Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

IGF2BP2 inhibits invasion and migration of clear cell renal cell carcinoma via targeting Netrin-4 in an m6A-dependent manner

Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma, often leads to a poor prognosis due to metastasis. The investigation of N6-methyladenosine (m6A) methylation, a crucial RNA modification, and its role in ccRCC, particularly through the m6A reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), revealed significant insights. We found that IGF2BP2 was notably downregulated in ccRCC, which correlated with tumor aggressiveness and poor prognosis. Thus, IGFBP2 has emerged as an independent prognostic factor of ccRCC. Moreover, a strong positive correlation was observed between the expression of IGF2BP2 and Netrin-4. Netrin-4 was also downregulated in ccRCC, and its lower levels were associated with increased malignancy and poor prognosis. Overexpression of IGF2BP2 and Netrin-4 suppressed the invasion and migration of ccRCC cells, while Netrin-4 knockdown reversed these effects in ccRCC cell lines. RNA immunoprecipitation (RIP)-quantitative polymerase chain reaction validated the robust enrichment of Netrin-4 mRNA in anti-IGF2BP2 antibody immunoprecipitates. MeRlP showed significantly increased Netrin4 m6A levels after lGF2BP2 overexpression. Moreover, we found that IGF2BP2 recognized and bound to the m6A site within the coding sequence of Netrin-4, enhancing its mRNA stability. Collectively, these results showed that IGF2BP2 plays a suppressive role in the invasion and migration of ccRCC cells by targeting Netrin-4 in an m6A-dependent manner. These findings underscore the potential of IGF2BP2/Netrin-4 as a promising prognostic biomarker and therapeutic target in patients with ccRCC metastasis.

Knockdown of ADAM8 inhibits the proliferation, migration, invasion, and tumorigenesis of renal clear cell carcinoma cells to enhance the immunotherapy efficacy

The current study performed bioinformatics and in vitro and in vivo experiments to explore the effects of ADAM8 on the malignant behaviors and immunotherapeutic efficacy of renal clear cell carcinoma (ccRCC) Cells. The modular genes most associated with immune cells were screened. Then, prognostic risk models were constructed by univariate COX analysis, LASSO regression analysis and multivariate COX analysis, and their diagnostic value was determined. The correlation between tumor mutation load (TMB) scores and the prognosis of ccRCC patients was clarified. Finally, six key genes (ABI3, ADAM8, APOL3, MX2, CCDC69, and STAC3) were analyzed for immunotherapy efficacy. Human and mouse ccRCC cell lines and human proximal tubular epithelial cell lines were used for in vitro cell experiments. The effect of ADAM8 overexpression or knockdown on tumor formation and survival in ccRCC cells was examined by constructing subcutaneous transplanted tumor model. Totally, 636 Black module genes were screened as being most associated with immune cell infiltration. Six genes were subsequently confirmed for the construction of prognostic risk models, of which ABI3, APOL3 and CCDC69 were low-risk factors, while ADAM8, MX2 and STAC3 were high-risk factors. The constructed risk model based on the identified six genes could accurately predict the prognosis of ccRCC patients. Besides, TMB was significantly associated with the prognosis of ccRCC patients. Furthermore, ABI3, ADAM8, APOL3, MX2, CCDC69 and STAC3 might play important roles in treatment concerning CTLA4 inhibitors or PD-1 inhibitors or combined inhibitors. Finally, we confirmed that ADAM8 could promote the proliferation, migration and invasion of ccRCC cells through in vitro experiments, and further found that in in vivo experiments, ADAM8 knockdown could inhibit tumor formation in ccRCC cells, improve the therapeutic effect of anti-PD1, and prolong the survival of mice. Our study highlighted the alleviative role of silencing ADAM8 in ccRCC patients.

Humoral complementomics - exploration of noninvasive complement biomarkers as predictors of renal cancer progression

Despite the progress of anti-cancer treatment, the prognosis of many patients with solid tumors is still dismal. Reliable noninvasive biomarkers are needed to predict patient survival and therapy response. Here, we propose a Humoral Complementomics approach: a work-up of assays to comprehensively evaluate complement proteins, activation fragments, and autoantibodies targeting complement proteins in plasma, which we correlated with the intratumoral complement activation, and/or local production, focusing on localized and metastatic clear cell renal cell carcinoma (ccRCC). In two prospective ccRCC cohorts, plasma C2, C5, Factor D and properdin were elevated compared to healthy controls, reflecting an inflammatory phenotype that correlated with plasma calprotectin levels but did not associate with CRP or with patient prognosis. Conversely, autoantibodies against the complement C3 and the reduced form of FH (a tumor neo-epitope reported in lung cancer) correlated with a favorable outcome. Our findings pointed to a specific group of patients with elevated plasma C4d and C1s-C1INH complexes, indicating the initiation of the classical pathway, along with elevated Ba and Bb, indicating alternative pathway activation. Boostrapped Lasso regularized Cox regression revealed that the most predictive complement biomarkers were elevated plasma C4d and Bb levels at the time of surgery, which correlated with poor prognosis. In conclusion, we propose Humoral Complementomics as an unbiased approach to study the global state of the complement system in any pathological plasma sample and disease context. Its implementation for ccRCC revealed that elevated C4d and Bb in plasma are promising prognostic biomarkers, correlating with shorter progression-free survival.

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Proteomic Profiling of Advanced Melanoma Patients to Predict Therapeutic Response to Anti-PD-1 Therapy

PURPOSE

Despite high clinical need, there are no biomarkers that accurately predict the response of patients with metastatic melanoma to anti-PD-1 therapy.

EXPERIMENTAL DESIGN

In this multicenter study, we applied protein depletion and enrichment methods prior to various proteomic techniques to analyze a serum discovery cohort (n = 56) and three independent serum validation cohorts (n = 80, n = 12, n = 17). Further validation analyses by literature and survival analysis followed.

RESULTS

We identified several significantly regulated proteins as well as biological processes such as neutrophil degranulation, cell-substrate adhesion, and extracellular matrix organization. Analysis of the three independent serum validation cohorts confirmed the significant differences between responders (R) and nonresponders (NR) observed in the initial discovery cohort. In addition, literature-based validation highlighted 30 markers overlapping with previously published signatures. Survival analysis using the TCGA database showed that overexpression of 17 of the markers we identified correlated with lower overall survival in patients with melanoma.

CONCLUSIONS

Ultimately, this multilayered serum analysis led to a potential marker signature with 10 key markers significantly altered in at least two independent serum cohorts: CRP, LYVE1, SAA2, C1RL, CFHR3, LBP, LDHB, S100A8, S100A9, and SAA1, which will serve as the basis for further investigation. In addition to patient serum, we analyzed primary melanoma tumor cells from NR and found a potential marker signature with four key markers: LAMC1, PXDN, SERPINE1, and VCAN.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.Communicated by Ramaswamy H. Sarma.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer

BACKGROUND

Identifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC.

RESULTS

The IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. 'CIBERSORT' analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450.

CONCLUSIONS

This IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC.

Severe and fatal adverse events of immune checkpoint inhibitor combination therapy in patients with metastatic renal cell carcinoma: a systematic review and meta-analysis

Introduction

Immune checkpoint inhibitor (ICI) combination therapy has changed the treatment landscape for metastatic renal cell carcinoma (mRCC). However, little evidence exists on the treatment-related severe adverse events (SAEs) and fatal adverse events (FAEs) of ICI combination therapy in mRCC.

Method

We searched PubMed, Embase, and Cochrane Library databases to evaluate randomized controlled trials (RCTs) of ICI combination therapy versus conventional tyrosine kinase inhibitor (TKI)-targeted therapy in mRCC. Data on SAEs and FAEs were analyzed using revman5.4 software.

Results

Eight RCTs (n=5380) were identified. The analysis showed no differences in SAEs (60.5% vs. 64.5%) and FAEs (1.2% vs. 0.8%) between the ICI and TKI groups (odds ratio [OR], 0.83; 95%CI 0.58-1.19, p=0.300 and OR, 1.54; 95%CI 0.89-2.69, p=0.120, respectively). ICI-combination therapy was associated with less risk of hematotoxicities, including anemia (OR, 0.24, 95%CI 0.15-0.38, p<0.001), neutropenia (OR, 0.07, 95%CI 0.03-0.14, p<0.001), and thrombocytopenia (OR, 0.05, 95%CI 0.02-0.12, p<0.001), but with increased risks of hepatotoxicities (ALT increase [OR, 3.39, 95%CI 2.39-4.81, p<0.001] and AST increase [OR, 2.71, 95%CI 1.81-4.07, p<0.001]), gastrointestinal toxicities (amylase level increase [OR, 2.32, 95%CI 1.33-4.05, p=0.003] and decreased appetite [OR, 1.77, 95%CI 1.08-2.92, p=0.020]), endocrine toxicity (adrenal insufficiency [OR, 11.27, 95%CI 1.55-81.87, p=0.020]) and nephrotoxicity of proteinuria (OR, 2.21, 95%CI 1.06-4.61, p=0.030).

Conclusions

Compared with TKI, ICI combination therapy has less hematotoxicity in mRCC but more specific hepatotoxicity, gastrointestinal toxicity, endocrine toxicity, and nephrotoxicity, with a similar severe toxicity profile.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023412669.

Liquid biopsy at the frontier in renal cell carcinoma: recent analysis of techniques and clinical application

Renal cell carcinoma (RCC) is a major pathological type of kidney cancer and is one of the most common malignancies worldwide. The unremarkable symptoms of early stages, proneness to postoperative metastasis or recurrence, and low sensitivity to radiotherapy and chemotherapy pose a challenge for the diagnosis and treatment of RCC. Liquid biopsy is an emerging test that measures patient biomarkers, including circulating tumor cells, cell-free DNA/cell-free tumor DNA, cell-free RNA, exosomes, and tumor-derived metabolites and proteins. Owing to its non-invasiveness, liquid biopsy enables continuous and real-time collection of patient information for diagnosis, prognostic assessment, treatment monitoring, and response evaluation. Therefore, the selection of appropriate biomarkers for liquid biopsy is crucial for identifying high-risk patients, developing personalized therapeutic plans, and practicing precision medicine. In recent years, owing to the rapid development and iteration of extraction and analysis technologies, liquid biopsy has emerged as a low cost, high efficiency, and high accuracy clinical detection method. Here, we comprehensively review liquid biopsy components and their clinical applications over the past 5 years. Additionally, we discuss its limitations and predict its future prospects.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.

GRAMD1A Is a Biomarker of Kidney Renal Clear Cell Carcinoma and Is Associated with Immune Infiltration in the Tumour Microenvironment

Background

GRAM structural domain-containing protein 1A (GRAMD1A) is upregulated in a variety of human cancer tissues and is closely associated with tumourigenesis and progression.

Methods

Patient RNA-sequencing data and clinicopathological information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The expression of GRAMD1A in kidney cancer cell lines and KIRC patients was analysed by quantitative polymerase chain reaction (qPCR). Receiver Operator Characteristic (ROC) curves, nomograms, Kaplan-Meier analysis, forest plots, and COX analysis were used to assess the diagnostic and prognostic value of GRAMD1A in KIRC, and gene set enrichment analysis (GSEA) was used to explore its potential signalling pathways. In addition, the Sangerbox website, Kaplan-Meier plotter database, and TISIDB and TIMER databases were used to further analyse the correlation of GRAMD1A with microsatellite instability (MSI), tumour mutational burden (TMB), immune checkpoint genes, and tumour-infiltrating lymphocytes (TILs).

Results

GRAMD1A was significantly highly expressed in KIRC and associated with shorter overall survival and relapse-free survival (P < 0.05). The AUC value of the ROC curve to identify KIRC and normal renal tissues was 0.942. Forest plot and COX analysis visualized that GRAMD1A could be an independent prognostic factor in KIRC patients (P < 0.01), and nomograms to determine the overall survival (OS) of KIRC patients also showed good efficacy (C-index: 0.776). Moreover, Spearman correlation analysis showed a positive correlation between GRAMD1A and MSI, TMB (P < 0.01). On the other hand, GRAMD1A was also found to be closely associated with immune checkpoint genes. Meanwhile, patients with KIRC with high GRAMD1A expression had a relatively low hazard ratio (HR) of death when B lymphocytes, natural killer T cells, CD4+ T lymphocytes, CD8+ T lymphocytes, and macrophages were enriched in the tumour microenvironment (TME), and a greater HR of death when regulatory T lymphocytes with tumour-specific immunosuppressive effects were significantly enriched. Last, GSEA shows that GRAMD1A is closely associated with the regulation of energy metabolism in KIRC.

Conclusions

GRAMD1A is a promising diagnostic and prognostic biomarker for patients with KIRC, and its biological function correlates to some extent with immune infiltration in TME.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.