Effects of Purine Metabolism-Related LINC01671 on Tumor Heterogeneity in Kidney Renal Clear Cell Carcinoma

Background : Renal cell carcinoma has several subtypes, with kidney renal clear cell carcinoma (KIRC) being the most common and heterogeneous. Purine metabolism is associated with cancer progression. However, the role of purine metabolism-related long non-coding RNAs (lncRNAs) in KIRC remains unknown. Methods : KIRC were grouped into Cluster-1 and Cluster-2 based on purine genes. Limma package was used to identify differentially expressed lncRNAs between two classes of purine genes. Single-factor screening was used followed by random forest dimensionality reduction and Lasso method to screen lncRNAs. A risk score model (Purine Score) containing the 3 lncRNAs was developed using the Lasso method. Results : A total of 22 differentially expressed lncRNAs were identified. These were reduced to a final set of three ( LINC01671 , ARAP1-AS1 and LINC02747 ). Age and metastasis (M) were identified as independent prognostic factors for KIRC using univariate and multivariate Cox analysis. An abnormal immune cell response was also associated with patient survival. The Purine Score correlated with abnormal expression of immune checkpoint genes. Genetic analysis of KIRC found somatic mutations in TP53 , TRIOBP , PBRM1 , PKHD1 , VHL , NPHP3 , TLN2 , CABIN1 , ABCC6 , XIRP2 , and CHD4 . In vitro cell experiments showed that knockdown of LINC01671 promoted the proliferation and migration of 786-O cells, while inhibiting apoptosis. Overexpression of LINC01671 inhibited the proliferation and migration of CAKI-1 cells, while promoting apoptosis. Gene Set Enrichment Analysis (GSEA) analysis revealed that LINC01671 was significantly enriched in the MAPK, NF-kappa B, mTOR, PI3K-Akt, and Wnt signaling pathways. Conclusions : LINC01671 may be a novel prognostic marker with important therapeutic value for KIRC.


Introduction
Kidney epithelium is the site of origin of renal cell carcinoma (RCC), which accounts for around 90% of all kidney cancers [1].The most prevalent subtype of RCC is kidney renal clear cell carcinoma (KIRC) [2], characterized by a high cytoplasmic lipid content and considered to be a metabolic cancer [3].KIRC is a highly metastatic and recurrent malignant renal tumor associated with high morbidity and mortality [4], and has become a major health problem worldwide [5].This cancer type is characterized by mutations in genes that control the hypoxia signaling pathway, thus leading to metabolic imbalance, enhanced angiogenesis, intra-tumoral heterogeneity, and a harmful tumor microenvironment (TME) [6].KIRC also interacts with the TME, which helps to guide appropriate treatment [7].It is therefore imperative to achieve a comprehensive understanding of the molecular mechanisms that underlie KIRC and to devise effective strategies for its timely diagnosis and treatment.
The reprogramming of energy metabolism is a hallmark of cancer that has recently gained special attention due to its promotion of cell growth and proliferation [8].Purine is a vital substrate in organisms and serves as a crucial material for cell proliferation and important factor in immune regulation [9].It is also an essential component of various cellular processes, including energy metabolism, cell signaling, and the encoding of genetic material [10].The final product of purine metabolism in humans, uric acid, has potent antioxidant properties [11].Dysfunction of purine metabolism has serious physiological and pathological consequences [12], and impaired purine metabolism is associated with cancer progression [13].Jackson RC et al. [14] were the first to describe the involvement of purine metabolism enzymes in the renal cortex and kidney cells of RCC in humans and rats.However, the role of purine metabolism in KIRC is not yet fully understood.
Long non-coding RNAs (lncRNAs) are RNA transcripts >200 nucleotides in length that bind to DNA, RNA and proteins [15] and are thus capable of modulating cellular physiology and function.Disruption of lncRNA expression or function is closely associated with various inherited, autoimmune and metabolic diseases, as well as tumors [16].The overexpression of oncogenic lncRNAs and reduction of tumor suppressor lncRNAs are common features of human RCC.Abnormal expression of lncRNAs is a crucial factor in RCC progression and an indicator of poor prognosis for these patients [17].The role in KIRC of lncRNAs related to purine metabolism has yet to be determined.
With the above background in mind, we performed non-negative matrix factorization (NMF) clustering to classify KIRC and identify purine-related patterns.We developed a purine-related, differential lncRNA risk score (Purine Score) to predict the outcome of KIRC patients.We also conducted analyses of immune cell infiltration, immune checkpoint expression, and gene mutation.Finally, we carried out preliminary in vitro cell experiments to validate the function of purine metabolism-related differential lncRNAs.Characterization of these lncRNAs could help to guide the development of more personalized treatment strategies for KIRC.

Dataset and Preprocessing
The dataset for KIRC was downloaded from The Cancer Genome Atlas (TCGA) located at UCSC Xena (https: //xenabrowser.net/).RNA sequencing (RNA-seq) data was extracted from the TCGA data portal.Values for fragments per kilobase million (FPKM) were converted to transcripts per million (TPM).

Clustering of Purine Genes in KIRC
A total of 130 purine-related genes were obtained from KEGG (hsa00230; purine metabolism), and 129 overlapping genes were identified by intersecting with the TCGA gene set.Single-factor Cox filtering was used to identify 65 genes with p < 0.01.NMF clustering was used to classify KIRC and to identify purine-related patterns, with the patients then grouped for subsequent analysis.

Development of a Purine-Related LncRNA Risk Score
The limma package (version 3.56.2,https://bioinf.wehi.edu.au/limma/) was employed for single-factor filtering and to identify differentially expressed lncRNAs in the purine-associated category, using a significance threshold of p < 0.05 and |logFC| >1.Random survival forest was then used to perform further screening, and the Lasso method was employed to build a model using the selected genes.The risk score was calculated by multiplying gene expression values with the regression coefficients.The surv_cutpoint function of the survminer package (version 0.4.9, https://rpkgs.datanovia.com/survminer/index.html) was then applied to classify patients into high-and low-risk groups.The timeROC R package (version 0.4, https://ww w.rdocumentation.org/packages/timeROC/versions/0.4) was used to plot time-ROC curves.

Immune Cell Infiltration and Pathway Analysis
The MCPcounter and TIMER algorithms were utilized to evaluate immune cell abundance in KIRC samples and to identify disparities in immune cell infiltration across distinct clustering categories or risk groups.Gene Set Enrichment Analysis (GSEA) was performed to examine KEGG pathway regulation.The maftools R package (version 2.16.0, https://github.com/PoisonAlien/maftools)was used for mutation analysis.

Cell Culture and Treatments
The cells of the human RCC line 786-O (AW-CCH060) and CAKI-1 (AW-CCH173) were purchased from Abiowell (Changsha, Hunan, China).786-O or CAKI-1 cells were cultured in RPMI-1640 or McCoy's 5A medium containing 10% fetal bovine serum and 1% Penicillin/Streptomycin.All cells were maintained at 37 °C with 5% CO 2 in a humidified atmosphere.All cell lines have been authenticated in the past three years.All cell lines were identified by STR profile.All experiments were performed with mycoplasma-free cells.Both knockdown and overexpression of LINC01671 were performed.786-O cells were divided into control, sh-NC, and sh-LINC01671 groups, and CAKI-1 cells into control, oe-NC, and oe-LINC01671 groups.The sh-LINC01671, oe-LINC01671, and negative controls (sh-NC and oe-NC) were provided by HonroGene (Changsha, Hunan, China).All transfections were performed using Lipofectamine 2000 (11668019, Invitrogen, Waltham, MA, USA).

Cell Migration Assay
Cells were suspended in serum-free medium at 1 × 10 6 /mL and 100 µL was added to the upper chamber of a Transwell (33318035, Corning, Somerville, MA, USA).The lower chamber was filled with complete medium containing 10% fetal bovine serum (FBS).After removal of the culture medium from the upper chamber, the upper surface of cells was wiped with a wet cotton swab.The cells were then fixed and stained with crystal violet.Cells on the outer surface of the upper chamber were observed and photographed under a microscope (Olympus, Tokyo, Japan).

Flow Cytometry
Cells were digested and centrifuged, and about 3.2 × 10 5 cells were collected.These were suspended in 500 µL of binding buffer, and 5 µL allophycocyanin (APC) and propidium iodide were added and mixed.Reaction was carried out in the dark, and the cells analyzed by flow cytometry (A00-1-1102, Beckman, Pasadena, CA, USA) within 1 h.

Terminal Deoxynucleotidyl Transferase Mediated dUTP Nick-End Labeling (TUNEL)
A TUNEL apoptosis detection kit (FITC) (40306ES50, Yeasen, Shanghai, China) was used to evaluate cell apoptosis.After fixation, cells were permeabilized with Triton X-100 and sodium citrate solution, and then treated with fluorescent-labeled nucleotides (dUTP) and TdT.TUNEL-positive cells with green fluorescence were observed and quantified with a fluorescence microscope.

Statistical Analysis
Normative variables were tested using the Shapiro-Wilk test, while normally distributed variables were compared with unpaired Student's t-test.Non-normally distributed variables were compared using the Wilcoxon test.One-way analysis of variance (ANOVA) was used as a parametric method to compare multiple groups, and the Kruskal-Wallis test as a non-parametric method.For each dataset, patients were categorized by binary risk score, with the R package ggplot2 (version 3.4.3,https://ggplot2.tidyverse.org/)used to visualize data.The Benjamini-Hochberg method was used to analyze differential gene expression.Significant genes were identified via  The Kaplan-Meier method was used to compare survival of different patient groups, with the logarithmic rank test used to assess whether differences were statistically significant.All survival curves were generated using the R package survminer, and all heatmaps using pheatmap.Statistical analysis was performed using R (version 3.6.1,https://www.r-project.org/), with statistical significance set at p < 0.05 for two-sided tests.

Clustering of KIRC Based on Purine Genes
A total of 130 purine-related genes (KEGG: hsa00230; purine metabolism) were identified from the literature.The intersection of these genes with TCGA resulted in 129 genes.Subsequently, 65 genes were selected by univariate Cox analysis (p < 0.01, Table 1).KIRC were grouped into Cluster-1 and Cluster-2 according to purine genes.As the number of clusters increased, the cophenetic coefficient decreased (Fig. 1A).Survival analysis showed that Cluster-2 patients had better survival than Cluster-1 patients (Fig. 1B).A volcano plot was used to visualize differences in lncRNAs between the two clusters.The limma package identified 22 differentially expressed lncRNAs between Cluster-1 and Cluster-2 (Fig. 1C).A heatmap further visualized the expression of purine-related lncRNAs between the two clusters (Fig. 1D).

Prediction of KIRC Patient Outcome Using Purine Score
According to the risk score model established following TCGA survival analysis, patients with high risk scores had worse prognosis (p < 0.05, Fig. 3A).Moreover, based on receiver operating characteristic (ROC) analysis, the area under the curve (AUC) for Purine Score in TCGA were 0.688, 0.681, and 0.695 for 1-, 3-, and 5-year true-positive rates, respectively (Fig. 3B).We also investigated prognostic factors using univariate and multivariate Cox analyses.Age and metastasis (M) were found to be independent prognostic factors for KIRC patients (Fig. 3C).Finally, the Purine Score was evaluated according to various clinical features (Fig. 3D).Significant differences in the Purine Score were found according to gender, grade, stage, tumor (T), node (N) and status (p < 0.05).

Analysis of Immune Cell Infiltration and Immune Checkpoints
Next, we performed a correlation analysis between prognosis and immune cell infiltration.As shown in Fig. 4A, the Purine Score correlated with cellular immune response and cell components, as determined by the MCPcounter and TIMER algorithms.The survival model included abnormalities in cytotoxic lymphocytes, NK cells, myeloid dendritic cells, monocytic lineage, endothelial cells, neutrophils, B cells, T cells CD4, neutrophils, macrophages, and DCs.Patients with a low Purine Score showed significantly higher scores for ESTIMATE (p

Analysis of Gene Mutations
Somatic mutation analysis revealed alterations in 77 of 97 (79.38%)KIRC with a high Purine Score (Missense Mutation, Frame Shift Del, Splice Site, Frame Shift Ins, In Frame Ins, Nonsense Mutation, Translation Start Site, and Multi Hit).Moreover, 211 of 235 (89.79%)KIRC with a low Purine Score showed alterations (Nonsense Mutation, Frame Shift Del, Frame Shift Ins, Missense Mutation, In Frame Ins, Translation Start Site, In Frame Del, and Nonstop Mutation, Fig. 5A).Gene mutation frequencies for the high and low Purine Score groups are shown in Fig. 5B.The mutation frequencies for TP53, TRIOBP, PKHD1, NPHP3, TLN2, CABIN1, ABCC6, XIRP2, and CHD4 were significantly higher in the high Purine Score group compared to the low Purine Score group, whereas the mutation frequencies for PBRM1 and VHL were significantly lower.Furthermore, in the high Purine Score group, VHL mutation co-occurred with PBRM1 mutation, PBRM1 with SETD2, SETD2 with MUC17, MTOR with CHD4, KDM5C with ABCC6, TP53 with MUC17, XIRP2 with CHD4 and CSMD3, CSMD3 with CABIN1, and CABIN1 with DST.In the low Purine Score group, PBRM1 mutation did not co-occur with BAP1, TTN mutation co-occurred with BRCA2, ANK3, HMCN1 and BAP1 mutation, SETD2 mutation co-occurred with LRP2, BAP1 and MUC16 mutation, and ARID1A with DNAH9 (Fig. 5C).

Validation of LncRNA Expression
Next, we used in vitro experiments to validate cell expression of the selected 3 lncRNAs (LINC01671, ARAP1-AS1, and LINC02274).qRT-PCR revealed high expression of LINC01671 in 786-O cells, and low expression in CAKI-1 cells.Since ARAP1-AS1 and LINC02274 were highly expressed in both 786-O and CAKI-1 cell lines (Fig. 6A), LINC01671 was therefore selected for further study.Survival analysis showed that high expression of LINC01671 was associated with improved survival (Fig. 6B).Next, LINC01671 was knocked down in 786-O cells and overexpressed in CAKI-1 cells.Successful transfection of sh-LINC01671 and of oe-LINC01671 was achieved, as shown in Fig. 6C.Cell function experiments showed that knockdown of LINC01671 in 786-O cells promoted their prolif- eration and migration, but inhibited apoptosis.In contrast, overexpression of LINC01671 in CAKI-1 cells inhibited their proliferation and migration, while promoting apoptosis (Fig. 6D-G).

Discussion
KIRC is most common histological subtype of RCC and is more likely to metastasize, relapse, and resist radiotherapy and chemotherapy [18].Various types of drug resistance can occur in KIRC due to the highly dynamic, adaptable and heterogeneous nature of its TME, as well as to aberrant glucose and lipid metabolism [19,20].Hence, there is an urgent need for non-invasive tools to accurately stratify and select patients for treatment.In the present study, we performed NMF clustering to develop a purine-related differential lncRNA risk score (Purine Score).We then analyzed immune cell infiltration, immune checkpoints and gene mutations in KIRC.Finally, we conducted in vitro experiments with KIRC cell lines to validate the function of purine metabolism-related differential lncRNAs.Our study found that purine metabolism-related LINC01671 may be a key target for KIRC, thus affecting tumor heterogeneity.
Cancer cells undergo metabolic adaptation through multiple endogenous and exogenous signaling pathways.This enhances malignant cell growth and also initiates the transformative process of cell adaptation to the TME [21].RCC is essentially a metabolic disease characterized by the reprogramming of energy metabolism [22][23][24][25].In particular, the metabolic flux through glycolysis is partitioned [26][27][28], and mitochondrial bioenergetics, OxPhox and lipid metabolism are all impaired [26,[29][30][31].The translocation of metabolites related to the pentose phosphate pathway (PPP) are also known to be altered in RCC.The PPP supports key aspects of accelerated tumor growth and generates precursors for nucleotide synthesis.The "Warburg effect" is the first historical evidence that cancer cells can adjust their metabolism in order to promote cell growth.Indeed, the increased glucose uptake and metabolism that underlie the Warburg effect are now considered as one of the hallmarks of cancer [32].Purinergic signaling is a cellular communication pathway mediated by extracellular nucleotides and nucleosides [33].The nucleoside adenosine has crucial roles in the regulation of purine biosynthesis, gene translation, and the fate of RNA [34].Purines are components of nucleic acids and have important physiological functions as intracellular and extracellular signaling molecules.Purine metabolites, especially uric acid, are associated with congenital and complex diseases [35].It has been shown that cellular metabolism is disrupted in RCC tumors, and that changes in purine metabolism are associated with the poor survival of RCC patients [36].In the present study, KIRC were clustered according to purine genes, and a purinerelated, differential lncRNA risk score (Purine Score) was developed to predict the outcome of KIRC patients.We found that age, N, and M were independent prognostic factors for KIRC patients.
RCC is one of the most heavily immune-infiltrated tumors [37,38], and the immune response is a critical factor in the occurrence and treatment of KIRC [39].Emerging evidence suggests that activation of specific metabolic pathways may play a role in regulating angiogenesis and inflammatory signatures [40,41].Features of the TME may also strongly affect disease biology and the response to systemic therapy [42][43][44][45].Therefore, identifying the cells of origin for RCC, as well as novel cell types within the TME, are very important for the development of targeted therapies [46].In the current research we therefore investigated the relationship between Purine Score and cellular components or immune responses.An abnormal immune cell response was associated with survival models (T, N, M, stage, gender, age, and status).A correlation was found between Purine Score and abnormal expression of immune checkpoint genes.Phenotypic variation can be observed as intratumoral heterogeneity, which leads to genomic instability resulting in mutations, somatic copy number alterations, and epigenomic changes [47].The heterogeneity observed between RCC subtypes is related to significant differences in tumor invasiveness and the risk of metastatic disease [48].Most clear cell carcinomas (sporadic and familial) are associated with mutations and deletions of the VHL gene on 3p.25, as well as other nearby genes (SETD2, BAP1, PBRM1) [49,50].In this study, we found mutations in TP53, TRIOBP, PBRM1, PKHD1, VHL, NPHP3, TLN2, CABIN1, ABCC6, XIRP2 and CHD4.Deletion of these tumor suppressor genes may play a role in the development of KIRC and affect the clinical course of this disease.
Molecular characterization of RCC helps to identify driver genes and specific molecular pathways, as well as the characterization of TME, thereby enhancing our understanding of this cancer type [51].Aberrant expression of lncRNAs is closely associated with various diseases, such as the occurrence and development of cancers [52].It has been reported that ferroptosis-related lncRNAs could accurately predict the outcome of KIRC [53].A prognostic signature of angiogenesis-associated gene-related lncR-NAs shows promise as an independent prognostic indicator for KIRC patients [54].Moreover, previous studies have suggested that LINC01671 may be a useful indicator for clinical stratification management and treatment decisionmaking in lung adenocarcinoma patients [55,56].Su et al. [57] reported that LINC01671 is a protective gene in clear cell RCC (hazard ratio <1).LINC01671 was found to be significantly associated with OS using multivariate Cox regression, which is the first report that LINC01671 has been associated with clear cell RCC in their study.However, research on LINC01671 in KIRC is limited.In our study, our bioinformatics analysis also showed that LINC01671 has a hazard ratio <1, indicating that LINC01671 is a protective gene.Survival analysis revealed that patients with high expression of LINC01671 have a higher survival rate, suggesting that LINC01671 has a positive impact on the prognosis of KIRC.Furthermore, in vitro experiments revealed that interfering with LINC01671 promoted proliferation and migration of 786-O cells while suppressing apoptosis.Overexpression of LINC01671 inhibited proliferation and migration of CAKI-1 cells while promoting apoptosis.Therefore, our results provide evidence that LINC01671 is a protective gene in KIRC and enrich our understanding of its regulatory role in cancer.Further analysis sug-gested that LINC01671 can positively regulate the MAPK, NF-kappa B, mTOR, PI3K-Akt, and Wnt signaling pathways.However, the specific cellular mechanisms involving LINC01671 require further study.

Conclusions
In summary, purine metabolism-related LINC01671 plays an important role in the development, progression and prognosis of KIRC.We constructed a purine-related, differential lncRNA risk score model (Purine Score) that can predict the survival of KIRC patients with high accuracy.This study has identified new candidate genes for the treatment of KIRC patients.

Fig. 3 .
Fig. 3. Prediction of outcome in KIRC patients using Purine Score.(A) Survival analysis based on the risk score.(B) Receiver operating characteristic (ROC) analysis.(C) Identification of prognostic factors using univariate and multivariate Cox analyses.(D) Analysis of Purine Score according to clinical features.TCGA, The Cancer Genome Atlas.

Fig. 5 .
Fig. 5. Analysis of gene mutations.(A) Somatic mutation analysis.(B) Gene mutation frequencies in the high and low Purine Score groups.(C) Co-occurrence of mutated genes in the high and low Purine Score groups.Ip < 0.05, *p < 0.01.

Fig. 7 .
Fig. 7. Functional enrichment analysis of LINC01671.Gene Set Enrichment Analysis (GSEA) of the function and pathways for LINC01671.
the conversion of p-values to false discovery rate (FDR).