revising of the work. All authors have approved the submitted version and any substantially modified version that involves their contribution to the study. Furthermore, all authors have agreed to be personally accountable for their own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even those in which they were not personally involved, are appropriately investigated, resolved, and documented in the literature.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This work was supported by the National Natural Science Foundation of China (82272758), the Key Laboratory of Translational Radiation Oncology, Hunan Province (No. 2015TP1009), the Provincial Key Research and Development Program of Hunan Province (No. 2018SK2123), Hunan Cancer Hospital Climb Plan (No. QH201905), Scientific Research Project of Hunan Provincial Health Commission (B202309037920), Scientific Research Fund of Hunan Administration of traditional Chinese medicine (B2023007), Hunan Provincial Natural Science Foundation of China (2023JJ40407).

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Sincere gratitude is extended to the Department of Radiation Oncology at Hunan Cancer Hospital for their invaluable support and contributions to this research. Special appreciation is given to Dr. Jin and Dr. Yang for their insightful discussions and assistance.

Consent for Publication

The authors hereby grant consent for the publication of this research work in Journal of Translational Medicine. All authors have reviewed the final manuscript and approved it for submission. The manuscript represents original work and has not been published elsewhere. All necessary permissions have been obtained for any third-party content included in the manuscript.

Ethical Compliance

This research does not involve experiments on live vertebrates and/or higher invertebrates. All data used in this study were obtained through publicly available databases, ensuring compliance with ethical standards without the need for animal experimentation.

Image Use and Attribution

This research does not involve the replication or reproduction of images from previous publications. All images and figures presented in this study are original and created by the authors, ensuring no need for additional permissions or credits.

Date availability statement

Publicly available datasets were analyzed in this study. These can be found in The Cancer Genome Atlas (https://portal.gdc.cancer.gov/).

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Supplementaryfigure1.tif
Supplementary Materials Figure. S1 The OS Kaplan-Meier survival curves in other cancer types.
Supplementaryfigure2.tif
Figure. S2 (A) Correlation between MYO19, KIF5B and DNM1L expression levels and the age in MESO and KIRC. (B) Correlation between MYO19, KIF5B and DNM1L expression levels and the gender in MESO and KIRC. (C) Correlation between MYO19, KIF5B and DNM1L expression levels and the pathological stages.
Supplementaryfigure3.tif
Figure.S3 Mitocytosis-related gene expression was correlated with CD8+ T cell and B cell in pan-cancer
Supplementaryfigure4.tif
Figure. S4 (A)The partial likelihood deviance and least absolute shrinkage and selection operator (LASSO) coefficient profiles of the six key molecules. (B) Feature selection and classification prediction of mitocytosis-related genes based on the random forest model. (C) Risk prediction assessment and survival analysis of mitocytosis-related genes based on a nomogram.

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Unveiling Biomarkers: A Study on Mitocytosis-Related Genes Across Multiple Cancers

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Abstract

Figures

Introduction

Materials and Methods

Data collection and processing

Comparison of the mitocytosis-related gene expression level

Survival analysis and prognostic analysis

CNV and methylation analysis

Immunological correlation analysis

Database used to analyze the correlation between TMB, MSI of the TME in human cancers

Functional enrichment analysis

Unsupervised clustering and drug sensitivity analysis for clusters

Results

The upregulated expression of mitocytosis-related genes in several cancer

The positive correlation between mitcytosis-related gene CNV, SNV and methylation

The innate mechanism unravelled through GSEA

Immune-related characteristics of mitocytosis-related gene in pan-cancer

Clinical prognostic value of mitocytosis-related gene

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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