The presence of tumour-infiltrating neutrophils is an independent adverse prognostic feature in clear cell renal cell carcinoma

Citrullinated Histone H3, a Marker for Neutrophil Extracellular Traps, Is Associated with Poor Prognosis in Cutaneous Squamous Cell Carcinoma Developing in Patients with Recessive Dystrophic Epidermolysis Bullosa

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare severe hereditary skin disease characterized by skin and mucosa fragility, resulting in blister formation. The most severe complication in RDEB patients is the development of cutaneous squamous cell carcinoma (SCC), leading to premature death. There is a great deal of evidence suggesting a permissive tumor microenvironment (TME) as a driver of SCC development in RDEB patients. In a cohort of RDEB patients, we characterized the immune profiles of RDEB-SCCs and compared them with clinical, histopathological, and prognostic features. RDEB-SCCs were subdivided into four groups based on their occurrence (first onset or recurrences) and grading according to clinical, histopathological parameters of aggressiveness. Thirty-eight SCCs from 20 RDEB patients were analyzed. Five RDEB patients experienced an unfavorable course after the diagnosis of the first SCC, with early recurrence or metastasis, whereas 15 patients developed multiple SCCs without metastasis. High-risk primary RDEB-SCCs showed a higher neutrophil-to-lymphocyte ratio in the tumor microenvironment and an increased proportion of neutrophil extracellular traps (NETs). Additionally, citrullinated histone H3, a marker of NETs, was increased in the serum of RDEB patients with high-risk primary SCC, suggesting that this modified form of histone H3 may serve as a potential blood marker of unfavorable prognosis in RDEB-SCCs.

Immature stroma and high infiltration of CD15+ cells are predictive markers of poor prognosis in different subsets of patients with pancreatic cancer

Preoperative treatment is commonly carried out for borderline resectable pancreatic ductal adenocarcinoma (PDAC). However, the relationship between the combination of immune cells in the tumor microenvironment and their intratumoral heterogeneity along with their association with histological findings remains unclear, especially in patients receiving preoperative chemotherapy. We aimed to explore the therapeutic strategies for patients with PDAC with poor prognosis after receiving chemotherapy based on histological and immunological microenvironmental classifications. We investigated the correlation between the prognosis and histological immune microenvironmental factors of patients who initially underwent surgery (n = 100) and were receiving gemcitabine plus nab-paclitaxel (GEM + nabPTX) as preoperative chemotherapy (n = 103). Immune profiles were generated based on immune cell infiltration into the tumor, and their correlation with patient outcomes and histological features was analyzed. Tumor-infiltrating neutrophils (TINs) were identified as independent poor prognostic factors using multivariate analysis in both surgery-first and preoperative chemotherapy groups. The patients were further classified into four groups based on immune cell infiltration into the tumor. Patients with high CD15 infiltration into the tumor and immature stroma at the cancer margins showed the worst prognosis in the preoperative chemotherapy group. The analysis of mRNA expression and immunohistochemical features revealed that CXCR2, the receptor for CXCL8, was correlated with disease-free and overall survival. We inferred that patients with immature stroma at the margins and high infiltration of CD15+ neutrophils within the tumor showed the worst prognosis and they could particularly benefit from treatment with inhibitors targeting CXCR2 or CXCL8.

Genome-wide analyses reveal the contribution of somatic variants to the immune landscape of multiple cancer types

It has been well established that cancer cells can evade immune surveillance by mutating themselves. Understanding genetic alterations in cancer cells that contribute to immune regulation could lead to better immunotherapy patient stratification and identification of novel immune-oncology (IO) targets. In this report, we describe our effort of genome-wide association analyses across 22 TCGA cancer types to explore the associations between genetic alterations in cancer cells and 74 immune traits. Results showed that the tumor microenvironment (TME) is shaped by different gene mutations in different cancer types. Out of the key genes that drive multiple immune traits, top hit KEAP1 in lung adenocarcinoma (LUAD) was selected for validation. It was found that KEAP1 mutations can explain more than 10% of the variance for multiple immune traits in LUAD. Using public scRNA-seq data, further analysis confirmed that KEAP1 mutations activate the NRF2 pathway and promote a suppressive TME. The activation of the NRF2 pathway is negatively correlated with lower T cell infiltration and higher T cell exhaustion. Meanwhile, several immune check point genes, such as CD274 (PD-L1), are highly expressed in NRF2-activated cancer cells. By integrating multiple RNA-seq data, a NRF2 gene signature was curated, which predicts anti-PD1 therapy response better than CD274 gene alone in a mixed cohort of different subtypes of non-small cell lung cancer (NSCLC) including LUAD, highlighting the important role of KEAP1-NRF2 axis in shaping the TME in NSCLC. Finally, a list of overexpressed ligands in NRF2 pathway activated cancer cells were identified and could potentially be targeted for TME remodeling in LUAD.

m1A Regulatory gene signatures are associated with certain immune cell compositions of the tumor microenvironment and predict survival in kidney renal clear cell carcinoma

Adenosine N1 methylation (m1A) of RNA, a type of post-transcriptional modification, has been shown to play a significant role in the progression of cancer. The objective of the current research was to analyze the genetic alteration and prognostic significance of m1A regulators in kidney renal clear cell carcinoma (KIRC). Genomic and clinicopathological characteristics were obtained from 558 KIRC patients in the Cancer Genome Atlas (TCGA) and Gene Omnibus Expression (GEO) databases. Alterations in the gene expression of ten m1A-regulators were analyzed and survival analysis was performed using the Cox regression method. We also identified three clusters of patients based on their distinct m1A alteration patterns, using integrated analysis of the ten m1A-related regulators, which were significantly related to overall survival (OS), disease-free survival (DFS) and tumor microenvironment (TME) immune cell infiltration cells in KIRC. Our findings showed that m1A alteration patterns have critical roles in determining TME complexity and its immune cell composition. Furthermore, different m1A expression patterns were significantly associated with DFS and OS rates in KIRC patients. In conclusion, the identified m1A RNA modification patterns offer a potentially effective way to classify KIRC patients based on their TME immune cell infiltration, enabling the development of more personalized and successful treatment strategies for these patients.

Preoperative serum low-density lipoprotein cholesterol is an independent prognostic factor in patients with renal cell carcinoma after nephrectomy

BACKGROUND

Little is known about the association between the preoperative low-density lipoprotein cholesterol (LDL-C) level and prognosis in patients with renal cell carcinoma (RCC) after nephrectomy, and its prognostic value needs to be elucidated.

METHODS

The clinical and follow-up data of 737 RCC patients who underwent nephrectomy were retrospectively analyzed. The optimal cut-off LDL-C level was determined using X-tile, and then patients were divided into low and high LDL-C groups. The association between LDL-C levels and survival of RCC patients was assessed using the Kaplan-Meier method and Cox regression analysis.

RESULTS

The optimal cut-off LDL-C level was 1.93 mmol/L, and patients were divided into the low (≤ 1.93 mmol/L) and high LDL-C (> 1.93 mmol/L) groups. The Kaplan-Meier analysis showed that patients in the low LDL-C group had significantly shorter overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) than those in the high LDL-C group (P = 0.001, P = 0.001, and P = 0.003, respectively). The COX univariate analysis showed that the preoperative LDL-C level was closely associated with OS, CSS, and RFS in RCC patients (P = 0.002, P = 0.003, and P = 0.005, respectively). The multivariate analysis showed that the preoperative LDL-C level was an independent factor for predicting survival (OS, CSS and RFS) in RCC patients after nephrectomy. The low preoperative LDL-C levels predicted worse OS (hazard ratio [HR]: 2.337; 95% confidence interval [CI]: 1.192-4.581; P = 0.013), CSS (HR: 3.347; 95% CI: 1.515-7.392; P = 0.003), and RFS (HR: 2.207; 95% CI: 1.178-4.132; P = 0.013).

CONCLUSIONS

The preoperative LDL-C level is an independent factor for the prognosis of RCC patients after nephrectomy, and low preoperative LDL-C levels predict worse survival (OS, CSS, and RFS).

The Infiltration of Neutrophil Granulocytes Due to Loss of PTEN Was Associated with Poor Response to Immunotherapy in Renal Cell Carcinoma

Introduction

A primary impediment to the efficacy of immune checkpoint inhibitors is the lack of biomarkers for therapeutic responses and prognosis. Although patients with clear cell renal cell carcinoma (ccRCC) could be precisely selected for targeted therapy based on somatic mutations, it remains controversial to choose the suitable patients with a high response rate to immune checkpoint inhibitors (ICIs). The immune-dependent roles of tumor suppressor PTEN in the formation of tumor immune microenvironment remain elusive.

Methods

We comprehensively analyzed the genomic and transcriptomic data from multiple ccRCC datasets, including bulk-RNA sequencing and single-cell RNA sequencing data. In vitro, immunoblotting, qRT-PCR, and RNA sequencing were conducted in ccRCC cell lines upon PTEN depletion. Gene ontology and gene set enrichment analysis were performed to screen the critical pathway and molecules in response to PTEN deletion. Immunohistochemistry staining and further bioinformatic analysis were used to validate our data.

Results

Based on multi-omics analysis of public datasets of renal cancer, the frequently mutated or deleted PTEN was found to be correlated with a suppressive tumor immune microenvironment in ccRCC. Furthermore, we depleted PTEN via CRISPR-Cas9 in Caki-1 cells, which led to the upregulation of multiple neutrophil chemokines, particularly CXCL1, CXCL2, CXCL5, CXCL6, and CXCL8. The roles of neutrophil chemokines and neutrophil markers were further validated and investigated for the association with prognosis in vitro, clinical samples, and the publicly available databases. The expression of CXCL1, CXCL8, and neutrophil markers, S100A9 and BCL2A1, were significantly associated with a poor immunotherapy-related prognosis in public dataset of renal cancer patients receiving ICIs treatment.

Conclusion

These results add a new layer to understanding the association between PTEN status and the role of neutrophil infiltration in ccRCC. Moreover, our findings propose low expression of PTEN as candidate factor of resistance to anti-PD-1-based immunotherapy in ccRCC.

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma

Background. Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies. Methods. Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA). An ICS score model was generated in light of univariate Cox regression and Least absolute shrinkage and selection operator (Lasso)-Cox regression, which was independently validated in ArrayExpress database. In addition, we appraised the predictive power of the model via Kaplan-Meier (K-M) curves and time-dependent receiver operating characteristic (ROC) curves. Eventually, immune infiltration, genomic alterations and immunotherapy were analyzed between high and low ICS score groups. Results. Initially, we screened 11 ICS with prognostic impact based on 515 ccRCC patients. K-M curves presented that the high ICS score group experienced a poorer prognosis ( $P<0.001$ ). In parallel, ROC curves revealed a satisfactory reliability of model to predict individual survival at 1, 3, and 5 years, with area under the curves (AUCs) of 0.744, 0.713, and 0.742, respectively. In addition, we revealed that the high ICS score group was characterized by increased infiltration of immune cells, strengthened BAP1 mutation frequency, and enhanced expression of immune checkpoint genes. Conclusion. The ICS score model has higher predictive power for patients’ prognosis and can instruct ccRCC patients in seeking suitable treatment.

A Predictive Model Based on Pyroptosis-Related Gene Features Can Effectively Predict Clear Cell Renal Cell Carcinoma Prognosis and May Be an Underlying Target for Immunotherapy

Methods

The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed.

Results

The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group.

Conclusion

Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.