A multidimensional analysis of MRPL17 protein in human tumors

doi:10.21203/rs.3.rs-4095111/v1

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A multidimensional analysis of MRPL17 protein in human tumors

https://doi.org/10.21203/rs.3.rs-4095111/v1

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MRPL17 is one of mitochondrial ribosome protein (MRP) family proteins, which have oncogenic effects in several malignant tumors. However, it is unclear that the relationship between MRPL17 expression pattern and prognosis across different cancer types. Also, the biological function or effects on the immune microenvironment of MRPL17 is unknown. In this study, we parsed multiple public databases to explore the potential tumorigenic actions of MRPL17, including correlations with prognosis, microsatellite instability (MSI), tumor mutational burden (TMB), immune checkpoint genes, immune cell infiltration, and immunotherapy response in pan-cancer. Moreover, we validated MRPL17 expression in a tissue microarray by immunohistochemistry. The results showed that MRPL17 was upregulated in 19 cancer types and correlated with poor prognosis in many cancers. The correlation between MRPL17 and TMB was found in 10 cancers as well as MSI in five. The expression level of MRPL17 was found to be notably correlated with immune cell infiltration, showing a negative correlation with CD4 T cells infiltration and a positive correlation with dendritic cells (DC). MRPL17 expression levels were positively associated with drug sensitivity in certain cancers. In addition, we discovered that MRPL17 participated in the DNA repair at the single-cell level for most cancers. These findings provides a promising candidate for therapeutic targets and a new direction for future research.

MRPL17

pan-cancer

prognosis

immune cell infiltration

bioinformatics

Cancer has become the main cause of death worldwide and is a public health issue. In 2020, there were 19.3 million new cases of cancer worldwide and nearly 10 million deaths from cancer. Moreover, the global cancer burden is expected to be 28.4 million cases in 2040, a 47% increase over 2020¹. In the past few decades, with the progress of science and technology, the treatments of some cancers, surgery, radiotherapy, chemotherapy and immunotherapy, have made great progress. However, outcomes remain unsatisfactory. More and more evidence shown that tumor occurrence, development, recurrence and metastasis are closely related to tumor microenvironment (TME)^2–5. Tumor immunity plays an important role in tumor recurrence and poor prognosis. Unfortunately, many patients are insensitive to immunotherapy⁶. Thus, it is necessary to find new targets to evaluate the immune microenvironment and tumor development for individual patients.

The mitochondrial ribosome protein (MRP) family contains 30 small mitoribosomal proteins (MRPSs) and 52 large mitoribosomal proteins (MRPLs) which are encoded by nuclear genes^{7 8}. In recent years, many studies indicated that dysregulation of MRPLs genes has been found in various cancers⁹. MRPL14 increased cell proliferation of thyroid cancer and promoted cell migration via epithelial-mesenchymal transition-related proteins. Moreover, MRPL14 can increase the expression of oxidative phosphorylation complex IV (MTCO1) and reduce the accumulation of ROS¹⁰. Though hnRNPK can mediates tumorigenesis through splicing regulation of MRPL33 pre-mRNA, MRPL33-L could increase tumorigenic potential of hnRNPK-depleted cancer cells¹¹. MRPL41 can stabilizes the p53 protein, and enhances its translocation to the mitochondria, thereby inducing apoptosis¹². MRPL52 can suppress apoptosis and promote migration and invasion of hypoxic breast cancer through preventing uncontrolled ROS generation¹³. Nevertheless, only a small number of studies to date have reported MRPL17 in some specific types of cancer, including lung cancer^{14 15}, endometrial cancer¹⁶ and tongue squamous cell carcinoma¹⁷. Thus, the role of MRPL17 on a pan-cancer dimension needs to be unraveled to inform functional and therapeutic studies.

2.1 Gene Expression Analysis of MRPL17 in Pan-Cancer

The TIMER2 web (http://timer.cistrome.org/)¹⁸ was used to explore the expression difference of MRPL17 between tumor types and corresponding normal tissues of The Cancer Genome Atlas (TCGA) database. The Sangerbox 3.0 web (http://sangerbox.com/home.html) was used for the paired comparison of MRPL17 expression of the pan-cancer with case-matched adjacent normal tissues from the TCGA project. For the tumors with highly limited or without normal tissues, the GEPIA2 web (http://gepia2.cancer-pku.cn/#index)¹⁹was used to generate box plots of expression of tumor tissues and adjacent normal tissues of the GTEx (Genotype-Tissue Expression) database, under the settings of P-value cutoff = 0.01, log2FC (fold change) cutoff = 1, and “Match TCGA normal and GTEx data”. The abbreviations of 33 types of tumors are shown in Table 1. MRPL17 expression data of tumor cell lines were obtained from the CCLE database (https://sites.broadinstitute.org/ccle/datasets).

Table 1

The abbreviations and corresponding full names of 33 cancers.
Cancer Type	Abbreviation
Adrenocortical carcinoma	ACC
Bladder Urothelial Carcinoma	BLCA
Breast invasive carcinoma	BRCA
Cervical squamous cell carcinoma and endocervical adenocarcinoma	CESC
Cholangio carcinoma	CHOL
Colon adenocarcinoma	COAD
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma	DLBC
Esophageal carcinoma	ESCA
Glioblastoma multiforme	GBM
Head and Neck squamous cell carcinoma	HNSC
Kidney Chromophobe	KICH
Kidney renal clear cell carcinoma	KIRC
Kidney renal papillary cell carcinoma	KIRP
Acute Myeloid Leukemia	LAML
Brain Lower Grade Glioma	LGG
Liver hepatocellular carcinoma	LIHC
Lung adenocarcinoma	LUAD
Lung squamous cell carcinoma	LUSC
Mesothelioma	MESO
Ovarian serous cystadenocarcinoma	OV
Pancreatic adenocarcinoma	PAAD
Pheochromocytoma and Paraganglioma	PCPG
Prostate adenocarcinoma	PRAD
Rectum adenocarcinoma	READ
Sarcoma	SARC
Skin Cutaneous Melanoma	SKCM
Stomach adenocarcinoma	STAD
Testicular Germ Cell Tumors	TGCT
Thyroid carcinoma	THCA
Thymoma	THYM
Uterine Corpus Endometrial Carcinoma	UCEC
Uterine Carcinosarcoma	UCS
Uveal Melanoma	UVM

2.2 Immunohistochemistry (IHC) staining

We obtained IHC images of MRPL17 protein expression in four tumors tissues and normal tissues in healthy control from the Human Protein Atlas (the HPA) (http://www.proteinatlas.org/), including BRCA, COAD, LIHC, LUAD, KIRC and STAD to explore the protein expression differences.

2.3 Survival prognosis analysis

We verified the prognostic value of MRPL17 based on clinical data from the TCGA database, Xiantao Academic Online Website (https://www.xiantao.love/) was used for bioinformatics analysis based on the R language. In the R environment, RNA sequencing data in fragments per kilobase per million formats were transformed into transcripts per million reads format. The “Survival” (version3.2-10) and “survminer” (version 0.4.9) packages were used for statistical analysis and visualization, respectively. The statistical significance of OS and DSS between the high and low MRPL17 expression groups in patients with 33 cancer types was analyzed by univariate Cox regression. Statistical significance was set at P < 0.05.

2.4 Gene Mutation Analysis

cBioPortal (https://www.cbioportal.org/) web was used to analyse tumor genomic characteristics in the MRPL17 gene. We performed the pan-cancer mutation frequency analysis of MRPL17 using this database. The mutation alterations included mutation, amplification, and deep deletion.

2.5 Correlation analysis of MRPL17 and immune checkpoint, immune cell infiltration or immune response prediction and MRPL17 differential expression after immunotherapy

We investigated the association between MRPL17 and immune response-related checkpoints based on the TCGA database. Correlative studies on MRPL17 expression with immune checkpoints were also discussed by Spearman analysis. We analyze the relationship between MRPL17 and immune cell infiltration using the Sangerbox platform²⁰. We extracted the gene expression profile of each tumor and calculated the stromal, immune, and ESTIMATE scores of each patient in each tumor based on gene expression using the R package ESTIMATE. We obtained immune infiltration scores for 10 180 tumor samples from 44 tumor types and calculated the Pearson's correlation coefficient between gene and immune infiltration scores for each tumor using the corr. Test function of the R package psych (version 2.1.6) to determine the significant correlation of immune infiltration scores. Using TIMER, in the R package IOBR (version 0.99.9)²¹, the B cell, T cell CD4, T cell CD8, neutrophil, macrophage, DC and other immune cell infiltration scores were evaluated in each tumor based on gene expression.

2.6 Correlational Research on MRPL17 and TMB and MSI

TMB is defined as the total number of base mutations per million cells in the tumor. It is generally believed that TMB can stimulate the production of tumor-specific and high-immunogen antibodies and is a novel target for predicting the effect of tumor immunotherapy²². MSI is caused by abnormal DNA MMR and leads to gene duplication disorder and tumor development, affecting tumor prognosis^{23 24}. Correlation between MRPL17 and TMB and MSI was conducted using Spearman correlation analysis. MMR is a genetic monitoring mechanism that identifies and repairs mismatched nucleotides during DNA replication to maintain genetic stability²⁵.

2.7 Tumor cell stemness

We analyzed the relationship between MRPL17 and tumor stemness using the Sangerbox platform²⁰. We obtained six tumor stemness scores calculated by mRNA expression or methylation signature from a previous study²⁶. We integrated the samples' stemness scores and gene expression data, calculating their Pearson correlation in each tumor.

2.8 Single-Cell Functional Analysis of MRPL17

The Cancer single-cell state Atlas (CancerSEA, http://biocc.hrbmu.edu.cn/CancerSEA/, accessed on 20 April 2023) is an analytic tool for studying cancer cell functions at the single-cell level, containing 14 tumor-related cellular functions of 900 cancer cells from 25 cancers²⁷.

2.9 Drug Sensitivity Analysis

GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/, accessed on 20 April 2023) is a comprehensive platform for analyzing gene expression and drug sensitivity analysis.

2.10 Co-Expressed Genes and Enrichment Analysis of MRPL17

The STRING website (https://string-db.org/) was used to acquire the top 50 MRPL17-binding proteins. We set the following main parameters: minimum required interaction score [“Low confidence (0.150)”], meaning of network edges (“evidence”), max number of interactors to show (“no more than 50 interactors” in 1st shell) and active interaction sources (“Experiments, T ext mining, Databases”). Visualization of protein–protein interaction (PPI) network was realized by Cytoscape (version 3.9.0). Next, we obtained the top MRPL17 expression-related genes via the GEPIA2 database, and conducted the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)²⁸enrichment analyses for the top 50 MRPL17 binding proteins via the XIANTAO Academic (https://www.xiantao.love/products).

2.11 Correlation of MRPL17 expression with clinicopathologic characteristics in LIHC

To evaluate whether the risk score system can serve as an independent predictive index, univariate and multivariate Cox regression analyses were performed with clinicopathological parameters, including pathologic T stage, pathological M stage, pathologic stage, MRPL17 expression and tumor status from TCGA database.

2.12 Immunohistochemistry (IHC)

Thirty-nine cases of liver cancer and their corresponding normal tissues were collected from the Department of of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, for immunohistochemical staining. The study was autho-rized by local institutional review boards, and written informed consent was obtained from all patients before surgery. The clinical tissue specimens were fixed with 10% formalin and stored in paraffin embedding once they are in vitro after hepatectomy²⁹. MRPL17 rabbit polyclonal antibody (PA3429S, 1:100, Abmart, China). The results of IHC analysis were assessed and quantized according to three indicators, including IHC mean density, IHC score value (H-Score) and IHC immunoreactive score (IRS).

3.1 Significant differences in MRPL17 expression and assessment of MRPL17 as a pan‑cancer prognostic biomarker

We explored MRPL17 expression in tumor and normal tissues from the TIMER database and found that MRPL17 was significantly upregulated in 19 cancer types, including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, STAD, THCA and UCEC. However, MRPL17 was downregulated only in KICH (Fig. 1A). The result of comparison of MRPL17 expression of the pan-cancer with normal tissues in the Sangerbox 3.0 web showed that MRPL17 mRNA expression was significantly high among most cancer types except PRAD and KICH, which was consistent with the TIMER database results (Fig. 1B). We further analyze the cancers without normal tissues in the TIMER database via the GEPIA database, and the results showed that MRPL17 was significantly upregulated in DLBC, LAML, TGCT, THYM and UCS. However, MRPL17 was downregulated only in LAML (Fig. 1C). We downloaded data of tumor cell lines from the CCLE database and analyzed the expression of MRPL17 in multiple tumor cells (Fig. 1D). From the result, it was observed that the expression level of MRPL17 was significantly different in cancer cell lines (Kruskal Wallis test p < 0.001) of different tissue origins.

We further explored the IHC results in HPA database to figure out MRPL17 expression in tumors. The result showed that normal breast, colon, kidney, liver, lung and stomach had negative or medium MRPL17 IHC staining, but tumor tissues showed medium or strong IHC staining (Fig. 2).

3.2 Prognostic value of MRPL17 across cancer types

To further elucidate the effect of FAM110A expression on the prognosis of patients with cancer, we downloaded TCGA RNA-seq and clinical data. Univariate COX regression analysis was performed to explore the relationship between MRPL17 expression and overall survival (OS) in 33 cancer types, as shown in Fig. 3A. High expression of MRPL17 was significantly associated with poorer prognosis in patients with ACC, KICH, KIRC, LAML, LGG, LIHC, LUAD and UVM (Fig. 3C-J).

To exclude the bias caused by non-tumor events, we further evaluated the effect of MRPL17 expression levels on disease-specific survival (DSS) (Fig. 3B). The results were roughly consistent with the OS analysis, demonstrating that high MRPL17 expression was associated with poor prognosis in patients with ACC, KIRC,KIRP,LGG, LIHC, LUAD and UVM (Fig. 3K-Q).

Therefore, MRPL17 overexpression in ACC, KIRC, LGG, LIHC, LUAD and UVM had shorter OS and DSS. Moreover, the time-dependent ROC curve indicated that the 1-, 3-, and 5-year OS of MRPL17 were above 0.5 in ACC, KIRC, LGG, LIHC, LUAD and UVM (Figure S1). These results revealed that MRPL17 expression levels are significantly associated with prognosis in patients with multiple tumor types.

3.3 Mutation Analysis of MRPL17

We discussed MRPL17 genetic alterations in pan-cancer using the cBioPortal database. Mutation, amplification, deep deletion and are the main type of frequent genetic alterations of MRPL17. The frequency of genetic variation of MRPL17 was the highest in Glioma (3.7%) and was mainly in the form of deep deletion. The second and the third highest frequency of MRPL17 occurred in pheochromocytoma and ACC (Fig. 4A). Moreover, we showed additional genetic alterations and their locations within MRPL17(Fig. 4B).

3.4 Correlation of MRPL17 expression on immune checkpoints and immunotherapy

Since the expression of immune checkpoint genes is closely related to the efficacy of immunotherapy, we first explored the relevance of MRPL17 to genes that are recognized as immune response-related checkpoints using the TCGA database. Interestingly, two significant but diametrically opposite trends were observed among the different cancers (Fig. 5A). Next, we verified the correlations between MRPL17 and several immune checkpoint blocker genes, including PD1, PD-L1, CTLA-4 and LAG-3, in the TIMER 2.0 database. The most significant positive correlation between MRPL17 and these genes was observed in LIHC and UVM, and the most significant negative correlation was observed in LUSC (Fig. 5B-E; Supplementary Table 1).

We also performed the correlation analysis between MRPL17 expression and TMB and MSI, which were significantly linked with immune checkpoint inhibitor (ICIs) sensitivity. A significant positive correlation was observed between MRPL17 and TMB in ACC, KIRC, LGG, LUAD, PAAD, SARC, SKCM, STAD, UCEC and UCS (Fig. 5F). MRPL17 displayed a positive association with MSI in KIRC, STAD, UCEC and UVM, and a negative association in LGG (Fig. 5G).

3.5 Correlation of MRPL17 expression with immune infiltration

We investigated the link between MRPL17 expression and tumor purity. We utilized the ESTIMATE algorithm to calculate the stroma score, immune score, and estimate score of relevant tumor samples based on the TCGA database and assessed the correlation between MRPL17 expression levels and those scores. Based on our data, the three cancer types that showed the strongest association between MRPL17 and the stroma score were UVM, TGCT and PAAD. The three tumor types that showed the strongest association between MRPL17 expression and immune score were UVM, PAAD and LUSC. The three tumor types that showed the highest association between MRPL17 and estimate scores were UVM, PAAD and LUSC (Fig. 6A). In addition, we also analyzed the relationship between MRPL17 expression and immune cell infiltration using TIMER. The most obvious positive correlation between immune cell infiltration and MRPL17 was found in LIHC. The expression of MRPL17 was positive correlation with dendritic cells (DC) in most tumors, with CHOL being the most significant. MRPL17 expression showed a negative correlation with CD4 T cells in most tumors (Fig. 6B).

To determine whether MRPL17 expression predicts the therapeutic effect of immunosuppressants, we analyzed the correlation between MRPL17 expression and the immunophenoscore (IPS), which was conducted from a panel of immune-related genes belonging to the four classes-effector cells, immunosuppressive cells, MHC, and selected immune checkpoints³⁰. IPS predicted outcomes in melanoma patients treated with the CTLA-4 and PD-1 blockers³¹. We found that MRPL17 was positively linked to EC and negatively linked to CP in most cancers (Fig. 6C). IPS analysis demonstrated the ability of MRPL17 to predict PD- 1/PD- L1 blockade responses in various cancers.

3.6 Tumor stemness and MRPL17 expression

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem-cell-like features. Tumor stemness is associated with suppressed immune response, higher intratumoral heterogeneity, and dramatically worse outcome for the majority of cancers²⁶. Therefore, we further investigated the relationship between MRPL17 expression and tumor stemness scores on 6 different dimensions using the Sangerbox platform. We found a significant positive correlation between MRPL17 expression and tumor stemness scores calculated by mRNA expression and methylation signature in most malignant tumors, especially STES, STAD, PAAD, SARC and PCPG (Fig. 7 and Supplementary Table 2), suggesting that tumors with high MRPL17 expression often contribute to stemness maintenance.

3.7 Single-Cell Functional Analysis of MRPL17

To further study the latent role of MRPL17 in tumors, we investigated the function of MRPL17 at the single-cell level using CancerSEA (Fig. 8A). The findings displayed that MRPL17 was positively linked with DNA damage, DNA repair and stemness of acute lymphoblastic leukemia (ALL). In BRCA, MRPL17 had a positive relationship with DNA repair and inflammation and had a negative relationship with metastasis. In LUAD, MRPL17 had a positive relationship with proliferation and DNA damage and had a negative relationship with quiescence. In OV, MRPL17 had a positive relationship with apoptosis and had a negative relationship with invasion, proliferation and DNA repair. In PRAD, MRPL17 had a positive relationship with DNA repair and had a negative relationship with EMT and proliferation. However, MRPL17 had a negative relationship with quiescence, DNA damage, apoptosis, DNA repair and metastasis in UVM (Fig. 8B).

3.8 Drug Sensitivity Analysis

We used Gene Set Cancer Analysis (GSCA) to investigate the drug sensitivity of MRPL17 expression in tumors. MRPL17 expression was negatively associated with 50% inhibitory concentration (IC50) values of TGX221 and dabrafenib. Moreover, there was a positive correlation between MRPL17 expression and IC50 values of 20 types of drugs, such as AT-7519, AZD8055, BHG712, BIX02189, BMS345541, CP466722, I-BET-762, KIN001-102, Masitinib, NPK76-II-72-1, OSI-027, PIK-93, TG101348, TL-1-85, TL-2-105, TPCA-1, Vorinostat, WZ3105, YM201636 and ZSTK474(Fig. 9).

3.9 Protein Interaction Network and Enrichment Analysis of MRPL17

To further explore the role of XRCC4 in tumorigenesis, we screened out the targeting MRPL17-binding proteins and the XRCC4 expression-related genes for enrichment analyses. Via the STRING database, we obtained 50 MRPL17-binding proteins, which showed the protein–protein interactions network (Fig. 10A). Next, we obtained the top 100 MRPL17 expression-related genes via the GEPIA2 database. Biological process (BP) enrichment analysis showed that MRPL17-related genes were mainly involved in ribonucleoprotein complex biogenesis, ribosome biogenesis and RNA localization. Molecular function (MF) enrichment analysis showed that the role of MRPL17 in tumor pathogenesis was related to structural constituent of ribosome, threonine-type peptidase activity and threonine-type endopeptidase activity. We found that MRPL17-related genes were enriched in the peptidase complex, endopeptidase complex and proteasome complex in cellular component (CC) enrichment analysis. In addition, KEGG pathway analysis showed that MRPL17 participated in some pathways, such as amyotrophic lateral sclerosis, huntington disease and proteasome (Fig. 10B).

3.10 MRPL17 Is Regarded as an Independent Marker in LIHC

We used logistic regression analysis to estimate the correlation between MRPL17 expression and clinical characteristics in LIHC and PAAD from TCGA database. Univariate analysis confirmed that pathologic T stage, pathological M stage, pathologic stage, MRPL17 expression and tumor status were important prognostic factors of OS in LIHC. MRPL17 expression and tumor status were identified as independent prognostic factors in LIHC by multivariate analysis (Table 2).

Moreover, the IHC analysis also exhibited high expression of MRPL17 in tumors relative to normal tissues (Fig. 11A). Three indicators—IHC mean density, IHC H-Score and IHC IRS —all supported MRPL17 expression dramatically increased in 39 paired tumor tissues (Fig. 11B). These results indicated that AARS2 may perform oncogenic functions in LIHC.

Table 2

Univariate and Multivariate Cox analysis of OS in TCGA-LIHC dataset.
Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Pathologic T stage
T1&T2	277
T3&T4	93	2.598 (1.826–3.697)	< 0.001	2.029 (0.274–15.024)	0.489
Pathologic N stage
N0	254
N1	4	2.029 (0.497–8.281)	0.324
Pathologic M stage
M0	268
M1	4	4.077 (1.281–12.973)	0.017	1.179 (0.280–4.959)	0.822
Pathologic stage
Stage I&Stage II	259
Stage III&Stage IV	90	2.504 (1.727–3.631)	< 0.001	1.298 (0.177–9.528)	0.798
MRPL17
Low	187
High	186	1.700 (1.199–2.411)	0.003	2.177 (1.363–3.478)	0.001
Histologic grade
G1&G2	233
G3&G4	135	1.091 (0.761–1.564)	0.636
Tumor status
Tumor free	202
With tumor	152	2.317 (1.590–3.376)	< 0.001	1.916 (1.198–3.064)	0.007

MRPL17 is one of mitochondrial ribosomal proteins, whose abnormal expression in various tumors. Recently, a study revealed that MRPL17 was identified as an important prognostic factor for lung cancer patients¹⁴. Miao et al. revealed that MRPL17 considered as one of the shared gene signatures for endometrial cancer and polycystic ovary syndrome¹⁶. Here, we analyzed the role of MRPL17 in pan-cancer from a holistic perspective for the first time. we conducted a comprehensive bioinformatics analysis of MRPL17 using multiple public databases.

Our results showed that MRPL17 mRNA is widely distributed and overexpressed in most cancer tissues compared to that in normal tissues. The multiple organ tissue arrays obtained from HAP showed the protein expression of XRCC4 was upregulated in BRCA, COAD, KIRC, LIHC, LUAD and STAD. We performed IHC on liver cancer to confirm the expression of MRPL17 protein in cancers and normal tissue samples. The expression of MRPL17 protein was higher in LIHC compared to normal tissues. These findings suggest an important role of MRPL17 in different cancers. MRPL17’s high expression indicated poor OS and DSS in ACC, KIRC, LGG, LIHC, LUAD and UVM. Moreover, pathologic T stage, pathological M stage, pathologic stage, MRPL17 expression and tumor status were independent biomarkers in LIHC according to the univariate and multivariate regression analyses. The expression of MRPL17 in LIHC was first reported. In the present study, we found deep deletion are the highest MRPL17 alteration in pan-caner and the frequency of genetic variation of MRPL17 was the highest in Glioma. These findings show that MRPL17 can be used as a latent marker for predicting the outcomes of tumors.

Tumor immunotherapy aims to improve the natural immune system and relies on the patients’ own immune function to destroy cancer cells and tumor tissues³². Immunotherapy has revolutionized cancer treatment through the successful use of cytokines, monoclonal antibodies, vaccines, cellular immunotherapy and counter-immunoediting^{33 34}. In addition, immune-related gene expression is considered to be a predictor of immunotherapy for many cancers^{35 36}. Based on the TCGA database, we analyzed the correlation between MRPL17 and more than 40 pan-cancer immune checkpoint genes and verified the correlation of MRPL17 between several immune checkpoint genes, including PD-1, PD-L1, LAG-3 and CTLA-4 in the TIMER 2.0 database. Our data show that MRPL17 has the strongest positive association with these immune checkpoint genes in cancers that are considered important risk factors, such as LIHC and UVM. Meanwhile, in LGG where MRPL17 is a protective factor, the expression of MRPL17 has the strongest negative correlation with these immunosuppressive checkpoint genes. This may explain the association between MRPL17 overexpression and poor outcomes in cancer patients.

In recent years, the tumor microenvironment has been recognized as a critical role in tumor initiation, progression, metastasis, and responses to immunotherapy^{6 37–39}. Under normal circumstances, body's immune system can recognize and harbor tumor cells by interacting with surrounding cells. However, tumor cells can evolve different strategies to escape and survive, making the immune system impeded. The most revolutionary tumor immune-therapy can help rejuvenate the body's natural defenses to eliminate non-self-cancer cells. In our work, we show that MRPL17 was substantially positively correlated with DCs in most tumors and negatively correlated with CD4 T cells in most tumors. Tumor-inducing signals prevent the production of mature, immunologically active DCs and, at the same time, transform tumor-associated DCs (tDCs) from immunostimulatory cell types to immunosuppressive cell types. Due to their high heterogeneity, the tDCs included diverse subtypes, including plasmacytoid DCs (pDCs), conventional DCs (cDCs) and monocyte-derived inflammatory DCs (moDCs), which were marked by different immune markers⁴⁰. Moreover, tDCs can suppress the activation of cytotoxic T cells and promote the generation of immunosuppressive Tregs, which further regulate the immunostimulatory phenotype at an early stage of tumor progression to an immunosuppressive phenotype^{41 42}. The functional role of anti-tumor CD4 + T cells in the TME varies greatly and depends on different types of APCs⁴³. In a murine B cell lymphoma model, APCs can uptake and presentation of tumor antigens on MHC class II molecules to for T cell recognition, which can enable indirect CD4 + T cell-dependent tumor cell killing⁴⁴. CD4 + T cell help supports activation and maintenance of both innate and adaptive anti-tumor immune functions^{45 46}. Moreover, many studies showed that cancer vaccines can lead to substantial activation of CD4 + T cells in patients with cancer^{47 48}. Meanwhile, our results show that MRPL17 was significantly positive correlation to EC and negative correlation to CP, suggesting that high MRPL17 expression may lead to the immunosuppressive microenvironment and immunotherapy resistance.

We used CancerSEA and GSEA to perform pan-cancer functional analyses of MRPL17. Single-cell function analysis showed that MRPL17 was positively related to DNA damage and DNA repair in some tumors. However, the relationship of MRPL17 to DNA damage and DNA repair has not been reported, which needs to be investigated further in future work. Furthermore, the correlation between MRPL17 mRNA and anticancer drug sensitivity was examined. The overexpression of MRPL17 was negatively correlated with the IC50 values of seven types of the drug through the GDSC database, including TGX211 and Dabrafenib. The two drugs had the potential to prevent cancer progression. This finding would help guide clinical drug selection and patient prognosis.

Although the effect of MRPL17 in pan-cancer was described, some limitations shouldnot be overlooked. Firstly, all the analyses were based on bioinformatics analysis, and MRPL17 expression was only confirmed by IHC in LIHC. Secondly, our study did not investigate the molecular mechanism of MRPL17 in cancers. Further studies on the mechanism of MRPL17 expression in tumors are needed in the future.

In summary, our work systematically puts forward that MRPL17 expression was significantly linked with clinical features, prognosis, mutational status, TME, TMB, MSI, tumor stemness and drug sensitivity in several cancers, which provides a promising candidate for therapeutic targets and a new direction for future research.

Acknowledgments

We acknowledge the contributions from TCGA, TIMER2, GEPIA2, Sangerbox 3.0, UCSC, GTEx, cBioPortal, CCLE, CancerSEA, GSCALite, STRING website and XIANTAO Academic. More information can be accessed from correspondence authors.

Author Contributions

H.L. wrote the article; XW.X. and L.Z. processed the data analysis; SK.F. revised the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Clinical Medical Technology Innovation Guide Project of Hunan Province (grant number 2021SK51701).

Informed Consent Statement

Not applicable

Conflict of interest

The authors declare no conflict of interest.

Data Availability Statement

The datasets presented in this study can be found in online repositories.

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No competing interests reported.

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A multidimensional analysis of MRPL17 protein in human tumors

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Abstract

Figures

1.Introduction

2. Materials and Methods

2.1 Gene Expression Analysis of MRPL17 in Pan-Cancer

2.2 Immunohistochemistry (IHC) staining

2.3 Survival prognosis analysis

2.4 Gene Mutation Analysis

2.6 Correlational Research on MRPL17 and TMB and MSI

2.7 Tumor cell stemness

2.8 Single-Cell Functional Analysis of MRPL17

2.9 Drug Sensitivity Analysis

2.10 Co-Expressed Genes and Enrichment Analysis of MRPL17

2.11 Correlation of MRPL17 expression with clinicopathologic characteristics in LIHC

2.12 Immunohistochemistry (IHC)

3. Result

3.1 Significant differences in MRPL17 expression and assessment of MRPL17 as a pan‑cancer prognostic biomarker

3.2 Prognostic value of MRPL17 across cancer types

3.3 Mutation Analysis of MRPL17

3.4 Correlation of MRPL17 expression on immune checkpoints and immunotherapy

3.5 Correlation of MRPL17 expression with immune infiltration

3.6 Tumor stemness and MRPL17 expression

3.7 Single-Cell Functional Analysis of MRPL17

3.8 Drug Sensitivity Analysis

3.9 Protein Interaction Network and Enrichment Analysis of MRPL17

3.10 MRPL17 Is Regarded as an Independent Marker in LIHC

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

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