Microenvironment-related gene IGLL5 has potential to predict the prognosis of clear cell renal cell carcinoma

doi:10.21203/rs.3.rs-1431353/v1

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Microenvironment-related gene IGLL5 has potential to predict the prognosis of clear cell renal cell carcinoma

https://doi.org/10.21203/rs.3.rs-1431353/v1

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Background: Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the urinary system. With the increasing use of immune checkpoint inhibitors, the key roles of the tumour microenvironment (TME) in tumogenesis, tumour development and treatment resistance are gradually recognised. However, the understanding of ccRCC-TME and progression remains unclear.

Methods: Transcriptome RNA-seq data and their corresponding clinical data were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were identified by estimating stromal and immune scores. Common DEGs were identified by protein–protein interaction (PPI) network and univariate Cox regression analyses. Furthermore, Western blot was used to verify the expression level of candidate biomarkers in ccRCC cells. In addition, gene set enrichment analysis and immune infiltration analysis were performed to explore the mechanism of ccRCC and related immune cells. Finally, the correlation between the core genes and clinicopathological characteristics was assessed for potential clinical application.

Results: IGLL5 was the only core gene found through PPI network intersection analysis and Cox regression analysis. IGLL5 was negatively correlated with the prognosis of patients with RCC. The IGLL5 protein expression was elevated in RCC. Clinically, 453 (86.45%) and 71 (13.55%) patients with RCC were stratified into the IGLL5≤16.33 and IGLL5>16.33 groups, respectively.Compared with IGLL5≤16.33, the IGLL5>16.33 group conferred a higher risk of developing high stage (odds ratio [OR], 2.759; 95% confidence interval [CI], 1.651–4.609) and high T stage (OR, 2.686; 95% CI, 1.615–4.467; all P<0.001).

Conclusion: IGLL5 gene is a potential predictor of the high malignancy of RCC. It may serve as a potential immunotherapeutic target for RCC.

IGLL5

Tumour microenvironment

Tumour-infiltrating immune cells

Renal cell carcinoma

According to the Global Cancer Observatory, kidney cancer is responsible for 431,288 new cases and 179,368 deaths in 2020, which account for 2.2% and 1.8% of the global burden of all cancers, respectively[1]. Renal cell carcinoma (RCC) is one of the most common kidney tumours and the most lethal of all genitourinary cancers. The most frequent pathological subtype of RCC is clear cell renal cell carcinoma (RCC), which accounts for approximately 80% of RCC cases[2]. Although ccRCC can be successfully treated with surgery or ablative procedure when detected early; Sadly, up to 30% of cases present with or develop metastases[3] and the median survival time is 13 months[4]. The treatment options for advanced ccRCC are limited compared with those of other types of genitourinary cancers, because most patients with ccRCC are insensitivity to chemotherapy and radiotherapy[5]. Currently, no effective screening strategies for ccRCC have been established yet, and more than half of ccRCC cases are detected incidentally[6], which results in a more delayed diagnosis[7] and unfavorable prognosis. Thus, developing markers for the early diagnosis and proper follow-up of individuals with ccRCC is important. Although a growing array of studies have aimed at identifying prognostic markers to identify ccRCC cases who are likely to be at a higher risk of tumour recurrence and death[8], no ccRCC biomarker is yet available. Therefore, developing predictive markers and therapeutic targets for this deadly disease is urgently needed.

The tumour microenvironment (TME) is composed of malignant cells and non-tumour cells, including immune cells and other kinds of stromal cells[9], whose mutualistic and dynamic interactions build a supportive microenvironment for upcoming tumour cells from initiation to progression and to full malignancy[10, 11]. Evidence indicated that the TME is closely correlated with cancer progress, clinical prognosis and treatment response[12, 13]. Thus, TME-related gene biomarkers are extensively investigated[14, 15] and are potential to become prognostic markers and therapeutic targets. Immune and stromal cells, which are the two main non-tumour cell types, play a remarkably important role in the TME. Stromal components affect disease prognosis[16] and are therefore recognised as potential therapeutic targets for inhibiting tumour growth and improving drug resistance[17]. Immune components usually exhibit immunosuppressive activities[18] but also show tumour-promoting effects by regulating tumour cell metabolism[19]; thus, they have emerged as targets for immunotherapy. Understandling TME profile in various tumours is needed to explore the mechanism of tumourigenesis and progression to find valuable therapeutic targets. However, the correlation of TME with ccRCC remains ambiguous compared with other carcinomas[20], and TME-related prediction and prognostic markers for ccRCC are still lacking, which has led to the failure of some novel therapeutic strategies[21]. Despite several studies[22, 23] have attempted to explore the relationship between ccRCC prognosis and TME-related biomarkers, their conclusions showed remarkable heterogeneity. A growing body of evidence demonstrated that tumour-infiltrating immune cells (TICs) in the TME serve as a promising indicator[24] for cancer such as lung[25] and breast cancers[26]. However, their roles in ccRCC are controversial and elusive[27].

Therefore, the present study used ccRCC data from The Cancer Genome Atlas (TCGA), to clarify the biological characteristics of immune and stromal cells using the ESTIMATE algorithm and identify crucial TME-related genes that may predict the prognosis of ccRCC through the identification of common differentially expressed genes (DEGs) and by protein–protein interaction (PPI) network analysis and Cox regression analysis. Subsequently, the gene biomarkers were verified through internal and external validation, and the potential mechanism was explored through gene set enrichment analysis (GSEA) and immune infiltration analysis to assess their clinical application using the X-tile software.

Analyses of Kaplan–Meier survival curve and clinicopathological parameters

Transcriptome RNA-seq data with corresponding clinical data were obtained from TCGA database (https://portal.gdc.cancer.gov/). The ESTIMATE algorithm was run in R software (version 3.6.3) to estimate the proportion of immune/stromal components in the TME of each tumour sample. The survival of patients with ccRCC who were grouped according to the median immune, stromal, and ESTIMATE scores (combined immune and stromal scores) was analysed by Kaplan–Meier analysis using the survival and survminer packages. Moreover, the correlation of these scores with clinicopathological characteristics, such as age, sex, laterality, neoplasm, grade, clinical stage, tumor (T) stage, node (N) stage and metastasis (M) stage, were analysed using the limma and ggpubr packages with Wilcoxon rank sum test.

Core gene identification

The DEGs grouped by the median immune and stromal scores were described by gene expression heatmaps using the limma and pheatmap package. In the heatmaps, the row name is the gene name, and the column name is the ID of the samples. An absolute value of log2 fold change > 1 (high-score cohort/low-score cohort) and false discovery rate < 0.05 indicate a statistically significant difference. The common up-regulated and down-regulated DEGs between different immune and stromal scores were demonstrated in Venn diagrams constructed using the venndiagram package. Then, Gene Ontology (GO) annotation and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis were performed using the clusterProfiler, enrichplot and ggplot2 packages, in which P < 0.05 and q < 0.05 indicate significant enrichment. Furthermore, the common DEGs were used to describe the PPI network with reference to the Search Tool for Retrieval of Interacting Genes, and the interaction was visualised using Cytoscape (version 3.6.1). Modules with an interaction relationship confidence larger than 0.95 were selected to build a network, and adjacent nodes more than 5 were selected to screen the core gene. The correlation between the common DEGs and survival time was analysed by univariate Cox regression using the survival package. The significant hazard risks of the DEGs (P < 0.05) were presented in a forest plot. The common core DEG between the DEGs with PPI leading nodes and the DEGs with significant hazard risk were screened with a Venn diagram using the venndiagram package.

Core gene validation

The mRNA expression levels of the adjacent normal renal tissues and renal tumour tissues of patients with ccRCC were compared by Wilcoxon rank-sum test using the limma, beeswarm and ggpubr packages. The differentiated expression of the IGLL5 protein between kidney cell lines (ACHN, SW839, 293T, Caki-1, 786-O, 769-P, A498 and Renca) and normal HK2 cells lines was examined by Western blot. The cell lines ACHN, SW839, 293T, Caki-1, 786-O, 769-P, A498, Renca, HK2 and 293T were obtained from the American Type Culture Collection. The renal cells were incubated with RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) containing 10% foetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) in an atmosphere with 5% CO₂ at 37°C. Then, radioimmunoprecipitation assay buffer with protease and phosphatase inhibitors was used to harvest cell protein. The protein sample was isolated with sodium dodecyl sulphate and acrylamide gels (120 V, 1 h), transferred to polyvinylidene difluoride membrane (110 V, 2 h), incubated with 5% skimmed milk for 1 h and incubated with primary antibodies overnight at 4°C. Then, the membranes were washed with tris-buffered saline with Tween for three times (10 min each) and incubated with secondary antibodies for 1 h at room temperature. Protein expression was detected with an enhanced chemiluminiscence ECL system and analysed with Image Lab. The following antibodies were used anti-IGLL5 (1:1000, cat. no. ab48394, Abcepta), anti-vinculin (1:1000, sc-73614, Santa) and goat anti-rabbit IgG (1:5000, cat. no. A9169, Sigma-Aldrich). Student's t-test was performed in GraphPad Prism 7.0 to analyse the data between two groups, and P < 0.05 was considered statistically significant.

The Hallmark collection (signature gene sets) and C2 collection (v7.1, immunologic gene sets) in the Molecular Signature Database (MSigDB) were analysed by GSEA to search for the enriched pathways of the core gene. Differences were considered statistically significant at nominal P < 0.05.

The proportion of TICs in ccRCC tumour samples were described by barplot through the CIBERSORT algorithm, and the correlation amongst these TICs were shown as heatmap using the corrplot package. The TIC proportions in the high and low core gene expression groups of ccRCC tumour samples were compared and demonstrated in violin plots using the limma and vioplot packages. The significant TICs were selected and described as scatter plots by correlation test (Pearson coefficient) using the limma, ggplot2, ggpubr and ggExtra packages. Only cases with a P < 0.05 were selected for follow-up analysis. The common TICs were screened by taking the intersection of the two analysis and displayed in a Venn diagram.

Core gene clinical correlation

Data from core gene expression and clinical traits were merged. Then, the optimal cut-off point of IGLL5 expression was established based on the X-tile programme (Yale University, CT, USA)[28] to further explore the link between the core gene and clinical traits. A P value < 0.05 was considered statistically significant. Statistical analyses were performed using the SPSS 23.0 software (Chicago, IL, USA). Categorical variables are presented as number, and the differences were compared byChi-square test or Fisher’s exact test.

Study flow (Fig. 1)

Gene screening

The transcriptome RNA-seq data of 539 tumour tissues and 72 adjacent normal tissues from patiens with ccRCC were collected from the TCGA database. The correlations of immune and stromal scores with survival and clinicopathological parameters were establised. A total of 93 DEGs, including 44 up- and 49 down-regulated genes, were identified. These DEGs were identified to construct the PPI network and conduct univariate Cox regression analysis. Amongst the DEGs, IGLL5 was the only common gene (Fig. 2–5).

Gene validation and clinical correlation

High IGLL5 mRNA expression was validated in ccRCC tissues from the TCGA database. The IGLL5 protein expression was up-regulated in ccRCC cell lines assessed with Western blotting. The potential mechanism of IGLL5 was described by signalling pathways (GSEA) and correlated TICs (CIBERSORT). An IGLL5 cut-off value of 16.33 was identified and then used to explore the clinical applications. Then, the survival curves and related clinicopathological characteristics of different groups were calculated (Fig. 6–9 and Table 1).

Table 1

IGLL5 was a strong risk factor for the degree of malignancy of ccRCC
Clinical Characteristics	IGLL5 ≤ 16.33 (n = 456)	IGLL5 > 16.33 (n = 74)	OR(95%CI)	p-value
Stage (n,%)			2.754(1.659–4.569)	< 0.001
Ⅰ+Ⅱ	294(64.47)	29(39.19)
Ⅲ+Ⅳ	162(35.53)	44(59.46)
T (n, %)				< 0.001
T1 + T2	308(67.54)	32(43.24)	2.731(1.657–4.503)
T3 + T4	148(32.46)	42(56.76)

Kaplan–Meier survival curve and clinicopathological parameters

The patients with high immune score had significantly shorter survival time than those with low immune score (P < 0.05), whereas patients with high stromal score and ESTIMATE score had shorter survival time but without significant difference (Fig. 2). Significantly higher immune scores were found amongst male patients and patients with neoplasm, G3–G4, Stage III–IV, M1 or T3–T4 (Fig. 3). Patients who were more than 60 years old and and those with T3–T4 stages had significantly higher stromal score (P < 0.05, Supplementary Fig. 1). A significantly higher ESTIMATE score was found amongst the same patients with high immune score, except those with neoplasm (Supplementary Fig. 2).

DEG identification and GO/KEGG enrichment analyses

As shown in the heatmaps, 656 immune score-related DEGs, including 510 up-regulated and 146 down-regulated genes, and 411 stromal score-related DEGs, including 259 up-regulated and 152 down-regulated genes, were obtained (Figs. 4A and 4B). Furthermore, 44 up-regulated and 49 down-regulated DEGs were shared by both groups (Figs. 4C and 4D). The GO enrichment analysis showed that 93 DEGs were remarkably enriched in humoral immune responses, such as B cell, leukocyte, lymphocyte and mononuclear cell proliferation (Fig. 4E). The KEGG enrichment analysis showed that the 93 DEGs were enriched in the following pathways: cytokine–cytokine receptor interaction, primary immunodeficiency, synthesis and degradation of ketone bodies and viral protein interaction with cytokines and cytokine receptors (Fig. 4F).

PPI network and Cox regression analyses

The PPI network showed that 52 of the 93 DEGs, including 29 up-regulated and 23 down-regulated DEGs, had an interaction relationship confidence larger than 0.95. The top 20 genes ranked by the number of nodes had more than five adjacent nodes. Twenty-three DEGs were screened by univariate Cox regression analysis and presented through forest plot. The two analyses showed that IGLL5 was the only common DEG in Venn diagram (Fig. 5).

IGLL5 validation

As shown in Fig. 6, IGLL5 mRNA expression was remarkably higher in the tumour tissues compared with the adjacent normal tissues in independent sample and paired Wilcoxon rank-sum tests. IGLL5 protein was remarkably up-regulated in three human ccRCC cell lines (Caki-1, 786-O and 769-P) compared with normal HK2 cells. Meanwhile, similar results was showed in another cell line 293T.

In the HALLMARK signature gene sets, the IGLL5 up-regulated pathways showed substantially higher enrichment in the P53 pathway, KRAS signalling and apoptosis. The IGLL5 down-regulated pathways had remarkably higher enrichment in adipogenesis and fatty acid metabolism. In the C2 immunologic gene sets, the IGLL5 up-regulated pathways showed considerably higher enrichment in immune cell pathways, such as natural killer (NK) cell-mediated cytotoxicity pathway, T cell receptor signalling pathway and B cell receptor signalling pathway, and the down-regulated pathways were enriched in multiple metabolic signalling pathways (Fig. 7 and Supplementary Table 1).

The proportion and interrelation of 22 types of TICs are presented in Supplementary Fig. 3. The difference analysis of the violin plot showed that 14 kinds of TICs had remarkable differences between the two groups (Fig. 8A). The correlation analysis of scatter plots also showed that 14 kinds of TICs had clear associations with IGLL5 expression (Fig. 8B). Finally, Venn diagram plotting screened 13 kinds of common TICs (Fig. 8C). As shown in Fig. 8B, memory B cells, plasma cells, CD8 T cells, CD4 memory activated T cells, follicular helper T cells, regulatory T cells (Tregs) and gamma delta T cells were positively correlated with IGLL5 expression, and their Pearson correlation coefficients were 0.13, 0.72, 0.2, 0.29, 0.16, 0.25 and 0.21, respectively. Conversely, CD4 memory resting T cells, resting NK cells, monocytes, M2 macrophages, dendritic cells and resting mast cells were negatively correlated with IGLL5 expression, and their Pearson correlation coefficients were − 0.18, − 0.24, − 0.22, − 0.16, − 0.19 and − 0.26, respectively.

Correlation of IGLL5 expression and clinicopathological characteristics

A total of 530 samples were acquired after merging and the identified optimal cut-off point of IGLL5 expression was 16.33 via the X-tile software (Fig. 9). Thus, 456 (86.04%) and 74 (13.96%) patients were ccRCC were stratified into the IGLL5 ≤ 16.33 and IGLL5 > 16.33 groups (Supplementary Table 3). Between the two subgroups, IGLL5 expression was significantly different amongst Stages and T stages (all P < 0.001). A higher IGLL5 expression conferred a higher risk of developing high stage (odds ratio [OR], 2.754; 95% confidence interval [CI], 1.659–4.569) and high T stage (OR, 2.731; 95% CI, 1.657–4.503; Table 1).

This study found that the TME-related gene, IGLL5 has potential as a robust indicator for TME status and a strong predictor for the ccRCC population in the TCGA database. The ESTIMATE algorithm was used to calculate the immune and stromal scores of ccRCC tumour samples. The Kaplan–Meier survival curve showed that the patients with high immune score had worse prognosis. IGLL5 was the only common gene identified by PPI network and Cox regression analyses. Internal and external validation were performed to verify that IGLL5 is highly expressed in ccRCC tumour tissues showed that IGLL5 > 16.33 conferred a higher risk of developing high Stage and high T stage in clinical application; thus, it might serve as an indicator for the early detection of ccRCC development and recurrence.

TME is the complex cellular milieu where the tumour is located. The importance of TME in tumour formation, progression, invasion and recurrence has been well shown by a wealth of evidence[17, 29, 30]. In the present study, the Kaplan–Meier survival curve revealed that patients with high immune score had significantly shorter survival time than those with low immune score (P < 0.05, Fig. 2A). Moreover, high immune score led to a higher tumour grade and higher Stage than low immune score (Fig. 3). These results indicated that patients with higher immune score had considerably worse overall survival. This finding also corroborated with previous studies[31, 32]. This finding demonstrated that the immune component of the ccRCC TME may be double-edged sword. The immune system, as a defensive mechanism, could inhibit tumour development to protect the host. However, immune components might promote tumour cell growth and progression once cancer immunogenicity is reduced[33]. Furthermore, several cytokines and chemokines produced by tumour cells, such as TGF-β and VEGF, promote the recruitment and activation of immune-inhibitory cells[34] and lead to an immunosuppressive state[35], which results in the growth of the tumour, whose bidirectional interactions may function as an ‘amplification loop’ to enhance ccRCC tumour progression[36].

Based on the analyses of the PPI network, univariate Cox regression and Venn diagram, IGLL5 was identified as a potential prognostic biomarker (Figs. 4 and 5) that is negatively correlated with the prognosis of patients with ccRCC. This finding was consistent with previous reports[31, 32] and was internally and externally validated. The mRNA expression of IGLL5 was remarkably higher in the tumour tissues compared with the adjacent normal tissues (Figs. 6A and 6B). Additionally, the protein expression of IGLL5 was remarkably up-regulated in Caki-1, 786-O and 769-P cell lines (Fig. 6C). The 293T cell line, derived from human embryonic kidney cells, displays a cancer stem cell-like properties[37]. Compared to the HK2 cells lines, the expression of IGLL5 was significantly increased in 293T cell line, which may indicate the role of IGLL5 in tumorigenesis and development. Multiple studies reported that IGLL5 plays a key role in tumour pathogenesis, especially in haematological malignancies. As oncogenic drivers, the coding and non-coding mutations of IGLL5 have been observed in chronic lymphocytic leukaemia[38], Burkitt lymphoma[39], diffuse large B-cell lymphoma[40] and multiple myeloma[41] and lead to rapid tumourigenesis. IGLL5 is also a biomarker for the early progression of multiple myeloma[42] and serves as a predictor for the relapse-free survival of patients with breast cancer[43]. One possible mechanism is that IGLL5 expression might remodel an immunosuppressed microenvironment via the infiltration of immune-inhibitory cells or the loss of anti-tumour T cell activity by pro-inflammatory cytokines and various immune cells pathways. These interactions would be enhanced further as the expression of IGLL5 exceeds 16.33, which results in the advanced tumour stage and worse prognosis of patients with ccRCC.

Indeed, GSEA and immune infiltration analyses were performed to further unveil the potential mechanism of IGLL5. IGLL5 had a strong association with inflammatory and immune responses (Figs. 7A, Fig. 7C and Supplementary Table 1). According to the analysis results of the spatial transcriptomics of patients with periodontitis, IGLL5 is remarkably up-regulated in the inflamed areas of the gingival connective tissue[38], which suggests the unique role of IGLL5 in chronic inflammatory response. KEGG enrichment analysis also showed that IGLL5 was substantially enriched in cytokine– cytokine receptor interaction (Fig. 7C), which implies that high IGLL5 expression might activate various pro-inflammatory cytokines. In fact, ccRCC is largely considered to be a pro-inflammatory tumour. Pro-inflammation mediators,such as IL-1β, IL-6 and TNF, are produced at high levels in ccRCC and stimulate immune-inhibitory cell activation to promote tumour growth and metastasis[44]. Moreover, as a member of the immunoglobulin gene superfamily[45], IGLL5 was suggested to be closely related to various immune responses. Pathway enrichment analysis revealed that IGLL5 was remarkably enriched in the NK cell-mediated cytotoxicity pathway, T cell receptor signalling pathway and B cell receptor signalling pathway (Fig. 7C). Besides, the immune infiltration analysis demonstrated that IGLL5 was positively correlated with 7 kinds of TICs (B cell memory, plasma cells, CD8 T cells, CD4 memory activated T cells, follicular helper T cells, Tregs and gamma delta T cells; Fig. 8), which fully illustrated a high immune infiltration state in ccRCC. This result was in accordance with previous reports[31, 32]. The study of Chevrier et al. showed that half of infiltrating immune cells in the TME are the T cells of ccRCC tumours and have immunosuppressed properties[46]. This finding implies that high levels of infiltrating T cells leads to an immunosuppressed microenvironment. This finding is in line with our data and that is most of the cell types tightly linked to IGLL5 are T cells. Tregs belong to the immune-inhibitory cells and mediate immune dysfunction by inhibiting the secretion of immunosuppressed signals. When T cells are persistently exposed to antigen and/or inflammatory cytokines, they could lose their effector functions[44]. Thus, one possible mechanism is that the expression of IGLL5 might remodel an immunosuppressed microenvironment via the infiltration of immune-inhibitory cells or the loss of anti-tumour T cell activity by pro-inflammatory cytokines and various immune cell pathways, which result in the poor prognosis of patients with ccRCC.

Finally, we explored the relation between the expression level of IGLL5 and clinicopathological characteristics to verify the clinical application of IGLL5. Firstly, the optimal cut-off value of IGLL5 was identified as 16.33 using the X-tile software, and IGLL5 was split into two groups based on this threshold (Fig. 9). Secondly, the comparison of the two groups showed that the risk of developing high Stage and high T stage in the IGLL5 > 16.33 group was two times higher than that in the IGLL5 ≤ 16.33 group (all P < 0.001). Thus, IGLL5 > 16.33 may suggest a later tumour stage and worse prognosis.

The strength of our study is that the protein expression of IGLL5 in ccRCC cells was externally verified by Western blotting, which further proves the role of IGLL5 in tumorigenesis and development. The expression level of IGLL5 was split into two groups depending on the optimal cut-off value calculated by the X-tile software to confirm clinical application of IGLL5. However, some limitations of our study should be acknowledged. Randomised controlled studies are needed to confirm the clinical value of IGLL5 to improve the outcome of patients with ccRCC in our future study.

Our study identified IGLL5 as a robust indicator for the ccRCC population. It may give us a new prospective for predicting disease prognosis, and it may serve as a potential immunotherapeutic target for patients with ccRCC but its clinical application warrants further study.

ccRCC

Clear cell renal clear cell carcinoma

TME

Tumor microenvironment

TCGA The Cancer Genome Atlas

DEGs

Differentially expressed genes

ESTIMATE

Estimation of STromal and Immune cells in Malignant Tumours using Expression data

PPI

Protein–protein interaction

TICs

infiltrating immune cells

IGLL5

immunoglobulin lambda-like polypeptide 5

MSigDB

Molecular Signatures Database

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GSEA

Gene set enrichment analysis

AJCC

Americal Joint Committee on Cancer.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The datasets generated during the current study are available in The Cancer Genome Atlas (TCGA) repository, [https://portal.gdc.cancer.gov/].

Competing interests

All authors declare that they have no competing interests.

Funding

This study was supported by the National Nature Science Foundation of China (No. 81771485 and 81971290), the Key Research and Development Program of Shaanxi Province (No. 2020SF-136), the Program for New Scientific and Technological Star of Shaanxi Province (No. 2019KJXX-046), Young Talent Support Plan of Shaanxi Province; The Clinical Research Award of the First Affiliated Hospital of Xi'an Jiaotong University, China (No. XJTU1AF2021CRF-012).

Authors’ contributions

NB and WG contributed to the study design, WCD and WFL collected the online data and drafted the manuscript. NB and WFL performed the bioinformatics and statistical analysis. YM, ZDK and WG revised of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets.

Author details

¹Department of Anesthesiology & Center for Brain Science & Center for Translational Medicine, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, China;²Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China; ³Department of Radiology, the First Hospital of Qinhuangdao, Qinhuangdao, P.R, China.

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Microenvironment-related gene IGLL5 has potential to predict the prognosis of clear cell renal cell carcinoma

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Abstract

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Introduction

Methods

Analyses of Kaplan–Meier survival curve and clinicopathological parameters

Core gene identification

Core gene validation

Core gene clinical correlation

Results

Kaplan–Meier survival curve and clinicopathological parameters

DEG identification and GO/KEGG enrichment analyses

PPI network and Cox regression analyses

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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