The renal clear cell carcinoma immune landscape

Follistatin-like protein 1: Implications for renal disease progression

Renal diseases, including glomerulonephritis, acute kidney injury, chronic kidney failure, and kidney tumors are all current global health challenges. Lesions in other systems can cause renal diseases and can affect other systems or even the whole body. Despite ongoing advancements in pharmaceutical and technological innovations, the prognosis for end-stage renal disease, encompassing renal failure and tumors, continues to be bleak. Follistatin-like protein 1 (FSTL1) is a secreted glycoprotein produced mainly by mesenchymal cells. FSTL1 is a glycoprotein that belongs to the family of secreted, cysteine-rich acidic proteins (SPARC). It plays a pivotal role in cell survival, proliferation, differentiation, and migration, as well as in modulating inflammation and immune responses. Research has shown that FSTL1 plays a crucial role in the onset and progression of renal diseases. This review explores the functions and underlying mechanisms of FSTL1 in kidney pathology. SIGNIFICANCE STATEMENT: This review highlights the pivotal role of FSTL1 in renal diseases, particularly its involvement in renal fibrosis, inflammation, and ischemia-reperfusion injury. By elucidating its dual roles across different pathologies, this work underscores FSTL1's potential as both a biomarker and a therapeutic target, offering novel insights for managing chronic kidney disease and associated complications.

APOBEC3C-mediated NF-κB activation enhances clear cell renal cell carcinoma progression

Renowned as the predominant form of kidney cancer, clear cell renal cell carcinoma (ccRCC) exhibits susceptibility to immunotherapies due to its specific expression profile as well as notable immune cell infiltration. Despite this, effectively treating metastatic ccRCC remains a significant challenge, necessitating a more profound comprehension of the underlying molecular mechanisms governing its progression. Here, we unveil that the enhanced expression of the RNA-binding protein DNA dC → dU-editing enzyme APOBEC-3C (APOBEC3C; also known as A3C) in ccRCC tissue and ccRCC-derived cell lines serves as a catalyst for tumor growth by amplifying nuclear factor-kappa B (NF-κB) activity. By employing RNA-sequencing and cell-based assays in ccRCC-derived cell lines, we determined that A3C is a stress-responsive factor and crucial for cell survival. Furthermore, we identified that A3C binds and potentially stabilizes messenger RNAs (mRNAs) encoding positive regulators of the NF-κB pathway. Upon A3C depletion, essential subunits of the NF-κB family are abnormally restrained in the cytoplasm, leading to deregulation of NF-κB target genes. Our study illuminates the pivotal role of A3C in promoting ccRCC tumor development, positioning it as a prospective target for future therapeutic strategies.

Identification of a risk model for prognostic and therapeutic prediction in renal cell carcinoma based on infiltrating M0 cells

The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with therapeutic implications. This study aims to identify novel biomarkers to differentiate ccRCC from non-ccRCC using bioinformatics and machine learning. The gene expression profiles of ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe RCC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAVID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistochemistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly specific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC enriched in PD-1 signaling, immune system, and cytokine signaling in the immune system, whereas TCA cycle and respiratory, signaling by insulin receptor, and metabolism were enriched in chRCC. Feature selection based on Decision Tree Classifier revealed that the model with two genes, including NDUFA4L2 and DAT, had an accuracy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best performance with 82% accuracy and 0.9 AUC. NDUFA4L2 expression was associated with lymphovascular invasion, pathologic stage and pT stage in ccRCC. Using integrated bioinformatics and machine learning analysis, NDUFA4L2 and DAT were identified as novel biomarkers to differential diagnosis ccRCC from non-ccRCC.

NKG2A+CD8+ T cells infiltration determines immunosuppressive contexture and inferior response to immunotherapy in clear cell renal cell carcinoma

BACKGROUND

Immunotherapy is gaining momentum, but current treatments have limitations in terms of beneficiaries. Clear cell renal cell carcinoma (ccRCC) harbors the highest expression of human leukocyte antigen E (HLA-E), ligand of NKG2A, among all solid tumors. In this study, we aim to investigate the role of NKG2A+CD8+ T cells in tumor microenvironment and its potential as a novel target in ccRCC.

METHODS

This study included four independent cohorts, including 234 patients from Zhongshan cohort (ZSHC) who underwent partial or radical nephrectomy at Zhongshan Hospital, and 117 metastatic patients from metastatic Zhongshan cohort (ZSHC-metastatic renal cell carcinoma) who were treated with immune checkpoint inhibitor or tyrosine kinase inhibitor alone. We also incorporated a cohort of 530 patients diagnosed with ccRCC from The Cancer Genome Atlas (referred to as TCGA-kidney renal clear cell carcinoma) and 311 patients from CheckMate cohort for bioinformatics exploration and hypothesis validation. Fresh surgical specimens from 15 patients who underwent ccRCC surgery at Zhongshan Hospital were collected for flow cytometry analysis. Another 10 fresh surgical specimens were used to investigate the therapeutic potential of NKG2A blockade after in vitro intervention. The infiltration of NKG2A+CD8+ T cells was assessed using immunohistochemical staining, flow cytometry, and immunofluorescence staining in ZSHC cohort.

RESULTS

Patients with higher infiltration of NKG2A+CD8+ T cells in ccRCC exhibited shorter overall survival and resistance to immunotherapy. NKG2A+CD8+ T cells expressed upregulated checkpoint molecules and displayed impaired effector functions, along with tissue-residency characteristics. Combination of programmed cell death protein-1 (PD-1) blockade and NKG2A blockade demonstrated an enhanced capability in reactivating CD8+ T cells effector functions.

CONCLUSION

Intense infiltration of NKG2A+CD8+ T cells were associated with poorer prognosis and response to immunotherapy. NKG2A blockade combined with current immunotherapy exhibited a robust ability to reactivate CD8+ T cells effector functions.

BTN3A3 inhibits clear cell renal cell carcinoma progression by regulating the ROS/MAPK pathway via interacting with RPS3A

Butyrophilin subfamily 3 member A3 (BTN3A3) is a member of the immunoglobulin superfamily and functions as a tumor suppressor in multiple cancer types. Our study has revealed that in clear cell renal cell carcinoma (ccRCC), patients who express high levels of BTN3A3 experience longer survival times than those with lower expression. Further, we have observed that BTN3A3 inhibits the proliferation, migration, and invasion of ccRCC cells. Through the utilization of an immunoprecipitation assay followed by mass spectrometry, we have discovered that BTN3A3 binds directly to RPS3A. Knockdown of BTN3A3 led to increased cell proliferation, migration, and invasion. However, this effect was significantly reduced when RPS3A was simultaneously overexpressed. Previous reports have demonstrated that RPS3A positively regulates mitochondrial function and reactive oxygen species (ROS) levels. Our study has shown that overexpression of both BTN3A3 and RPS3A can increase cellular oxygen consumption rate (OCR) and ROS levels. Furthermore, we have observed that the addition of H2O2 can reverse the effects of BTN3A3 knockdown on cell proliferation and migration by increasing the cellular ROS level. ROS play a crucial role in regulating the MAPK pathway and tumor cell growth. To further explore this relationship, we examined RNA-Seq and immunoblotting data and found that BTN3A3 can negatively regulate the degree of activation of the MAPK signaling pathway. This finding suggests that the BTN3A3/RPS3A complex may regulate ccRCC progression by modulating MAPK pathways. Therefore, BTN3A3 could serve as both a prognostic marker and a potential therapeutic target for ccRCC patients.

Association of lncRNA and transcriptome intersections with response to targeted therapy in metastatic renal cell carcinoma

Long non-coding RNAs (lncRNAs) serve an important role in cancer progression and may be used as efficient molecular biomarkers. The present study aimed to identify lncRNAs associated with the response to the receptor tyrosine kinase inhibitor sunitinib and transcriptome profile and clinical features of metastatic renal cell carcinoma (mRCC). The gene expression of 84 cancer-associated lncRNAs in tumor and non-malignant tissue samples of 38 patients with mRCC was evaluated using quantitative PCR. In addition, the coding transcriptome was estimated using RNA sequencing in a subgroup of 20 patients and mRNA-lncRNA intersections were identified. In total, 37 and 13 lncRNAs were down- and upregulated, respectively, in tumor compared with non-malignant adjacent tissue samples. A total of 10 and 4 lncRNAs were up- and downregulated, respectively, in good responders to sunitinib compared with poor responders. High expression of HNF1A-AS1 and IPW lncRNAs was associated with prolonged progression-free survival of patients and a high expression of the TUSC7 lncRNA was associated with poor response and worse survival. Significant associations of dysregulated MEG3 and SNHG16 lncRNAs with expression of protein-coding genes representing various pathways, were identified. Furthermore, a significantly higher expression of CLIP4 gene was observed in good responders. The present study revealed promising candidates for predictive and prognostic biomarkers with further therapeutic potential.

Crosstalk of necroptosis and pyroptosis defines tumor microenvironment characterization and predicts prognosis in clear cell renal carcinoma

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.