A Novel Tumor Mutation Burden Related lncRNA Signature Identified Prognosis and Tumor Immune Microenvironment Features in Clear Cell Renal Cell Carcinoma

Abstract

Background

Emerging evidence indicates that long noncoding RNA (lncRNA) plays an important biological role in clear cell renal cell carcinoma (ccRCC); however, the clinical value of tumor mutation burden-related lncRNA in ccRCC patients is unknown yet.

Methods

Somatic mutation profiles and lncRNA expression data of ccRCC were downloaded from the TCGA database. We retrospectively analyzed lncRNA expression data and survival information from 116 patients with ccRCC fromJanuary 2013 to January 2014. Univariate and multivariate Cox regression analyses were performed to construct lncRNA signature, and the prognosis value was determined by Kaplan-Mayer and receiver operating characteristic curve (ROC) analysis.

Results

Based on 160 differentially expressed TMB-related lncRNAs, two TMB-related molecular clusters with distinct immune checkpoints expression and immune cells infiltration were established for ccRCC patients. Moreover, a novel TMB-related lncRNA signature was constructed based on five lncRNAs for individualized prognosis assessment. High-risk group represents significantly worse overall survival in all cohorts. The area under the ROC curve was 0.716, 0.775 and 0.744 in the training cohort, testing cohort and TCGA cohort, respectively. Results of qRT-PCR successfully validated the expression levels of AP002360.3, LINC00460, AL590094.1, LINC00944 and LINC01843 in HK-2, 786-O, 769-P and ACHN cells. More importantly, the predictive performance of TMB-related lncRNA signature was successfully validated in an independent cohort of 116 ccRCC patients at our institution.

Conclusion

This study successfully developed and validated a novel TMB-related lncRNA signature for individualized prognosis assessment of ccRCC patients.

1. INTRODUCTION

Renal cell carcinoma (RCC) is one of the most common malignant tumors, accounting for 2% to 3% of adult malignancy, with an estimated incidence of approximately 330,000 new cases each year [1]. Clear cell renal cell carcinoma (ccRCC) is the most common type and occupies around 80-90% of renal cell carcinoma [1, 2]. Despite the rapidly evolving experimental technologies and therapeutic guidelines in this field, such as tyrosine kinase inhibitors (TKI) [3], optimized surgery [4] and immunotherapy [5], there is no curative treatment for advanced ccRCC. Therefore, it is warranted to develop candidate therapeutic biomarkers for improving the survival of ccRCC patients.

Tumor mutation burden (TMB) is defined as the total number of nonsynonymous mutations per megabase and outlined genomic mutation [6], which has usually been regarded as a hopeful biomarker for predicting the response of immunotherapy [7]. Furthermore, TMB levels have demonstrated promising prognostic value in several tumors (e.g., endometrial cancer, thyroid carcinoma, cutaneous melanoma, etc.) [8-10]. Long noncoding RNA (lncRNA) is a class of RNA molecules whose transcripts are large than 200 nucleotides without coding potential [11]. There is emerging evidence that lncRNA is the underlying mechanism for the progression of many diseases, especially cancer [12]. However, to our knowledge, the studies focusing on the correlation between TMB-related lncRNAs and the prognosis value of ccRCC patients are still limited.

In the present study, two TMB-related molecular clusters of ccRCC were established with distinct immune checkpoints expression and immune cells infiltration. Then, we developed and successfully validated a novel TMB-related lncRNA signature by univariate and multiple Cox regression analyses. Correlations of TMB-related lncRNA signature with TMB burden, immunophenoscore, MMR genes and immune checkpoints expression have been explored. In addition, we performed qRT-PCR in HK-2, 786-O, 769-P and ACHN cell lines to validate the expression levels of five risk lncRNAs. More importantly, the predictive performance of TMB-related lncRNA signature was successfully validated in an independent cohort of 116 ccRCC patients at our institution.

2. MATERIALS AND METHODS

2.1. Data Resources

The study was approved by the Human Research Ethics Committee of Fujian Medical University, and informed consent was obtained from patients and volunteers before sample collection. We retrospectively analyzed lncRNA expression data and survival information from 156 patients who underwent treatment and pathologically diagnosed with ccRCC at the Department of Urology, the First Affiliated Hospital of Fujian Medical University from January 2013 to January 2014. The inclusion criteria were as follows: 1) diagnosed with pathologically confirmed ccRCC; 2) have available lncRNA expression data and survival information. The exclusion criteria were as follows: 1) patients with other cancer; 2) history of hypertension, heart disease, diabetes, cerebrovascular accidents, psychiatric illness and/or other long-term chronic diseases.

Somatic mutation data, transcriptome sequencing information and clinical data of ccRCC patients were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). HTSeq-FPKM file type was chosen for transcriptome sequencing profiles of all ccRCC samples, including 539 ccRCC tumor samples and 72 adjacent-tumor samples. The details of the clinical characteristics of cohorts are shown in Table 1.

Table 1.

Clinicopathologic features of three ccRCC cohorts in our study.

Covariates	Type	Total Cohort (n=513)	Testing Cohort (n=256)	Training Cohort (n=257)	Our Institutional Cohort (n=116)
Age	<=65	340 (66.28%)	173 (67.58%)	167 (64.98%)	94 (81.03%)
-	>65	173 (33.72%)	83 (32.42%)	90 (35.02%)	22 (18.97%)
Gender	Female	176 (34.31%)	88 (34.38%)	88 (34.24%)	35 (30.17%)
-	Male	337 (65.69%)	168 (65.62%)	169 (65.76%)	81 (69.83%)
WHO/ISUP Grade	G1-2	231 (45.03%)	108 (42.19%)	123 (47.86%)	103 (88.79%)
-	G3-4	274 (53.41%)	145 (56.64%)	129 (50.19%)	13 (11.21%)
-	Unknow	8 (1.56%)	3 (1.17%)	5 (1.95%)	-
AJCC-Stage	Stage I-II	311 (60.62%)	148 (57.81%)	163 (63.42%)	96 (82.76%)
-	Stage III-IV	199 (38.79%)	107 (41.8%)	92 (35.8%)	20 (17.24%)
-	Unknow	3 (0.58%)	1 (0.39%)	2 (0.78%)	-
T	T1-2	329 (64.13%)	156 (60.94%)	173 (67.32%)	98 (84.49%)
-	T3-4	184 (35.87%)	100 (39.06%)	84 (32.68%)	18 (15.52%)
N	N0	229 (44.64%)	115 (44.92%)	114 (44.36%)	112 (96.55%)
-	N1	16 (3.12%)	8 (3.12%)	8 (3.11%)	4 (3.45%)
-	Unknow	268 (52.24%)	133 (51.95%)	135 (52.53%)	-
M	M0	407 (79.34%)	192 (75%)	215 (83.66%)	110 (94.83%)
-	M1	78 (15.2%)	47 (18.36%)	31 (12.06%)	6 (5.17%)
-	Unknow	28 (5.46%)	17 (6.64%)	11 (4.28%)	-

Note: ccRCC, Clear cell renal cell carcinoma.

2.2. Identification of TMB-Related lncRNA

To screen TMB-related lncRNA, Perl (64-bit, version5.30.1.1, http://www.perl.org) was used to perform the following steps: a. calculate the total number of somatic mutations in each sample; b. rank the samples in descending order by the total number of somatic mutations; c. assign the top 10% of samples to the high-TMB group, and the last 10% to the low-TMB group; d. compute and compare the expression levels of lncRNAs between the high-TMB group and the low-TMB group. Differentially expressed lncRNAs (log2FC>1 or <-1 and FDR<0.05) were defined as TMB-related lncRNA. Finally, a total of 160 TMB-related lncRNAs were selected.

2.3. Establishment of TMB-Related lncRNA Clusters by Consensus Clustering Analysis

Based on the expression levels of 160 lncRNAs, 539 ccRCC samples were classified into two clusters by using the R package “limma”. We compared the abundance of TMB, the expression levels of MMR genes and immune checkpoint genes in the two clusters using the R package “limma”. ESTIMATE, a method for inferring the fraction of stromal and immune cells in tumour samples using gene expression signature [13, 14], was implemented to quantify the TME scores of ccRCC samples. CIBERSORT, an algorithm for analyzing the cell composition of complex tissues based on their gene expression profiles, was utilized to evaluate the proportion of 22 immune cell subtypes in the ccRCC sample [15].

2.4. Construction and Validation of TMB-Related lncRNA Signature for ccRCC

Univariate Cox regression analysis was performed to identify lncRNA related to the prognosis of ccRCC patients by using the “survival” package. Multiple Cox regression analysis was applied to construct the TMB-related lncRNA signature. The risk score for each sample was calculated for predicting the prognosis of ccRCC patients as follows: Risk score=explncRNA1*coef lncRNA1 + explncRNA2*coef lncRNA2 +… + explncRNAi*coef lncRNAi. Based on the median cutoff point of risk score in the training cohort, patients with ccRCC were then sorted into high-risk and low-risk groups. Kaplan-Meier survival analysis and time-dependent ROC analysis were applied to investigate the predictive accuracy of lncRNA signature. Additionally, univariate and multivariate Cox regression analyses were used to verify the independence of lncRNA signature from other predictive factors. All statistical analyses were conducted using R software (version 4.0.3) with specific packages.

2.5. Correlation of TMB-Related lncRNA Signature with TMB Burden, MMR Genes and Immune Checkpoints Expression and Immunophenoscore Analysis

The differences in the TMB burden, the expression levels of MMR genes and immune checkpoint genes between high-risk and low-risk groups were explored. P value less than 0.05 was considered statistically significant. The immunophenoscore (IPS) information of ccRCC patients was obtained from the Cancer Immunome Atlas (TCIA) (https://tcia.at/home) and was analyzed in R. An immunotherapy treatment cohort of ccRCC from Miao’s study, which included immunotherapy response data and transcriptomic information, was used to assess the value of lncRNA signature [16]. The cohort included 15 patients who had received treatment with PD-1 blockade.

2.6. Evaluation of Tumor Immune Microenvironment and Gene Cohort Enrichment Analysis

CIBERSORT algorithm was used to analyze the differences in immune cell infiltration between high-risk and low-risk groups. Results with P< 0.05 have been presented in the boxplot form. XCell, a novel method for inferring 64 immune and stromal cell types based on gene signature [17], was performed to identify the proportion of immune and stromal cells in the tumor microenvironment. To investigate which specific biologically significant gene cohorts of the high-risk and low-risk groups were significantly associated, we implemented gene cohort enrichment analysis (GSEA) in the TCGA cohort using software obtained from the official website (http://software.broadinstitute.org/gsea/index.jssp) [18]. P<0.05 was considered statistically significant.

2.7. Cell Culture

Human proximal tubule epithelial cell line HK-2 (Procell CL-0109), human ccRCC cell lines 786-O (Procell CL-0010), 769-P (Procell CL-0009) and ACHN (Procell CL-0021) were purchased from Procell Life Science & Technology Co., Ltd (Wuhan China). The HK-2 cell line was cultured in Ham’s F-12K medium (Procell, Wuhan, China). Respectively, the 786-O and 769-P cell lines were cultured in RPMI 1640 medium (Meilunbio, Dalian, China), while the ACHN cell line was cultured in MEM EAGLE medium (Biological Industries, Beit HaEmek, Israel) supplemented with 10% fetal bovine serum (Viva Cell Biosciences, Shanghai, China) and 1% penicillin-streptomycin. More importantly, the standard humidified incubator was set to 37 degrees and 5% CO2.

2.8. Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR)

To validate the repeatability and reliability of the TMB-related lncRNA signature, we performed qRT-PCR for five risk lncRNAs in 116 ccRCC samples. To validate different lncRNAs expression levels, we performed qRT-PCR in HK-2, 786-O, 769-P and ACHN cell lines. The total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA). Then, reverse transcription reactions were performed using TransScript^® Green One-Step qRT-PCR SuperMix (TransGen Biotech, Beijing, China) in only one step on the basis of the specification. Then, we conducted qRT-PCR to detect the expression levels of five risk lncRNAs with the Step One PlusTM PCR System (Applied Biosystems) by using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) following the instructions. The expression levels of five risk lncRNAs were normalized based on the expression of GAPDH.

The primer sequences were as follows:

GAPDH (homo): forward 5-GGTGTGAACCATGAGA AGTATGA-3,

Reverse 5-GAGTCCTTCCACGATACCAAAG-3, product size 123 bp;

AP002360.3 (homo): forward 5-CCTGTACCTTGCATT AAGAACCTA-3,

Reverse 5-TCCTTCATTATTATCCTGGCTCTG-3, product size 142bp;

LINC00460 (homo): forward 5-CCCACCCAAATTCG TATGCTAA-3,

Reverse 5-GCCTTCTCGCTGTGTTCTAC-3, product size 185bp;

AL590094.1 (homo): forward 5-TCAGCAGTAAGTGA ACAGCAATA-3,

Reverse 5-TTAGCCACAATCCTATTCCAACAT-3, product size 176bp;

LINC00944 (homo): forward 5-CTCTTAATCCTCTGT CCTCCATCA-3,

Reverse 5-AGTCATTCCATTCCACAGTCTCT-3, product size 135bp;

LINC01843 (homo): forward 5-GAAGGCAGAGAC CAGAGTTATG-3,

Reverse 5-TTACAGCAGCAGACACTTATTCC-3, product size 178bp.

3. RESULTS

3.1. Identification of TMB-Related lncRNAs and Unsupervised Hierarchical Cluster Analyses

160 lncRNAs differentially expressed between low-TMB and high-TMB groups were filtered out (Fig. 1A). Among them, 94 lncRNAs were up-regulated and 66 lncRNAs were down-regulated in the TMB-high group. Unsupervised hierarchical clustering analysis was performed, and 539 ccRCC samples were clustered into two clusters (Fig. 1B).

Fig. (1).

Identification of tumor mutation burden-related lncRNAs. (A) Heatmap of the differentially expressed lncRNAs based on tumor mutation burden; (B) Unsupervised clustering; (C) Boxplots of tumor mutation burden; (D-E) Boxplots of MMR genes MLH1 and PMS2; (F-H) The boxplots indicated the differences in immune scores, stromal scores and estimate score between Cluster1 and Cluster2. (I) Differences in the levels of infiltration of the 22 immune cells between Cluster1 and Cluster2. TMB, tumor mutation burden; ICIs, immune checkpoint inhibitors.

We analyzed the relationship between TMB-related lncRNAs expression patterns and immunotherapy response biomarkers. As shown in Fig. (1C), the TMB in Cluster2 was significantly higher than that in Cluster1 (P=8.9e−12). The two MMR genes, MLH1 and PMS2, expressions were higher in Cluster1 than in Cluster2 (P =1.4e-10, P =0.046, Figs. 1D and 1E). The expression levels of PDCD1, CTLA4, BTLA, LAG3, HAVCR2, LGALS9, and PDCD1LG2 in Cluster2 were significantly higher than in Cluster1 (Fig. 2). By the ESTIMATE algorithm, we observed that the estimate score, immune score and stromal score in Cluster2 were all higher than in Cluster1 (P=5.1e-15, P=4.7e-16, P=1.1e-10, Figs. 1F-1H). Furthermore, CIBERSORT algorithm indicated that the macrophages M1 (P =0.0005), CD8⁺ T cells (P=0.0047) and naive CD4⁺ T cells (P=2.503e-05) in Cluster1 were significantly higher than those in Cluster2, while the levels of macrophages M0 (P=0.0021), Eosinophils (P=0.0112), and activated Dendritic cells (P=0.0344) were lower than those in Cluster2 (Fig. 1I).

Fig. (2).

The different expressions of immune checkpoints between the clusters.

3.2. Construction of TMB-Related lncRNA Signature

513 ccRCC samples were divided randomly into the training cohort (n=257) and the testing cohort (n=256). Univariate Cox regression analysis was performed in the training cohort, and 8 lncRNAs with P<0.001 were obtained for further analysis (Table 2). Finally, five lncRNAs (AP00 2360.3, LINC00460, AL590094.1, LINC00944 and LINC0 1843) were identified to construct the novel TMB-related lncRNA signature, and their detailed information is shown in Table 3. The risk score for the novel TMB-related lncRNA signature was developed as follows: Risk score = (0.0828*AP002360.3)+(0.0964*LINC00460)+(-0.1835*LINC 01843)+(0.2768*AL590094.1)+(0.2058*LINC00944).

Table 2.

Univariate Cox regression analyses of eight TMB-related lncRNAs.

Gene Name	HR	HR. 95L	HR. 95H	p Value
AP002360.3	1.188	1.079	1.308	<0.001*
LINC00460	1.151	1.105	1.198	<0.001*
LINC01843	0.798	0.701	0.908	<0.001*
AP000439.2	0.964	0.943	0.985	<0.001*
LINC01550	0.524	0.366	0.749	<0.001*
AL590094.1	1.623	1.341	1.965	<0.001*
AC156455.1	1.251	1.144	1.368	<0.001*
LINC00944	1.463	1.236	1.731	<0.001*

Note: TMB, tumor mutation burden; HR, hazard ratio; lncRNAs, long non-coding RNAs; ccRCC, clear cell renal cell carcinoma.

Table 3.

Multivariate regression analysis of five lncRNAs.

Gene Name	Coef	HR	HR. 95L	HR. 95H	p Value
AP002360.3	0.083	1.086	0.984	1.199	0.101
LINC00460	0.096	1.101	1.051	1.154	<0.001*
LINC01843	-0.184	0.832	0.725	0.955	0.009*
AL590094.1	0.277	1.319	1.046	1.662	0.019*
LINC00944	0.206	1.228	1.008	1.497	0.041*

Note: HR, hazard ratio; Coef, coefficient; lncRNAs, long non-coding RNAs; ccRCC, Clear cell renal cell carcionma.

3.3. Evaluation of the Prognostic Value of TMB-Related lncRNA Signature

The risk score of each ccRCC patient was calculated based on the novel TMB-related lncRNA signature. All patients in each cohort were classified into high-risk group and low-risk group according to the median risk score. Kaplan-Meier analysis showed that the overall survival for patients in the high-risk group was worse than that of patients in the low-risk group (P<0.001, Fig. 3A); a parallel result was discovered in the testing cohort (P<0.001, Fig. 3C) and the total TCGA cohort (P<0.001, Fig. 3E). The time-dependent ROC curve analysis of three cohorts showed the area under the curve (AUC) of the signature to be 0.716, 0.775 and 0.744, respectively (Figs. 3B, 3D, 3F). The risk score, survival time and expression heatmap of the training cohort, testing cohort, and TCGA cohort are presented in (Fig. 4). Kaplan-Meier analysis was performed to analyze the relationship between the expression of each lncRNA and survival outcome. As shown in (Fig. 5), the expression levels of 5 lncRNAs were significantly related to the OS of ccRCC patient.

Fig. (3).

Kaplan-Meier curve analysis between the high and low risk groups, and corresponding area under ROC curve in training cohort (A, B), testing cohort (C, D), and TCGA cohort (G, H).

Fig. (4).

The distribution of risk score, expression heatmap and survival time of training cohort (A), testing cohort (B), and TCGA cohort (C).

Fig. (5).

Kaplan-Meier curve analysis.

3.4. The Novel TMB-Related lncRNA Signature Acts as an Independent Prognostic Factor from Other Clinicopathological Characteristics

Multivariate analyses indicated the novel TMB-related lncRNA signature, age and AJCC stage to all be significantly associated with overall survival in each cohort (Table 4). Therefore, we further analyzed whether the novel TMB-related lncRNA signature can predict survival prognosis independently of age and AJCC stage. Stratified by age, patients in the total TCGA cohort were divided into the younger group (n=340) and the older group (n=173) on the basis of the median age (age=65). There was a significant difference found in overall survival between the high-risk and low-risk groups in the younger group (P<0.001; Fig. 6A) as well as in the older group (P=0.007; Fig. 6B). Similar results were observed in the AJCC-stage stratification analysis (P=0.01, P=0.005; Figs. 6C and 6D). These results revealed the novel TMB-related lncRNA signature to be an independent prognostic factor associated with overall survival in ccRCC patients.

Table 4.

Univariate and Multivariate Cox regression analysis of the signature and overall survival in different patient cohorts.

Variables	-	Univariable Model			Multivariable Model
-	-	HR	95%CI	p-value	HR	95%CI	p-value
Train set (n=257)	-	-	-	-	-	-	-
Age	-	1.033	1.014-1.052	0.001	1.033	1.013-1.054	0.001*
Gender	Male/Female	0.934	0.603-1.447	0.76	-	-	-
Grade	(G1-2)/(G3-4)	2.802	2.075-3.784	<0.001	1.498	1.067-2.12	0.02*
Stage	(III+IV)/(I+II)	2.408	1.963-2.956	<0.001	2.041	1.619-2.574	<0.001*
Risk Score	High/Low	1.088	1.054-1.121	<0.001	1.055	1.015-1.096	0.007*
Test set (n=256)	-	-	-	-	-	-	-
Age	-	1.023	1.005-1.043	0.017	1.026	1.005-1.048	0.016*
Gender	Male/Female	0.994	0.628-1.571	0.979
Grade	(G1-2)/(G3-4)	1.929	1.446-2.575	<0.001	1.286	0.920-1.799	0.142
Stage	(III+IV)/(I+II)	1.607	1.336-1.932	<0.001	1.409	1.144-1.735	0.001*
Risk Score	High/Low	1.344	1.199-1.507	<0.001	1.218	1.060-1.340	0.006*
TCGA set (n=513)	-	-	-	-	-	-	-
Age	-	1.029	1.015-1.042	<0.001	1.029	1.014-1.044	<0.001*
Gender	Male/Female	0.964	0.703-1.323	0.82		--	-
Grade	(G1-2)/(G3-4)	2.268	1.845-2.787	<0.001	1.479	1.176-1.859	0.001*
Stage	(III+IV)/(I+II)	1.896	1.658-2.168	<0.001	1.674	1.438-1.949	<0.001*
Risk Score	High/Low	1.026	1.015-1.038	<0.001	1.019	1.009-1.030	<0.001*

Note: HR, hazard ratio

Fig. (6).

Stratification analyses. Kaplan–Meier analysis of overall survival for the younger group (A) and older group (B). Kaplan-Meier curve analysis of overall survival for stage I-II group (C) and stage III-IV group (D).

3.5. Correlation of TMB-Related lncRNA Signature with TMB Burden, MMR Genes and Immune Checkpoints Expression and Immunophenoscore Analysis

We found that the expression levels of key MMR genes MLH1, MSH2 and PSM2 were significantly lower in the high-risk group, and the expression level of MSH6 was marginally significantly lower in the high-risk group (Fig. 7A). Compared tothe low-risk group, the high-risk group exhibited a higher amount of TMB burden (Fig. 7B). By reviewing the previous literature, we identified 37 immune checkpoint genes and compared their expression differences in the high-risk and low-risk groups. As shown in (Fig. 7C), 31 immune checkpoint genes were observed to be significantly upregulated in the high-risk group, including PDCD1, CTLA4, and BTLA. Moreover, previous studies have shown that IPS could reflect the response of ICIs therapy. We analyzed the scores for IPS-CTLA4, IPS-PD-1 and IPS-CTLA4-PD-1. The results revealed that the high-risk group was equipped with higher IPS-CTLA4 score, IPS-PD-1 score and IPS-CTLA4-PD-1 score than the low-risk group (Fig. 7D). A ccRCC patient cohort with immunotherapy information and transcriptome data was used to verify the predictive ability of the novel TMB-related lncRNA signature. The results showed that the AUC of the novel TMB-related lncRNA signature in predicting immunotherapy response was 0.732, which was higher than that of PD-L1 (AUC=0.625) (Fig. 7E).

Fig. (7).

Correlation of TMB-related lncRNA signature with TMB burden, MMR genes and immune checkpoints expression and immunophenoscore analysis. (A) Four main MMR genes (MLH1, PSM2, MSH2 and MSH6) analysis. (B) TMB analysis. (C) IPS analysis. (D) 37 immune checkpoints related genes analysis. (E) ROC curve of the novel TMB-related lncRNA signature in predicting immunotherapy response.

3.6. Gene Cohort Enrichment Analyses and Immune Cell Infiltration Evaluation

To explore the underlying biological processes of our novel TMB-related lncRNA signature, we performed gene cohort enrichment analyses (GSEA), and found the two immune gene cohorts (immune system process, immune response) to be significantly enriched in the high-risk group of ccRCC patients. This result indicated the high-risk group to have a stronger immune response (Fig. 8A). We also evaluated the differences in the tumor microenvironment in the high-risk and low-risk groups. As shown in (Fig. 8B), a higher microenvironment score (P =0.00031) and immune score (P =6.9e-08) and lower stromal score were observed in the high-risk group compared to the low-risk group (P =0.0056). Furthermore, we used the CIBERSORT algorithm to analyze the abundance of 22 tumor-infiltrating immune cells in the two groups; the statistically significant results are shown in (Fig. 8C). In the high-risk group, the infiltration levels of CD8 T cell (P =0.0013), Macrophage M0 (P=2e-05), and T follicular helper cell (P=8.1e-10) were found to be raised, and the infiltration levels of naïve B cell (P=0.00083), Macrophage M2 (P=0.0014), activated Mast cell (P=3.9e-05), Monocyte (P=1.3e-05) and resting NK cell (P=1.1e-05) were found to be declined than those in the low-risk group.

Fig. (8).

Gene cohort enrichment analyses and immune cell infiltration evaluation. (A) Gene cohort enrichment analysis. (B) The immune score, stromal score and microenvironment score in the high-risk and low-risk groups. (C) Differentially expressed immune cells between risk groups.

3.7. Verification of Relative Expression Levels of Five Risk lncRNAs in HK-2, 786-O, 769-P and ACHN Cells

We conducted qRT-PCR to verify the relative expression levels of five risk lncRNAs (AP002360.3, LINC00460, AL590094.1, LINC00944, LINC01843) in HK-2, 786-O, 769-P and ACHN cell lines (Figs. 9A-9E). The expression levels of AP002360.3, LINC00460, AL590094.1, and LINC00944 were found to be significantly up-regulated in ccRCC cells (786-O, 769-P and ACHN cells) than those in human proximal tubule epithelial cells (HK-2 cell). While the expression levels of LINC01843 were found to be significantly down-regulated in ccRCC cells (786-O, 769-P and ACHN cells) than those in human proximal tubule epithelial cells (HK-2 cell).

Fig. (9).

The expression levels between ccRCC cells and human proximal tubule epithelial cells of AP002360.3 (A), LINC00460 (B), AL590094.1 (C), LINC00944 (D) and LINC01843 (E). Kaplan–Meier analysis between high and low risk score groups, and corresponding area under ROC curve in the independent cohort with 116 ccRCC patients at our institution (F, G). *: P<0.05; **: P<0.01; ***P<0.001; ****P<0.0001.

3.8. Validation of this Novel TMB-related lncRNA Signature in the Independent Cohort of 116 ccRCC Patients at our Institution

During the follow-up, 19 deaths occurred from ccRCC. The expression levels of these risk lncRNAs in 116 ccRCC patients were obtained via qRT-PCR. Risk score for 116 ccRCC patients was calculated using the above formula: Risk score = (0.0828*AP002360.3) + (0.0964*LINC00460)+ (-0.1835*LINC01843)+(0.2768*AL590094.1) + (0.2058*LI NC00944). Kaplan-Meier survival analysis revealed that patients with low-risk score showed a significant survival profit (Fig. 9F), and the area under the time-dependent ROC curve was 0.777 in the independent cohort (Fig. 9G).

4. DISCUSSION

CcRCC is the most malignant subtype of RCC and is highly associated with poor prognosis and distant metastasis [19]. Exploration of potential biomarkers is critical for the treatment and prognosis of ccRCC. Previous studies have demonstrated an important role of lncRNA in ccRCC [20]. The induction of lncRNA could be a novel therapeutic strategy for advanced ccRCC. However, fewer studies have addressed the prognostic value of TMB-related lncRNAs in clear cell carcinoma. In the current study, we screened 160 differentially expressed lncRNAs for ccRCC and identified two molecular clusters to be totally related to TMB for the first time. More interestingly, these molecular clusters displayed distinct immune checkpoints expression and immune cells infiltration. Furthermore, we succeeded in establishing a novel lncRNA signature associated with TMB for predicting the OS of ccRCC utilizing five lncRNAs. Then, we utilized independent prognostic analysis to show this TMB-related lncRNA to be a well-independent risk factor in predicting the OS of ccRCC. More importantly, we validated the performance in predicting OS for ccRCC in our institutional cohort with 116 ccRCC patients. Therefore, this novel TMB-related lncRNA signature could perform well in predicting the OS of ccRCC.

The novel TMB-related lncRNA signature was composed of five lncRNAs, including AP002360.3, LINC00460, AL590094.1, LINC00944 and LINC01843. Among these 5 lncRNAs, LINC00944, LINC00460 and LINC01843 have been reported to participate in the development of various cancers. Chen et al. demonstrated that lncRNA LINC00944 could promote tumorigenesis and suppress Akt phosphorylation in RCC [21]. The upregulated expression level of LINC00460 is correlated with larger tumor size, tumor node metastasis stage, lymph node metastasis, and shorter overall survival in various tumors [22-25], including colorectal cancer, head and neck squamous cell carcinoma, and hepatocellular carcinoma. Zhang et al. reported lncRNA LINC00460 to be a promising prognosis factor and associated with the progression of ccRCC [26]. Li et al. proved LINC01843 to be not only an immune-related lncRNA but also a risk indicator whose high expression predicts poor survival outcomes in lung adenocarcinoma. In our study, we found expression levels of AP002360.3, LINC00460, AL590094.1, and LINC00944 to be significantly higher in ccRCC cells than in normal human proximal tubule epithelial cells, while LINC01843 was higher in normal human proximal tubule epithelial cell than in ccRCC cell, which was consistent with bioinformatics analysis results. These results demonstrated these five risk lncRNAs to have the potential to be prognostic indicators of ccRCC.

CcRCC is considered an immunogenic tumor, and immunotherapy has been reported to play a key role in the treatment of mRCC [27]. Immunotherapy plays an antitumor role by comprehensively affecting the tumor immune microenvironment, immune cells and immune checkpoints, so identifying the immune characteristics of distinct ccRCC populations will help urologists determine patients who are potential beneficiaries of immunotherapy. Recently, the crucial function of lncRNA in regulating cancer immune response has gradually emerged. In addition, there is growing evidence that lncRNA is a potential biomarker for predicting immunotherapy response in various tumors. Yang et al. found lncRNA expression patterns in stomach adenocarcinoma to be associated with tumor mutation burden, tumor-infiltrating lymphocytes and microsatellite instability [28]. Zhang et al. found immune-related lncRNA signature to be significantly negatively related to tumor microenvironment (TME), CD8+T cells and cytotoxic lymphocytes infiltration [29]. In the study, we explored the relationship between the TMB-related lncRNA signature and tumor immune microenvironment features. In the high-risk group, TMB abundance, immune score, IPS score, and the expression level of most immune checkpoints were higher, while key MMR genes expression was lower than that in the low-risk group, which suggests ccRCC patients in the high-risk group to be potential beneficiaries of immunotherapy.

Several limitations of the study need to be mentioned. First, retrospective data limit the reliability of our analyses and require further validation in prospective multi-center cohorts. Second, qRT-PCR analyses are not sufficient to reveal the molecular mechanism, and further experiments are necessary for deeper research.

CONCLUSION

This study identified two TMB-related lncRNA clusters with distinct immune features for ccRCC. Moreover, a novel TMB-related lncRNA signature was successfully developed and validated for individualized prognosis assessment of ccRCC patients.

LIST OF ABBREVIATIONS

ccRCC

Clear Cell Renal Cell Carcinoma

ROC

Receiver Operating Characteristic

RCC

Renal Cell Carcinoma

TKI

Tyrosine Kinase Inhibitors

GSEA

Gene Cohort Enrichment Analyses

TCGA

The Cancer Genome Atlas

TCIA

The Cancer Immunome Atlas

TME

Tumor Microenvironment

LncRNA

Long Noncoding RNA

OS

Overall Survival

IPS

Immunophenoscore

AUTHORS' CONTRIBUTION

Lin Lin, Xiao-Hui Wu, and Jun-Ming Zhu wrote the original draft; Qing-Shui Zheng and Xiong-Lin Sun performed writing - review and editing; Yong Wei, Xiong-Lin Sun, Ning Xu, and Qing-Shui Zheng designed the methodology; Fei Lin performed formal analysis; Xiao-Hui Wu and Lin Lin contributed to data curation; Xue-Yi Xue and Yong Wei contributed to conceptualization; Jun-Ming Zhu and Shao-Hao Chen contributed to visualization; while Qing-Shui Zheng and Xiong-Lin Sun contributed to project administration.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The study was approved by the Human Research Ethics Committee of Fujian Medical University.

HUMAN AND ANIMAL RIGHTS

No animals were used for studies that are the basis of this research. All human procedures followed were in accordance with the Helsinki Declaration of 1975.

CONSENT FOR PUBLICATION

Informed consent was obtained from patients and volunteers.

STANDARD FOR REPORTING

STROBE guidelines and methodology were followed.

FUNDING

This work was sponsored by Natural Science Foundation of Fujian Province (No. 2022J01122622) and 2022 Hospital Nursing Special Innovation Research Project, China (No. 2002FY-HZ-27).

AVAILABILITY OF DATA AND MATERIALS

The datasets used and/or analyzed during the current study are available from the corresponding author [QZ, XS] upon reasonable request.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.