Significance of Tumor Mutation Burden related immune gene PAEP in the progression and prognosis of clear cell renal cell carcinoma

doi:10.21203/rs.3.rs-4650268/v1

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Research Article

Significance of Tumor Mutation Burden related immune gene PAEP in the progression and prognosis of clear cell renal cell carcinoma

https://doi.org/10.21203/rs.3.rs-4650268/v1

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Background

Clear cell renal cell carcinoma (ccRCC) is a common renal malignant disease with a poor prognosis. Tumor mutation load (TMB) has received much attention in various tumor studies, however, there were limited studies focus on the relationship between TMB and ccRCC. We aimed to investigate the role of TMB-related immune gene progestagen‑associated endometrial protein (PEAP) in ccRCC and the underlying molecular mechanisms.

Methods

Somatic mutation data of 336 patients with ccRCC were downloaded from the Cancer Genome Atlas (TCGA) database, and the mutational spectrum was analyzed using the "maftools" software package. Based on TCGA -ccRCC cohort, we summarized the status of gene mutations in ccRCC. The TMB was calculated and the samples were divided into high and low TMB groups. Then, we analyzed the relationship between TMB and clinical characteristic. Meanwhile, we identified some TMB-related immune genes through the intersection of TMB-Related differentially expressed genes (DEGs) and immune related genes. Finally, We selected the immune genes most associated with TMB, investigated its expression in renal tissues of ccRCC patients, and further investigated its role and potential molecular mechanisms In-vivo and in-vitro.

Results

Using bioinformatics we analyzed the most common mutation of Variant Classification, Variant Type, single nucleotide variants (SNV) Class for missense mutations, single nucleotide polymorphism (SNP) and C > T in ccRCC, respectively. we found that higher TMB related to shorter overall survival (OS), lower age and grade. Finally, we identified progesterone associated endometrial protein (PAEP) gene, a novel TMB-related immune gene in ccRCC, which was significantly overexpression in ccRCC tissues and cells with progression and poor survival in ccRCC patients. Furthermore, by constructing 786-O cell model, our results showed that PAEP promoted the invasion, migration, and proliferation of ccRCC cells; meanwhile, PAEP knockdown suppressed the PI3K/Akt/NF-κB signaling pathway. In-vivo studies, we found that after knocking out the PEAP gene, the subcutaneous transplanted tumors in nude mice were smaller and lighter. Mechanistically, we consider that PAEP may regulate the malignant biological phenotype and poor survival prognosis of ccRCC through the PI3K/Akt/NF-κB signaling pathway.

Conclusion

Our study suggests that PAEP might represents a potential target of antibody immunotherapy for ccRCC patients and also provides a strong theoretical basis for the clinical application of PAEP.

Progesterone associated endometrial protein

Tumor mutation burden

Immunotherapy

Clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is the most common genitourinary malignancy. As the most common type of RCC, Clear cell renal cell carcinoma (ccRCC) accounts for 75 ~ 80% of all RCC cases ^1,2. Recently, surgery and targeted therapy are the most common and effective clinical treatments for ccRCC patients ³. However, its overall mortality rates are still slightly increased, partly due to early-stage metastasis of the disease ⁴. Therefore, some novel markers for early diagnostic and therapeutic targets involved in ccRCC progression are urgently required with significant clinical value.

In recent years, antibody immunotherapy represents a promising therapy for the clinical treatment of ccRCC patents, successfully developed and widely applied ⁵. For example, Immune checkpoint inhibitors (ICI) such as PD-1/PD-L1 inhibitors develop as a potential clinical strategy for ccRCC ^6,7. However, ccRCC patients sometimes show low objective response rates against ICI ⁸. Tumor mutation burden (TMB) is defined as a total number of somatic coding mutations in the exon coding region of the genome of a tumor cell. TMB is a potential biomarker for predicting ICI response in varying tumors ^9,10. TMB was first identified as a potential biomarker for treatment with ICI in melanoma in 2014 ¹¹. Since then, TMB has been considered to be related to the therapeutic effect of immune checkpoint inhibitors and has become a key predictor of the efficacy of immunotherapy for some malignant tumors ¹². In addition, studies have shown that high TMB levels are associated with better prognosis in patients with many malignant tumors, such as non-small cell lung cancer and endometrial cancer ¹³. A study in advanced cancer patients not treated with ICI reported no correlation between high TMB and better prognosis ¹⁴. However, many studies have shown that high levels of TMB are associated with poor prognosis in patients with thyroid cancer ¹⁵, head and neck squamous cell carcinoma ¹⁶ and intrahepatic cholangiocarcinoma ¹⁶. At present, whether the level of TMB is related to the prognosis of patients with malignant tumors is still controversial. The connection between TMB and immune infiltration was deviated from varied tumors ^17,18. Nevertheless, limited studies on TMB-related immune genes in ccRCC, so we tried to research the potential relationship between TMB and ccRCC.

In this study, based on the TCGA-ccRCC cohort, we summarized the current status of gene mutations in ccRCC. The relationship between TMB and clinical features was then analyzed. Meanwhile, we identified some TMB-related immune genes by cross-validation of TMB-related differentially expressed genes (DEGs) and immune-related genes, and found that gene Progestagen‑associated endometrial protein (PAEP) was closely related to the prognosis of ccRCC. Finally, through clinical data and in vitro experiments, we further confirmed that gene PAEP promoted the malignant progression of ccRCC, which may be related to the PI3K/Akt/NF-κB signaling pathway.

Data source and mutation analysis

The data of ccRCC patients, including “mask somatic mutation”, transcriptome profiles and clinical data, was obtained from the the Cancer Genome Atlas (TCGA ) database on November 08,2020. At the same time, we used the Mutect algorithm to processed “mask somatic mutation” data and visualized the results using the “maftoools” R package. Then, we categorized the ccRCC patients into high-TMB group and low-TMB group according to the median value of TMB. To analyze the correlation between TMB status and several clinicopathological characteristics, we analyzed the significance. We also analyzed the difference of overall survival (OS) between the high-TMB group and low-TMB group using Kaplan-Meier statistics. To evaluate the diagnostic value of TMB-related immune genes in ccRCC, we performed Receiver Operating Characteristic (ROC) curves and calculated the area under the ROC curve (AUC) to assess the diagnostic efficiency.

TMB-Related differentially expressed genes and functional enrichment analysis

To understand the TMB-Related function, we used the R package “limma v3.38.3” to perform TMB-Related differentially expressed genes (DEGs) between the above two TMB subgroup with p < 0.05 and |logFC| > 1.0. Meanwhile, we visualized the DEGs with volcanic plot and heatmap. Nevertheless, Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) were identified using R package “clusterProfiler, org.Hs.eg.db, enrichplot” to identify the functional enrichment and pathway enrichment of all DEGs ¹⁹. All above results were visualized by R package “ggplot2, heatmap, ggpubr, ggthemes”.

Identification and Coexpression analysis of TMB-related immune genes

On the other hand, we obtained a list of immune related genes (IRG) from the Immport data (https://www.immport.org), then we defined the common genes as TMB-related immune genes between immune related genes and DEGs using R package “ggplot2”. Finally, we performed the expression, correlation, and risk score distribution, survival status of the TMB-related immune genes among patients based on the expression of these genes using R package “corrplot, ggplot2, heapmap”. Finally, we selected the highest correction and novel gene for the future analysis, including expression analysis, Gene Set Enrichment Analysis (GSEA), OS, ROC and clinical correlation with GSEA project (4.0.3) and R package “pROC, ggplot2, CBCgrps” ^20,21.

Cell culture and and treatment

The human ccRCC cell line,769-P, 786-0, A498 and ACHN, was purchased from ASY Biotechnology Ltd., Corp (Wuhan, China). All cells were cultured in 89% RoswellParkMemorialInstitute (RPMI) 1640 medium (Life Technologies, Gibco, USA, 11875119) supplemented with 10% fetal bovine serum(FBS)(Life Technologies, Gibco, USA,) A5670701 and 1% penicillin–streptomycin (Life Technologies, Gibco, USA, 15140122) unless stated otherwise. The cells were incubated at 37°C in a humidified incubator with 5% CO₂. They were thereafter subjected to growing until 85%– 95% confluency in the culture flask, trypsinized, and harvested for subsequent experiments.

Short hairpin RNA construction and cell transfection

PAEP short hairpin ribonucleic acid (shRNA) expressing lentivirus was obtained from GeneChem Co., Ltd. (Shanghai, China) (https://www.genechem.com.cn). Transfection conditions were reference to our previous paper. Briefly, the short hairpin RNA (shRNA) target sequence was shPAEP1, 5'-AAGATCAACTATACGGTGG-3', shPAEP2, 5'-AAGAGCCGTGCCGTTTCTA-3'. Conforming to the manufacturer's guidance, the 786-0 cells were transfected with shRNA non-sense control (shRNA-Con group) or with PAEP shRNA (shRNA-PAEP group) using lentiviral particles at a MOI (100:1) of 100 pfu/cell in the presence of polybrene (Yeasen, Shanghai, China, 40804ES86). In order to acquire PAEP knockdown cell lines, the transfected cells were treated with 5 mg/mL of puromycin (Yeasen, Shanghai, China, 60209ES10). Then, the resistant colonies were collected and cultivated for further analyses.

Patient selection and preparation of tissue

36 patients who were diagnosed as ccRCC at the Second Affiliated Hospital of Hainan Medical University were enrolled in our study. More detailed information of the patents is previous described. The ccRCC and their paired-normal tissues were snap-frozen immediately after removal and stored at − 80^◦C. During every stage of our experiments, we adhered to the guidelines outlined in the Code of Ethics of the World Medical Association. The Ethics Committee of Hainan Medical University conducted a review of our research and granted approval(2024-KCSN-13). After receiving sufficient information, all participants enrolled in this study provided their written consent.

Western blotting

Lysis of the cells was done with RIPA buffer (Beyotime Biotechnology, China, P0013B), protease inhibitor cocktail (Roche, Switzerland, P8215), and phenylmethylsulfonyl fluoride (Beyotime Biotechnology, China, ST507-10mL, China, P0011) in a ratio of volume 100:4:1 for 30 min on ice. A bicinchoninic acid protein assay kit (Beyotime Biotechnology, China, P0011) was utilized to accurately measure the protein concentration. In addition, 30µg protein was isolated on 15% or 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) gel (Biotechwell, China) and transferred onto the polyvinylidene difluoride (PVDF) membrane (# IPFL00010; Millipore, USA). After that, blocking of the PVDF membranes was done using 5% skimmed milk (# 232100; BD Biosciences, USA) for 3 h. The membranes were then incubated throughout the night at 4°C with primary antibodies: anti- PAEP (Abcam, UK, #ab270454, #1:1000), anti-p-PI3K (Abcam, UK, ab278545, #1:1000), anti-PI3K (Abcam, UK, ab302958, #1:1000), anti-AKT (Abcam, UK, ab8805, 1:500), anti-p-AKT (Abcam, UK, ab192623, 1:1000), anti-p-p65 (CST, USA, #3033, 1:1000), anti-p65 (CST, USA, #8242, 1:500), anti-p-IκBα (CST, USA, #2859, 1:1000), anti-IκBα (CST, USA, #9242, 1:1000), and anti-β-Actin (Abcam, UK, ab8226, 1:3000).Afterward, the fluorescent secondary antibodies were utilized for sample incubation in the darkness for 2 h. The Odyssey infrared imaging equipment (LI-COR Biosciences, USA) was utilized to scan and develop the membranes. The grayscale value of the experiment was analyzed with the aid of the ImageJ software.

qPCR

According to the manufacturer’s instructions, total RNA from the ccRCC and their paired-normal tissues was extracted using the Trizol reagent (Invitrogen, Carlsbad, CA, USA, 15596026CN). The concentration of the RNA was determined using ultraviolet spectrophotometry. The cDNA was synthesized using a PrimeScript RT Reagent Kit (Takara, Shiga, Japan, RR047A). Quantitative real-time PCR analysis for PAEP mRNA levels was performed using a TB Green® Premix Ex Taq™ II FAST qPCR (Takara, Shiga, Japan, CN830S) through an Applied Biosystems 7500 Real-Time PCR System(Thermo Fisher Scientific, Inc., USA). Relative mRNA expression levels were calculated using the relative Ct method, and the fold change compared with β-actin (Sangon Biotech, Shanghai, China) as the control. The primer sequences were as follows: PAEP: Forward:5-CCTGTTTCTCTGCCTACAGGA-3, Reverse:5-CCTGTTTCTCTGCCTACAGGA-3;

β-Actin: Forward:5-GTCCACCGCAAATGCTTCTA-3, Reverse:5-TGCTGTCACCTTCACCGTTC-3.

Cell Counting Kit‑8 (CCK‑8) assay

The density of 786-O cells was adjusted to 1 × 10⁴ cells /mL, 100µL was added to each well in the 96-well culture plate, and cultured in an incubator with 5% CO₂ and 37℃. The cell Optical Density was measured at 450 nm using the Spark™10 M microplate reader (Tecan Group, Ltd.) at 0h, 24h, 48h, and 72h according to the CCK-8 kit (Beyotime Biotechnology, China, C0038) operating manual, and cell growth curves were plotted.

Flow cytometry

786-O cells were inoculated on a 6-well plate 48 hours later, cell suspension was collected, 1000g, centrifuged at 4℃ for 5min, cells were collected and counted with precooled PBS, 5–10×10⁵ cells were taken, centrifuged at 1000g for 5min, and supernant was discarded. According to the Annexin V-FITC Apoptosis Detection Kit manufacturer's instructions (Beyotime Biotechnology, China, C1062S), Annexin V-FITC binding solution 195µL Annexin V-FITC was added, the cells were lightly suspended, 5µL Annexin V-FITC was added, the cells were lightly mixed, 10µL propyl iodide staining solution was added, the cells were gently mixed, and incubation was performed at room temperature for 10–20 min, the cells were re-suspended for 2–3 times to improve the staining effect, and then placed in an ice bath away from light. Finally on the machine detection.

Cell proliferation, migration and invasion assays

The colony formation test was conducted to examine the impact of PAEP expression knockdown on the growth potential of 786-O cells. In addition, 700 cells/well (786-O cells transfected with the sh-Con and sh-PAEP) were seeded in 6-well plates, correspondingly, and were treated after 14 days. Then, the cells were fixed in 75% alcohol for 30 min and stained with 0.5% crystal violet (Yeasen, Shanghai, China, 60506ES60) for 30 min. The cell colonies were calculated using Image J software. Three replicates of each experiment were carried out, and the mean ± standard deviation (SD) was calculated.

24-well Transwell plates (cat. no. 3422, Corning, USA) with or without Matrigel matrix (cat. no. 356234, BD Biosciences, USA) were used for migration assay and invasion assay. The upper chambers and the lower chambers were seeded 1 × 10⁵ cells in 200 µl without FBS RPMI 1640 medium and added 600 µl of RPMI 1640 containing 20% FBS, respectively. Subsequently, cells that migrated or invaded through the bottom of chambers were fixed with 4% paraformaldehyde, and then stained with 0.5% crystal violet for 30 min. Finally, the image was captured under a microscope. To measure the invasive and migratory capability of cells, we enumerated cells invading the Matrigel using ImageJ.

Tumor formation in nude mice

BALB/c female nude mice, 5 to 6 weeks old, purchased from Hunan SJA Laboratory Animal Co., Ltd. (production license No. SCXK [Hunan] 2019-0014), kept at 22˚C, Humidity 50%, light/dark cycle 12 h environment, free to eat and drink. All animal experiments were approved by the Ethics Committee of XX University.

The nude mice were randomly divided into 3 groups, Ctrl group, shCon group and shPEAP group, with 6 mice in each group. Ctrl group: Logarithmic growth of 786-O cells was implanted in the back of nude mice. shCon group: 786-O cells were transfected with negative control sh-NC, and logarithmic growth cells were implanted in the back of nude mice. shPEAP group: 786-O cells were transfected with lentivirus shPEAP, and logarithmic growth cells were implanted in the back of nude mice. The tumor volume was measured every 2–3 days. 15 days after inoculation, the nude mice were euthanized for cervical dislocation, and the tumors were photographed, measured, weighed, and WB detected.

Statistical analysis

We selected the TMB-Related DEGs with p < 0.05 and |logFC| > 1.0 to further assess. The KM analysis and log-rank test were used to assess OS in the ccRCC cohort. The Chi-square test was used to assess correlation between PAEP expression and clinicopathologic characteristics of ccRCC patients. All experiments were performed with at least three independent biological replicates. The data are expressed as means ± standard deviation (SDs). We employed R (version 4.3.1), SPSS (version 25.0; IBM, USA), and GraphPad Prism (version 7; GraphPad Software, USA) for all statistical analyses. Two-tailed t -tests and one-way analysis of variance (ANOVA) were used to analyze cell culture experiments. The criterion for statistical significance was set at P < 0.05.

Mutation analysis

Somatic mutation profiles of 537 ccRCC patients downloaded from TCGA database were analyzed and visualized using R package “maftools” ²². A waterfall plot was performed to exhibit the detailed mutation information in each sample. The majority of Variant Classification, Variant Type, single nucleotide variants (SNV) Class were missense mutations, single nucleotide polymorphism (SNP), C > T, respectively (Fig. 1A i-iii). Counting each sample separately, the median and maximum of mutations in the TCGA-ccRCC cohort were 47 and 1611, respectively (Fig. 1A iv and Fig. 1B). In addition, we exhibited the number of each variant classification in the different sample using box plots (Figure.1A v). The top 10 mutated genes in the 336 ccRCC patients were VHL (49%), PBRM1 (42%), TTN (18%), SETD2 (12%), BAP1 (10%), MUC16 (7%), MTOR (7%), KDM5C (6%), HMCN1 (5%), DNAH9 (5%) (Figs. 1A vi, 1B and 1D), while the PBRM1 and VHL have the highest correlation (Fig. 1C).

Correlation of TMB with prognosis and clinical features

After the clinical data of ccRCC patients were collected from TCGA, we selected 336 patients with complete information. Based on the median TMB value (1.29), we divided 336 patients into high-and low-TMB groups (n = 163 VS n = 173). As showed in the Kaplan-Meier curve, the high-TMB group had significantly poor OS outcomes (P = 0.007) (Fig. 2H). Besides, the correlation of TMB with clinical features showed that only age and grade were significantly associated with TMB (p < 0.05) (Figs. 2A and 2C), the other clinical characteristics showed no significantly difference (Figs. 2B and 2D-2G).

TMB-Related differentially expressed genes and Functional enrichment analysis

According to the DEGs expression analysis between high-TMB group and low-TMB group, a total of 120 DEGs with |log₂FC|> 1 and FDR < 0.05 was identified. There are 44 up-regulated genes and 76 down-regulated genes in the high-TMB group, compared to the low-TMB group. The heatmap and volcano plot showed the top 40 DEGs ranked in the order of FDR and all DEGs, respectively (Figs. 3A and 3B). According to GO and KEGG analysis, DEGs were enriched in the function enrichment of apical part of cell, apical plasma membrane, axon terminus and the pathway of Calcium signaling pathway, Vibrio cholerae infection and Taurine and hypotaurine metabolism (Figs. 3C and 3D).

Comparison of gene expression profiles between two tmb groups

We further identified the TMB-related immune genes through the intersection of 1793 immune related genes and 120 DEGs for subsequent analysis (Fig. 4A). Then, we selected 8 TMB-related immune genes including CRP, IGHA2, IGLC3, IL6, LBP, LCN1, PAEP, SLIT2. We extracted the mRNA expression of each TMB-related immune genes to draw the box plot, heatmap and correlation heatmap (Figs. 4B-4D). The results revealed that IGHA2, IGLC3, PAEP were increased, while CRP, LCN1 and SLIT2 decreased, IL-6 and LBP resting in ccRCC tissues, compared to the normal tissues (Figs. 4B and 4C). Importantly, PAEP and LCN1 had the highest correlation score (0.96) (Fig. 4D).in addition, the ccRCC patents with higher risk scores, composing by these genes, had poor outcomes (Fig. 4E). Finally, we selected the PAEP which is a never reported gene in ccRCC for the further research.

PAEP was overexpression and associated with shorter survival in ccRCC patents

Considering PAEP overexpression in ccRCC, we further investigated whether it contributed to ccRCC progression. Thus, we chose the 786-0 cells which had the highest levels of PAEP for further investigation (Figs. 5A-5B). 786-0 cells were transfected with short hairpin RNAs (shRNAs) targeting to PAEP, which significantly silenced the PAEP expression (Figs. 5C-5D). PAEP was significantly overexpressed in the TCGA-ccRCC cohort, compared to the normal group (Fig. 5E), which was further validated in ccRCC tissues and ccRCC cells by RT-qPCR and Western blot analysis (Figs. 5F-5H). Moreover, analysis of clinical characteristics indicated that PAEP overexpression was significantly correlated with Primary tumor size, TNM stage and Fuhrman grade (Table 1). Importantly, Kaplan-Meier analysis revealed that ccRCC patients with high PAEP expression had poor OS and disease-free survival (DFS )in the TCGA cohorts and further confirmed in our cohort (without DFS result) (Figs. 5I-5K), indicating that PAEP upregulation was potentially related to the ccRCC progression. To evaluate the diagnostic value of PAEP in ccRCC, we constructed ROC curve and calculated the AUC (Fig. 5L). The ROC curve of PAEP indicated it was a medium diagnostic tool with an AUC is 0.764. We performed GSEA to reveal potential molecular mechanism of PAEP regulating the progression of ccRCC. The results showed that all the significant pathways were enrichment in the PAEP overexpression group related to various tumorigenesis-related characteristics, containing MAPK signaling pathway, Renal Cell Carcinoma, TGF-β signaling pathway, ubiquitin mediated proteolysis and Wnt signaling pathway, etc (Fig. 5M). These results provide new clues for exploring the molecular mechanism of ccRCC in the future. In conclusion, PAEP serves as an important oncogene and is associated with a poor clinical outcome of ccRCC.

Table 1

Correlation between PAEP expression and clinicopathologic characteristics of ccRCC patients^a
Characteristics	N	PAEP expression level		p -value
Characteristics	N	Low	High	p -value
Age at surgery				0.289
< 65 years	24	10	14
≥ 65 years	12	8	4
Sex				0.146
Men	25	10	15
Women	11	8	3
Primary tumor size				0.007
< 7 cm	26	17	9
≥ 7 cm	10	1	9
pT stage				0.691
pT1-2	28	15	13
pT3-4	8	3	5
TNM stage				0.027
I + II	25	16	9
III + IV	11	2	9
Fuhrman grade				0.041
1 + 2	28	17	11
3 + 4	8	1	7
Abbreviations: N for number of cases, T for tumor stage, TNM for tumor node and distant metastasis. ^a for Chi-square test, * p < 0.05, ** p < 0.01.

PAEP knockdown inhibited ccRCC cells growth

First of all, CCK-8 results showed that compared with the Blank group, the cell viability of sh-Con group had no significant change, while the cell viability of sh-PEAP group was significantly reduced (Figs. 6A). The results of flow cytometry showed that compared with the Blank group, the apoptosis of sh-Con group was not obvious, and the apoptosis of sh-PEAP group was significantly increased (p < 0.05; Figs. 6B and 6C). Then, we performed the transwell assays and colony formation assays to evaluate invasion, migration and proliferation in response to PAEP knockdown in 786-0 cells. Transwell assays showed that PAEP knockdown remarkably reduced cell invasion and migration in 786-0 cells, compared to Blank groups and shCon groups (p < 0.001; Figs. 6D − 6F), Meanwhile, colony formation assays revealed that PAEP knockdown significantly reduced cell colonies in 786-0 cells compared with Blank groups and shCon groups (p < 0.05; Figs. 6G and 6H).

These results showed that PAEP knockdown inhibited ccRCC cells growth. Taken together, our results suggested that PAEP promoted the progression of ccRCC.

PAEP induces activation of the PI3K/Akt/NF-κB pathway

Previous studies showed that PAEP contributed to the activation of the PI3K/Akt/NF-κB signaling pathway ²³. It could be inferred that PAEP played an important role in the activation of the PI3K/Akt/NF-κB signaling pathway. Therefore, we performed western blotting assays to assess changed genes involved in the PI3K/Akt/NF-κB signaling pathway in 786-0 cells. The results shown that PAEP knockdown inhibited PI3K/Akt/NF-κB activation (p < 0.05; Figs. 6I-6K).

PAEP knockdown inhibited tumor formation in nude mice.

We further studied the effect of PEAP knockdown on subcutaneous tumor formation in nude mice in vivo, and the results showed that we first successfully constructed a subcutaneous tumor transplantation model in nude mice (Fig. 7C and 7D). Western blotting study results showed that compared with Ctrl group, there was no significant change in the expression of PEAP protein in shCon group, while the expression of PEAP protein in shPEAP group was significantly reduced (Fig. 7A and 7B). At the same time, we also found that compared with the Ctrl group, the weight and volume of transplanted tumors in the shCon group did not change much, while the weight and volume of transplanted tumors in the shPEAP group significantly decreased (Fig. 7E-7F).

In recent years, the incidence of RCC has continued to rise, affecting more than 400,000 people worldwide every year ²⁴, causing a huge burden on global public health. Of all the renal cell subtypes, ccRCC is the most common, accounting for about 70–80% ^1,2. Current clear cell renal cell carcinoma patients have high metastasis, incremental recurrence, and low response rate to immune checkpoint therapy ^1,8. As a new treatment strategy to predict immune responses, TMB had been exhibited their effect in a variety of tumor types ²⁵. However, the underlying mechanism of how TMB-related immune genes regulate the occurrence and metastasis of ccRCC remains unclear.

In our study, we explored the status of TMB in ccRCC. The landscape of mutation profiles in TCGA-ccRCC cohort showed that 88.1% of patients develop varied types of mutation. The majority of Variant Classification, Variant Type, SNV Class were missense mutations, SNP, C > T, respectively. For the correlations between TMB and clinicopathological characteristics, the higher TMB level correlated with higher age, higher AJCC-T, grade and stage. Meanwhile, ccRCC patents with higher TMB have shorter OS. These results were in accordance with previous study ²⁶.

Further, we identified some TMB-related immune genes through the intersection of DEGs and immune related genes, in which PAEP and LCN1 had the highest correlation. Finally, we selected the PAEP gene for the further research, which is a never reported gene in ccRCC. PAEP was overexpressed in approximately 80% of all tumors compared to normal tissue. PAEP was initially described as an immune system modulator in reproduction ²⁷. Knockdown of PAEP resulted in a deregulation of immune system modulators, such as PD-L1 ^28,29. In this study, we confirmed that PAEP was overexpressed in a cohort of 36 ccRCC patients and PAEP overexpression was closely correlated with Primary tumor size, TNM stage and Fuhrman grade. This expression pattern is similarly with the other cancers ³⁰. Further, PAEP is useful for determining prognosis of ccRCC and also can be severed as a potential diagnostic biomarker. PAEP knockdown significantly inhibited the proliferation, migration and invasion of ccRCC. Our research is consistent with the function of PAEP in other cancer ³¹. The abnormality of apoptosis plays a crucial role in the occurrence of tumor. For a long time, apoptosis has been considered as an important mechanism to prevent the occurrence of tumor, and one of the characteristics of tumor cells is to inhibit apoptosis³². In this study, we observed that PEAP knockdown promotes apoptosis of ccRCC cells, suggesting that PEAP may be an important target for the treatment of ccRCC. Mechanistically, PAEP knockdown decreased PI3K/Akt/NF-κB signaling pathway in ccRCC. Suraj Peri have found that the NF-κB signaling pathway is constitutively active in a high percentage of ccRCC cases ³³. Nonetheless, we found that knock down PAEP could efficiently depressed the activation of PI3K/Akt/NF-κB pathway, suggesting that PAEP maybe a potential target for this incurable malignancy. In addition, in vivo studies, we found that knocking down PEAP significantly reduced the expression of PEAP protein in transplanted tumor tissue, inhibited the formation of transplanted tumor, and reduced the size and weight of transplanted tumor tissue. These results suggest that low PEAP expression may inhibit the further development of ccRCC.

There are some shortcomings in this study. First of all, we only studied the gene mutation status of ccRCC based on the TCGA database and analyzed the relationship between clinical characteristics of TMB, and the results were biased to some extent. Secondly, we identified PAEP gene, which is a new TMB related ccRCC immune gene, and only made preliminary identification through ccRCC tissue. However, the amount of data is still too small and persuasive, and more clinical experiments are needed to further confirm it. Finally, due to the limitation of funds and time, we have not conducted rescue experiments to verify whether the regulatory effect of PEAP is played by the PI3K/Akt signaling pathway, and we plan to conduct in-depth research on this in the follow-up studies.

In summary, our study showed that overexpression of PAEP promotes ccRCC malignant progression may relate to the PI3K/Akt/NF-κB signaling pathway. Understanding the important role of PAEP in ccRCC would widen our knowledge of the biological basis of ccRCC progression and might represents a potential target of antibody immunotherapy for ccRCC patients.

TMB Tumor mutation burden

ccRCC Clear cell renal cell carcinoma

PAEP Progesterone associated endometrial protein

ICI Immune checkpoint inhibitors

Funding

This study was supported by Hainan Provincial Natural Science Foundation of China (ZDYF2024SHFZ122, 822NQ472, 820MS142).

Author contributions

Jie Yang, Zhifei Che, Shiying Zhou, Zechun Peng, Fangzhen Cai and Shuming He performed the bioinformatics analysis, drafted the manuscript and prepared the figures. Jie Yang and Zhifei Che collected the clinical data and experiments. Shuming He and Jie Yang design the study. Jie Yang, Zhifei Che and Shuming He collected the related references and participated in discussion. All authors contributed to this manuscript. All authors read and approved the final manuscript.

Availability of data and materials

All data generated or analyzed during this study are available from the corresponding author upon reasonable request. The datasets analyzed during the current study are available in the TCGA and Immport databases. [https://portal.gdc.cancer.gov/,https://www.immport.org/home].

Acknowledgments

The authors would like to thank the Central Laboratory of Hainan Medical University, where most of this work was performed.

Ethics approval and consent to participate

All experiments were carried out in accordance with the Code of Ethics of the World Medical Association. The Ethics Committee of Hainan Medical University conducted a review of our research and granted approval(2024-KCSN-13). All the human subjects gave their informed consent for the use of their samples and data.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflicts of interest.

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No competing interests reported.

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Significance of Tumor Mutation Burden related immune gene PAEP in the progression and prognosis of clear cell renal cell carcinoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Data source and mutation analysis

TMB-Related differentially expressed genes and functional enrichment analysis

Identification and Coexpression analysis of TMB-related immune genes

Cell culture and and treatment

Short hairpin RNA construction and cell transfection

Patient selection and preparation of tissue

Western blotting

qPCR

Cell Counting Kit‑8 (CCK‑8) assay

Flow cytometry

Cell proliferation, migration and invasion assays

Tumor formation in nude mice

Statistical analysis

Results

Mutation analysis

Correlation of TMB with prognosis and clinical features

TMB-Related differentially expressed genes and Functional enrichment analysis

Comparison of gene expression profiles between two tmb groups

PAEP was overexpression and associated with shorter survival in ccRCC patents

PAEP knockdown inhibited ccRCC cells growth

PAEP induces activation of the PI3K/Akt/NF-κB pathway

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1