Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study

Immune-related genetic single-nucleotide polymorphisms contribute to prognosis and response to chemotherapy in patients with acute lymphoblastic leukemia

The immune system is essential for immuno-surveillance and the generation of anti-tumor immunity. However, the role of immune-related single-nucleotide polymorphisms (SNPs) in the susceptibility and progression of acute lymphoblastic leukemia (ALL) is currently unknown. Here, we selected and analyzed 28 immune-related SNPs in 201 ALL patients and 228 healthy controls. We uncovered five important SNPs related to ALL susceptibility, including in TGFB1(rs1800469), GATA3 (rs3824662), TNFA (rs1800629), PARP1 (rs1805414), and IL6R (rs2228145). PARP1 (rs1805414) and GATA3 (rs3824662) were also associated with the ALL immunophenotype. Additionally, STAT3 (rs744166) and TMPRSS2 (rs12329760) significantly contributed to the susceptibility of Philadelphia chromosome-positive (Ph+) ALL. More importantly, MAVS (rs7269320) and NF-KBIA (rs2233406) were remarkably associated with the overall survival (OS) of ALL patients. Furthermore, ITGAM (rs4597342), PTPN22 (rs2488457), STAT5B (rs6503691), and MAVS (rs7269320) were significantly associated with the progression-free survival (PFS) of ALL patients. In the training cohort, we built a prognostic classifier, which identified five features. The five selected SNPs were related to GATA3, IL-6R, ITGAM, PTPN22, and STAT1. Moreover, the five SNP-based classifiers demonstrated a higher accuracy in predicting the OS and the PFS. In addition, we found that the mRNA expression of GATA3 gene was significantly higher in ALL patients than in healthy controls. GATA3 mRNA expression were also elevated in ALL patients with CA and AA genotypes. Our findings suggest that immune-related genetic polymorphisms contribute to the prognosis and treatment of ALL and could also serve as a valuable disease predictor.

Disulfidptosis, a novel regulated cell death to predict survival and therapeutic response in kidney renal clear cell carcinoma

BACKGROUND

Metabolic regulation of cell death has become a potential therapeutic target for kidney renal clear cell carcinoma (KIRC), which is distinguished by notable heterogeneity and significant immune infiltration. Disulfidptosis, a recently identified form of cell death, has gained prominence in antitumor immunity. This research aims to investigate the correlation between disulfidptosis and prognosis of KIRC, while also exploring the possibility of predicting therapeutic response by disulfidptosis-associated genes (DAGs).

METHODS

We sourced clinical data and RNA sequence of KIRC from the Cancer Genome Atlas Database. Employing unsupervised clustering based on 23 DAGs, we further identified key differentially expressed genes (DEGs) between clusters to construct a DAG prognostic signature. A nomogram was developed and validated to predict clinical outcome of KIRC. Finally, we examined immune cell infiltration, tumor mutational burden, immunotherapy response, and sensitivity to drugs in high and low-risk groups.

RESULTS

Two distinct KIRC patient clusters were successfully stratified using the 23-DAG-related prognostic signature, comprising 11 key genes. This resulted in a robust risk model with strong predictive accuracy for overall survival. The nomogram, incorporating DAG-based risk scores, age, and pM stage, exhibited excellent predictive performance. The high-risk group displayed increased immune cell infiltration and tumor mutational burden, while the low-risk group showed heightened sensitivity to immunotherapies and targeted treatments.

CONCLUSION

This study established a robust DAG-based risk model for KIRC, highlighting its significant correlation with the immune landscape and therapeutic responses. Novel disulfidptosis-related biomarkers revealed distinct immune profiles, drug sensitivities, and immunotherapy potentials among KIRC patients.

Retinoid X receptor γ predicts the prognosis and is associated with immune infiltration in kidney renal clear cell carcinoma: a qRT-PCR, TCGA and in silico research

BACKGROUND

Kidney clear cell carcinoma (KIRC) stands as one of the most prevalent primary malignant tumors, showcasing significant heterogeneity within the urological system. However, the precise molecular mechanisms underpinning tumorigenesis in KIRC remain elusive. While Retinoid X receptor γ (RXRG) has been implicated in various diseases and human cancers, its specific role in KIRC remains undetermined. This research aimed to investigate the involvement of RXRG in KIRC pathogenesis.

METHODS

Quantitative real-time polymerase chain reaction was performed to evaluate the expression levels of RXRG in KIRC. Utilizing RNA-seq data and corresponding clinicopathological information from The Cancer Genome Atlas (TCGA) database, we embarked on an analysis to ascertain the prognostic significance of RXRG in KIRC. Furthermore, bioinformatics analyses were employed to delineate the preliminary molecular mechanisms through which RXRG operates in KIRC tumorigenesis.

RESULTS

Our findings revealed a significant downregulation of RXRG in KIRC tumor tissues compared to normal kidney tissues, as evidenced in local and TCGA cohorts. Diminished RXRG expression correlated with adverse clinicopathological characteristics, including larger tumor size, higher clinical stage, and advanced histologic grade. Cox regression analyses unveiled that reduced RXRG expression was associated with poorer overall survival (OS) and disease-free survival (DFS) rates in KIRC patients. Bioinformatics analyses indicated that the RXRG-related differentially expressed genes (DEGs) were involved in tumorigenesis and metabolism by regulating a series of signaling pathways. Using ssGSEA, we found that RXRG expression was significantly associated with NK cells and macrophages.

CONCLUSION

Our study provides new insights and evidence that RXRG is involved in the tumorigenesis of KIRC and may be a suitable target for immunotherapy in KIRC.

SurvDB: Systematic Identification of Potential Prognostic Biomarkers in 33 Cancer Types

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research.

Efficient extraction via titanium organic frameworks facilitates in-depth profiling of urinary exosome metabolite fingerprints

Urinary exosome metabolite analysis has demonstrated notable advantages in uncovering disease status, yet its potential in decoding the intricacies of clear cell renal cell carcinoma (ccRCC) remains untapped. To address this, a core-shell magnetic titanium organic framework was designed to capture urinary exosomes and assist laser desorption/ionization mass spectrometry (LDI MS) to decipher the exosomal metabolic profile of ccRCC, with high sensitivity, throughput, and speed. A total of 492 urinary exosome metabolite fingerprints (UEMFs) from 176 samples were extracted for exploring the differences between ccRCC and healthy individuals. Leveraging machine learning algorithms, the exosomal metabolic profile was disclosed, achieving accurate differentiation and prediction of ccRCC patients versus healthy individuals, with an accuracy exceeding 97.3%. Furthermore, an optimized algorithm panel comprising five key features demonstrated consistent and high diagnosing accuracy rates of over 94.0% both in the training and blind test sets for ccRCC, underscoring the remarkable effectiveness and superiority of this strategy in ccRCC detection. This study not only refines the LDI MS method for metabolite analysis in urinary exosomes but also introduces a promising technical approach for unraveling the mysteries of ccRCC.

Nomogram for predicting severe abdominal adhesions prior to definitive surgery in patients with anastomotic fistula post-small intestine resection: a cohort study

BACKGROUND

This study aimed to develop and validate a nomogram for predicting the presence of severe intra-abdominal adhesions before definitive surgery (DS) for anastomotic fistula following small intestine resection (SIR).

METHODS

Patients were enrolled from January 2009 to October 2023 and were randomly divided (2:1) into development and validation cohorts. Predictors of severe adhesion were identified and integrated into a nomogram. The nomogram's performance was evaluated through calibration, discrimination, and clinical utility. Results : A total of 414 patients were included, with 276 in the development cohort and 138 in the validation cohort. Severe adhesion was diagnosed in 54 (13%) patients, including 37 (13.4%) in the development cohort and 17 (12.3%) in the validation cohort ( P = 0.76). Five predictors were identified: Sequential Organ Failure Assessment score, duration of early-stage abdominal infection, preoperative albumin (Alb) <35 g/L, visceral to subcutaneous fat area ratio, and preoperative C-reactive protein >10 mg/L. The nomogram demonstrated robust discrimination, with a concordance index (C-index) of 0.80 (95% CI, 0.76-0.90) in internal validation, and was well-calibrated. In the validation cohort, the model maintained good discrimination (C-index = 0.79; 95% CI, 0.67-0.94) and calibration. Decision curve analysis affirmed the nomogram's clinical utility.

CONCLUSION

This study introduces a practical nomogram for assessing the risk of severe abdominal adhesion prior to DS in patients undergoing surgery for anastomotic fistula after SIR.

Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review

The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.

Prognostic significance of immune evasion-related genes in clear cell renal cell carcinoma immunotherapy

Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignancy of the urinary system. Despite the integration of immune checkpoint inhibitors (ICIs) into the treatment paradigm for advanced RCC, resistance to immunotherapy has emerged as a pivotal determinant impacting the clinical outlook of ccRCC. Accumulating evidence underscores the pivotal role of immune evasion-related genes and pathways in enabling tumor escape from host immune surveillance, consequently influencing patients' responsiveness to immunotherapy. Nonetheless, the clinical relevance of immune evasion-related genes in ccRCC patients undergoing immunotherapy remains inadequately understood. In this study, we aggregated RNA sequencing and clinical data from ccRCC patients across three cohorts: the Cancer Genome Atlas (TCGA), CheckMate cohorts, and the JAVELIN Renal 101 trial. Leveraging a curated immune evasion-related gene set from Lawson et al., we employed the LASSO algorithm and Cox regression analysis to identify eight genes (LPAR6, RGS5, NFYC, PCDH17, CENPW, CNOT8, FOXO3, SNRPB) significantly associated with immune therapy prognosis (HR, 3.57; 95 % CI, 2.38-5.35; P<0.001). A predictive algorithm developed utilizing these genes exhibited notable accuracy in forecasting patients' progression-free survival in the training set (AUC, 0.835). Furthermore, stratification of patients by risk score revealed discernible differences in immunotherapy response and tumor microenvironment. In summary, we present a prognostic model intricately linked with immune status and treatment response. For ccRCC patients undergoing immunotherapy, this approach holds promise in aiding clinical decision-making by providing more precise and tailored treatment recommendations.

TDERS, an exosome RNA-derived signature predicts prognosis and immunotherapeutic response in clear cell renal cell cancer: a multicohort study

Background

Tumor-derived exosomes are involved in tumor progression and immune invasion and might function as promising noninvasive approaches for clinical management. However, there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma (ccRCC).

Methods

The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC patients were correspondingly deregulated in ccRCC tissues. We adopted a two-step strategy, including Lasso and bootstrapping, to construct a novel risk stratification system termed the TDERS (Tumor-Derived Exosome-Related Risk Score). During the testing and validation phases, we leveraged multiple external datasets containing over 2000 RCC cases from eight cohorts and one inhouse cohort to evaluate the accuracy of the TDERS. In addition, enrichment analysis, immune infiltration signatures, mutation landscape and therapy sensitivity between the high and low TDERS groups were compared. Finally, the impact of TDERS on the tumor microenvironment (TME) was also analysed in our single-cell datasets.

Results

TDERS consisted of 12 mRNAs deregulated in both exosomes and tissues from patients with ccRCC. TDERS achieved satisfactory performance in both prognosis and immune checkpoint inhibitor (ICI) response across all ccRCC cohorts and other pathological types, since the average area under the curve (AUC) to predict 5-year overall survival (OS) was larger than 0.8 across the four cohorts. Patients in the TDERS high group were resistant to ICIs, while mercaptopurine might function as a promising agent for those patients. Patients with a high TDERS were characterized by coagulation and hypoxia, which induced hampered tumor antigen presentation and relative resistance to ICIs. In addition, single cells from 12 advanced samples validated this phenomenon since the interaction between dendritic cells and macrophages was limited. Finally, PLOD2, which is highly expressed in fibro- and epi‑tissue, could be a potential therapeutic target for ccRCC patients since inhibiting PLOD2 altered the malignant phenotype of ccRCC in vitro.

Conclusion

As a novel, non-invasive, and repeatable monitoring tool, the TDERS could work as a robust risk stratification system for patients with ccRCC and precisely inform treatment decisions about ICI therapy.

Prognostic models for predicting oncological outcomes after surgical resection of a nonmetastatic renal cancer: A critical review of current literature

Prognostic models can be valuable for clinicians in counseling and monitoring patients after the surgical resection of nonmetastatic renal cell carcinoma (nmRCC). Over the years, several risk prediction models have been developed, evolving significantly in their ability to predict recurrence and overall survival following surgery. This review comprehensively evaluates and critically appraises current prognostic models for nm-RCC after nephrectomy. The last 2 decades have witnessed a notable increase in the development of various prognostic risk models for RCC, incorporating clinical, pathological, genomic, and molecular factors, primarily using retrospective data. Only a limited number of these models have been developed using prospective data, and their performance has been less effective than expected when applied to broader, real-life patient populations. Recently, artificial intelligence (AI), especially machine learning and deep learning algorithms, has emerged as a significant tool in creating survival prediction models. However, their widespread application remains constrained due to limited external validation, a lack of cost-effectiveness analysis, and unconfirmed clinical utility. Although numerous models that integrate clinical, pathological, and molecular data have been proposed for nm-RCC risk stratification, none have conclusively demonstrated practical effectiveness. As a result, current guidelines do not endorse a specific model. The ongoing development and validation of AI algorithms in RCC risk prediction are crucial areas for future research.

Construction and evaluation of a polygenic hazard score for prognostic assessment in localized gastric cancer

To investigate whether genetic variants may provide additional prognostic value to improve the existing clinical staging system for gastric cancer (GC), we performed two genome-wide association studies (GWASs) of GC survival in the Jiangsu (N = 1049) and Shanghai (N = 1405) cohorts. By using a TCGA dataset, we validated genetic markers identified from a meta-analysis of these two Chinese cohorts to determine GC survival-associated loci. Then, we constructed a weighted polygenic hazard score (PHS) and developed a nomogram in combination with clinical variables. We also evaluated prognostic accuracy with the time-dependent receiver operating characteristic (ROC) curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We identified a single nucleotide polymorphism (SNP) of rs1618332 at 15q15.1 that was associated with the survival of GC patients with a P value of 4.12 × 10-8, and we also found additional 25 SNPs having consistent associations among these two Chinese cohort and TCGA cohort. The PHS derived from these 26 SNPs (PHS-26) was an independent prognostic factor for GC survival (all P < 0.001). The 5-year AUC of PHS-26 was 0.68, 0.66 and 0.67 for Jiangsu, Shanghai and their pooled cohorts, respectively, which increased to 0.80, 0.82 and 0.81, correspondingly, after being integrated into a nomogram together with variables of the clinical model. The PHS-26 could improve the NRIs by 16.20%, 4.90% and 8.70%, respectively, and the IDIs by 11.90%, 8.00% and 9.70%, respectively. The 26-SNP based PHS could substantially improve the accuracy of prognostic assessment and might facilitate precision medicine for GC patients.

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma

BACKGROUND

Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown.

METHODS

Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration.

RESULTS

It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments.

CONCLUSIONS

Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied.

METHODS

We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis.

CONCLUSION

We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

Integration of pathologic characteristics, genetic risk and lifestyle exposure for colorectal cancer survival assessment

The development of an effective survival prediction tool is key for reducing colorectal cancer mortality. Here, we apply a three-stage study to devise a polygenic prognostic score (PPS) for stratifying colorectal cancer overall survival. Leveraging two cohorts of 3703 patients, we first perform a genome-wide survival association analysis to develop eight candidate PPSs. Further using an independent cohort with 470 patients, we identify the 287 variants-derived PPS (i.e., PPS287) achieving an optimal prediction performance [hazard ratio (HR) per SD = 1.99, P = 1.76 × 10-8], accompanied by additional tests in two external cohorts, with HRs per SD of 1.90 (P = 3.21 × 10-14; 543 patients) and 1.80 (P = 1.11 × 10-9; 713 patients). Notably, the detrimental impact of pathologic characteristics and genetic risk could be attenuated by a healthy lifestyle, yielding a 7.62% improvement in the 5-year overall survival rate. Therefore, our findings demonstrate the integrated contribution of pathologic characteristics, germline variants, and lifestyle exposure to the prognosis of colorectal cancer patients.

Identification of novel molecular subtypes to improve the classification framework of nasopharyngeal carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) treatment is largely based on a 'one-drug-fits-all' strategy in patients with similar pathological characteristics. However, given its biological heterogeneity, patients at the same clinical stage or similar therapies exhibit significant clinical differences. Thus, novel molecular subgroups based on these characteristics may better therapeutic outcomes.

METHODS

Herein, 192 treatment-naïve NPC samples with corresponding clinicopathological information were obtained from Fujian Cancer Hospital between January 2015 and January 2018. The gene expression profiles of the samples were obtained by RNA sequencing. Molecular subtypes were identified by consensus clustering. External NPC cohorts were used as the validation sets.

RESULTS

Patients with NPC were classified into immune, metabolic, and proliferative molecular subtypes with distinct clinical features. Additionally, this classification was repeatable and predictable as validated by the external NPC cohorts. Metabolomics has shown that arachidonic acid metabolites were associated with NPC malignancy. We also identified several key genes in each subtype using a weighted correlation network analysis. Furthermore, a prognostic risk model based on these key genes was developed and was significantly associated with disease-free survival (hazard ratio, 1.11; 95% CI, 1.07-1.16; P < 0.0001), which was further validated by an external NPC cohort (hazard ratio, 7.71; 95% CI, 1.39-42.73; P < 0.0001). Moreover, the 1-, 3-, and 5-year areas under the curve were 0.84 (95% CI, 0.74-0.94), 0.81 (95% CI, 0.73-0.89), and 0.82 (95% CI, 0.73-0.90), respectively, demonstrating a high predictive value.

CONCLUSIONS

Overall, we defined a novel classification of nasopharyngeal carcinoma (immune, metabolism, and proliferation subtypes). Among these subtypes, metabolism and proliferation subtypes were associated with advanced stage and poor prognosis of NPC patients, whereas the immune subtype was linked to early stage and favorable prognosis.

Predicting recurrence and survival in patients with non-metastatic renal-cell carcinoma after nephrectomy: a prospective population-based study with multicenter validation

BACKGROUND

Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones.

METHODS

In this prospective analysis and external validation study, the development set included 11  128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell's C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation.

RESULTS

Of the 11  128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7-7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773-0.806] for PFS and 0.793 [95% CI 0.773-0.811] for OS) and in the external validation set (0.773 [0.731-0.816] and 0.723 [0.731-0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all P <0.0001) for PFS and from 0.680 to 0.744 (all P <0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC.

CONCLUSIONS

Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival.

Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapy

Background

Disulfidptosis, an emerging type of programmed cell death, plays a pivotal role in various cancer types, notably impacting the progression of kidney renal clear cell carcinoma (KIRC) through the tumor microenvironment (TME). However, the specific involvement of disulfidptosis within the TME remains elusive.

Methods

Analyzing 41,784 single cells obtained from seven samples of KIRC through single-cell RNA sequencing (scRNA-seq), this study employed nonnegative matrix factorization (NMF) to assess 24 disulfidptosis regulators. Pseudotime analysis, intercellular communication mapping, determination of transcription factor activities (TFs), and metabolic profiling of the TME subgroup in KIRC were conducted using Monocle, CellChat, SCENIC, and scMetabolism. Additionally, public cohorts were utilized to predict prognosis and immune responses within the TME subgroup of KIRC.

Results

Through NMF clustering and differential expression marker genes, fibroblasts, macrophages, monocytes, T cells, and B cells were categorized into four to six distinct subgroups. Furthermore, this investigation revealed the correlation between disulfidptosis regulatory factors and the biological traits, as well as the pseudotime trajectories of TME subgroups. Notably, disulfidptosis-mediated TME subgroups (DSTN+CD4T-C1 and FLNA+CD4T-C2) demonstrated significant prognostic value and immune responses in patients with KIRC. Multiple immunohistochemistry (mIHC) assays identified marker expression within both cell clusters. Moreover, CellChat analysis unveiled diverse and extensive interactions between disulfidptosis-mediated TME subgroups and tumor epithelial cells, highlighting the TNFSF12-TNFRSF12A ligand-receptor pair as mediators between DSTN+CD4T-C1, FLNA+CD4T-C2, and epithelial cells.

Conclusion

Our study sheds light on the role of disulfidptosis-mediated intercellular communication in regulating the biological characteristics of the TME. These findings offer valuable insights for patients with KIRC, potentially guiding personalized immunotherapy approaches.

Molecular subtyping and characterization of clear cell renal cell carcinoma by tumor differentiation trajectories

Previous bulk RNA sequencing or whole genome sequencing on clear cell renal cell carcinoma (ccRCC) subtyping mainly focused on ccRCC cell origin or the complex tumor microenvironment (TME). Based on the single-cell RNA sequencing (scRNA-seq) data of 11 primary ccRCC specimens, cancer stem-cell-like subsets could be differentiated into five trajectories, whereby we further classified ccRCC cells into three groups with diverse molecular features. These three ccRCC subgroups showed significantly different outcomes and potential targets to tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs). Tumor cells in three differentiation directions exhibited distinct interactions with other subsets in the ccRCC niches. The subtyping model was examined through immunohistochemistry staining in our ccRCC cohort and validated the same classification effect as the public patients. All these findings help gain a deeper understanding about the pathogenesis of ccRCC and provide useful clues for optimizing therapeutic schemes based on the molecular subtype analysis.

Brusatol enhances MEF2A expression to inhibit RCC progression through the Wnt signalling pathway in renal cell carcinoma

Renal cell carcinoma (RCC) is the most aggressive subtype of kidney tumour with a poor prognosis and an increasing incidence rate worldwide. Brusatol, an essential active ingredient derived from Brucea javanica, exhibits potent antitumour properties. Our study aims to explore a novel treatment strategy for RCC patients. We predicted 37 molecular targets of brusatol based on the structure of brusatol, and MEF2A (Myocyte Enhancer Factor 2A) was selected as our object through bioinformatic analyses. We employed various experimental techniques, including RT-PCR, western blot, CCK8, colony formation, immunofluorescence, wound healing, flow cytometry, Transwell assays and xenograft mouse models, to investigate the impact of MEF2A on RCC. MEF2A expression was found to be reduced in patients with RCC, indicating a close correlation with MEF2A deubiquitylation. Additionally, the protective effects of brusatol on MEF2A were observed. The overexpression of MEF2A inhibits RCC cell proliferation, invasion and migration. In xenograft mice, MEF2A overexpression in RCC cells led to reduced tumour size compared to the control group. The underlying mechanism involves the inhibition of RCC cell proliferation, invasion, migration and epithelial-mesenchymal transition (EMT) through the modulation of Wnt/β-catenin signalling. Altogether, we found that MEF2A overexpression inhibits RCC progression by Wnt/β-catenin signalling, providing novel insight into diagnosis, treatment and prognosis for RCC patients.

Predictive value of single-nucleotide polymorphism signature for nephrolithiasis recurrence: a 5-year prospective study

Background

Genetic variations are linked to kidney stone formation. However, the association of single nucleotide polymorphism (SNPs) and stone recurrence has not been well studied. This study aims to identify genetic variants associated with kidney stone recurrences and to construct a predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones.

Methods

We genotyped 49 SNPs in 1001 patients who received surgical stone removal between Jan 1 and Dec 31 of 2012. All patients were confirmed stone-free by CT scan and then received follow-up at least 5 years. SNP associations with stone recurrence were analyzed by Cox proportion hazard model. A predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones was developed by use of LASSO Cox regression.

Results

The recurrence rate at 3, 5, 7 years were 46.8%, 71.2%, and 78.4%, respectively. 5 SNPs were identified that had association with kidney stone recurrence risk. We used computer-generated random numbers to assign 500 of these patients to the training cohort and 501 patients to the validation cohort. A nomogram that combined the 14-SNPs-based classifier with the clinical risk factors was constructed. The areas under the curve (AUCs) at 3, 5 and 7 years of this nomogram was 0.645, 0.723, and 0.75 in training cohort, and was 0.631, 0.708, and 0.727 in validation cohort, respectively. Results show that the nomogram presented a higher predictive accuracy than those of the SNP classifier or clinical factors alone.

Conclusion

SNPs are significantly associated with kidney stone recurrence and should add prognostic value to the traditional clinical risk factors used to assess the kidney stone recurrence. A nomogram using clinical and genetic variables to predict kidney stone recurrence has revealed its potential in the future as an assessment tool during the follow-up of kidney stone patients.

Smoking enhanced the expression of c-kit in chromophobe renal cell carcinoma

INTRODUCTION

Smoking is an important risk factor for inducing renal cell carcinoma (RCC), but its specific mechanism affecting the development of RCC remains to be elucidated. Chromophobe RCC (ChRCC) is a subtype of RCC. Many studies have shown smoking is closely associated with RCC occurrence and c-kit plays a critical role in the progression of RCC, however, few studies focus on ChRCC. This study investigated the molecular mechanism between smoking and the c-kit pathway in ChRCC.

METHODS

Differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) in ChRCC and the expression of KIT in ChRCC was analyzed through the TCGA database combined with Gene Expression Omnibus (GEO) and oncomine databases. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and Protein Protein Interaction (PPI) network analysis were performed to explore the function of KIT and correlated DEGs as well as its co-expression genes in ChRCC. Finally, ChRCC patient samples were used to verify the effect of smoking on the c-kit expression.

RESULTS

The results showed that KIT is one of the DEGs and plays a vital role in ChRCC tumorigenesis. Interestingly, the expression of c-kit in cancer tissues of 27 smoking patients was significantly higher than that of 25 non-smoking patients (p<0.05), which suggests smoking might enhance the expression of c-kit in ChRCC patients.

CONCLUSIONS

Our results demonstrate that smoking might play a pivotal role in the ChRCC tumorigenesis via a pathway related to c-kit, and provided new insight into the relationship between smoking and the c-kit pathway in ChRCC.

A reactive oxygen species-related signature to predict prognosis and aid immunotherapy in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant disease containing tumor-infiltrating lymphocytes. Reactive oxygen species (ROS) are present in the tumor microenvironment and are strongly associated with cancer development. Nevertheless, the role of ROS-related genes in ccRCC remains unclear.

Methods

We describe the expression patterns of ROS-related genes in ccRCC from The Cancer Genome Atlas and their alterations in genetics and transcription. An ROS-related gene signature was constructed and verified in three datasets and immunohistochemical staining (IHC) analysis. The immune characteristics of the two risk groups divided by the signature were clarified. The sensitivity to immunotherapy and targeted therapy was investigated.

Results

Our signature was constructed on the basis of glutamate-cysteine ligase modifier subunit (GCLM), interaction protein for cytohesin exchange factors 1 (ICEF1), methionine sulfoxide reductase A (MsrA), and strawberry notch homolog 2 (SBNO2) genes. More importantly, protein expression levels of GCLM, MsrA, and SBNO2 were detected by IHC in our own ccRCC samples. The high-risk group of patients with ccRCC suffered lower overall survival rates. As an independent predictor of prognosis, our signature exhibited a strong association with clinicopathological features. An accurate nomogram for improving the clinical applicability of our signature was constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the signature was closely related to immune response, immune activation, and immune pathways. The comprehensive results revealed that the high-risk group was associated with high infiltration of regulatory T cells and CD8+ T cells and more benefited from targeted therapy. In addition, immunotherapy had better therapeutic effects in the high-risk group.

Conclusion

Our signature paved the way for assessing prognosis and developing more effective strategies of immunotherapy and targeted therapy in ccRCC.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

BACKGROUND

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence.

METHODS

In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI).

FINDINGS

The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001).

INTERPRETATION

Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy.

FUNDING

National Natural Science Foundation of China, and National Key Research and Development Program of China.

The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

Background

Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.

Methods

This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.

Results

Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusions

The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

A novel risk model of three SUMOylation genes based on RNA expression for potential prognosis and treatment sensitivity prediction in kidney cancer

Introduction: Kidney cancer is one of the most common and lethal urological malignancies. Discovering a biomarker that can predict prognosis and potential drug treatment sensitivity is necessary for managing patients with kidney cancer. SUMOylation is a type of posttranslational modification that could impact many tumor-related pathways through the mediation of SUMOylation substrates. In addition, enzymes that participate in the process of SUMOylation can also influence tumorigenesis and development. Methods: We analyzed the clinical and molecular data which were obtanied from three databases, The Cancer Genome Atlas (TCGA), the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC), and ArrayExpress. Results: Through analysis of differentially expressed RNA based on the total TCGA-KIRC cohort, it was found that 29 SUMOylation genes were abnormally expressed, of which 17 genes were upregulated and 12 genes were downregulated in kidney cancer tissues. A SUMOylation risk model was built based on the discovery TCGA cohort and then validated successfully in the validation TCGA cohort, total TCGA cohort, CPTAC cohort, and E-TMAB-1980 cohort. Furthermore, the SUMOylation risk score was analyzed as an independent risk factor in all five cohorts, and a nomogram was constructed. Tumor tissues in different SUMOylation risk groups showed different immune statuses and varying sensitivity to the targeted drug treatment. Discussion: In conclusion, we examined the RNA expression status of SUMOylation genes in kidney cancer tissues and developed and validated a prognostic model for predicting kidney cancer outcomes using three databases and five cohorts. Furthermore, the SUMOylation model can serve as a biomarker for selecting appropriate therapeutic drugs for kidney cancer patients based on their RNA expression.

Hot topics in renal cancer pathology: implications for clinical management

INTRODUCTION

The updated European Association of Urology (EAU) Guidelines issued a weak recommendation for adjuvant pembrolizumab for patients with high-risk operable clear cell Renal Cell Carcinoma (ccRCC). High risk of recurrence was defined, as per protocol-criteria, as T2 with nuclear grade 4 or sarcomatoid differentiation, T3 or higher, regional lymph node metastasis, or stage M1 with no evidence of disease. Considering the heterogeneous population included in the recommendation, it has been questioned if adjuvant pembrolizumab may lead to overtreatment of some patients as well as undertreatment of patients with worse prognosis.

AREAS COVERED

In this review, we discuss the issues related to the assessment of pathological features required to identify those patients harboring a high-risk tumor, highlighting the issue related to interobserver variability and discuss the currently available prognostic scoring systems in ccRCC.

EXPERT OPINION

PPathologist assessment of prognostic features suffers from interobserver variability which may depend on gross sampling and the pathologist's expertise. The presence of clear cell feature is not sufficient criteria by itself to define ccRCC since clear cell can be also found in other histotypes. Application of molecular biomarkers may be useful tools in the near future to help clinicians identify patients harboring tumors with worse prognosis.

Renal Cell Carcinoma as a Metabolic Disease: An Update on Main Pathways, Potential Biomarkers, and Therapeutic Targets

Clear cell renal cell carcinoma (ccRCC) is the most frequent histological kidney cancer subtype. Over the last decade, significant progress has been made in identifying the genetic and metabolic alterations driving ccRCC development. In particular, an integrated approach using transcriptomics, metabolomics, and lipidomics has led to a better understanding of ccRCC as a metabolic disease. The metabolic profiling of this cancer could help define and predict its behavior in terms of aggressiveness, prognosis, and therapeutic responsiveness, and would be an innovative strategy for choosing the optimal therapy for a specific patient. This review article describes the current state-of-the-art in research on ccRCC metabolic pathways and potential therapeutic applications. In addition, the clinical implication of pharmacometabolomic intervention is analyzed, which represents a new field for novel stage-related and patient-tailored strategies according to the specific susceptibility to new classes of drugs.

TLR3 serves as a novel diagnostic and prognostic biomarker and is closely correlated with immune microenvironment in three types of cancer

Background: Toll-like receptor 3 (TLR3) plays an important role in both innate and adaptive immunity, but the prognostic value of TLR3 in heterogeneous tumors and the correlations between TLR3 expression and immune infiltration of heterogeneous tumors remain unclear. Methods: We investigated the expression of TLR3 in a variety of tumors and focused on the diagnostic and prognostic values of TLR3 in kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD) and brain lower grade glioma (LGG) by GEPIA, DriverDBv3, UALCAN, TIMER, LinkedOmics, STRING, GeneMANIA and FunRich, as well as the possible mechanisms of TLR3 affecting tumor prognosis were discussed. Additionally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to validate TLR3 expression in early KIRC. We also compared the expression of TLR3 in the plasma of early KIRC patients and normal controls by enzyme linked immunosorbent assay (ELISA). Results: TLR3 expression was significantly different in multiple tumors compared with paracancerous nontumor tissues. Elevated expression of TLR3 contributed to the prolonged survival outcome in KIRC patients. Suppressed expression of TLR3 contributed to the prolonged survival outcome in LGG and PAAD patients. Moreover, TLR3 was significantly elevated in stage1, grade1 and N0 of KIRC. The expression and function of TLR3 in KIRC, LGG and PAAD were closely related to tumor immune microenvironment. TRAF6 was a key gene in the interactions between TLR3 and its interacting genes. Finally, the results of RT-qPCR and ELISA indicated that TLR3 expression levels were significantly raised in renal tissue and plasma of early KIRC patients. Conclusion: TLR3 has the potential to be a diagnostic biomarker of KIRC, LGG and PAAD as well as a biomarker for evaluating the prognosis of KIRC, LGG and PAAD, particularly for the early diagnosis of KIRC. TLR3 affects tumors mainly by acting on the immune microenvironment of KIRC, LGG and PAAD. These findings could lead to new insights into the immunotherapeutic targets for KIRC, LGG, and PAAD.

Risk models for recurrence and survival after kidney cancer: a systematic review

OBJECTIVE

To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent.

MATERIALS AND METHODS

We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models.

RESULTS

Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival.

CONCLUSION

Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most lethal malignant tumors with a propensity for poor prognosis and difficult treatment. Endoplasmic reticulum (ER) stress served as a pivotal role in the progression of the tumor. However, the implications of ER stress on the clinical outcome and immune features of KIRC patients still need elucidation.

Methods: We identified differentially expressed ER stress-related genes between KIRC specimens and normal specimens with TCGA dataset. Then, we explored the biological function and genetic mutation of ER stress-related differentially expressed genes (DEGs) by multiple bioinformatics analysis. Subsequently, LASSO analysis and univariate Cox regression analysis were applied to construct a novel prognostic model based on ER stress-related DEGs. Next, we confirmed the predictive performance of this model with the GEO dataset and explored the potential biological functions by functional enrichment analysis. Finally, KIRC patients stratified by the prognostic model were assessed for tumor microenvironment (TME), immune infiltration, and immune checkpoints through single-sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE analysis.

Results: We constructed a novel prognostic model, including eight ER stress-related DEGs, which could stratify two risk groups in KIRC. The prognostic model and a model-based nomogram could accurately predict the prognosis of KIRC patients. Functional enrichment analysis indicated several biological functions related to the progression of KIRC. The high-risk group showed higher levels of tumor infiltration by immune cells and higher immune scores.

Conclusion: In this study, we constructed a novel prognostic model based on eight ER stress-related genes for KIRC patients, which would help predict the prognosis of KIRC and provide a new orientation to further research studies on personalized immunotherapy in KIRC.

Early Diagnosis of Bipolar Disorder Coming Soon: Application of an Oxidative Stress Injury Biomarker (BIOS) Model

Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%-77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%-75.6%) and external validation (AUC 65.7%, 63.9%-67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.

Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

Background

Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management. There are limited biomarkers to predict the metastatic probability of renal cancer, especially in the early-stage subgroup. Here, our study applied robust machine-learning algorithms to identify metastatic and recurrence-related signatures across multiple renal cancer cohorts, which reached high accuracy in both training and testing cohorts.

Methods

Clear cell renal cell carcinoma (ccRCC) patients with primary or metastatic site sequencing information from eight cohorts, including one out-house cohort, were enrolled in this study. Three robust machine-learning algorithms were applied to identify metastatic signatures. Then, two distinct metastatic-related subtypes were identified and verified; matrix remodeling associated 5 (MXRA5), as a promising diagnostic and therapeutic target, was investigated in vivo and in vitro.

Results

We identified five stable metastasis-related signatures (renin, integrin subunit beta-like 1, MXRA5, mesenchyme homeobox 2, and anoctamin 3) from multicenter cohorts. Additionally, we verified the specificity and sensibility of these signatures in external and out-house cohorts, which displayed a satisfactory consistency. According to these metastatic signatures, patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1 (MTCS1) and type 2 (MTCS2). MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1. In addition, MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1. The MTCS2 subgroup was more sensitive to saracatinib, sunitinib, and several molecular targeted drugs. In addition, MTCS2 displayed a higher genome mutation burden and instability. Furthermore, we constructed a prognosis model based on subtype biomarkers, which performed well in training and validation cohorts. Finally, MXRA5, as a promising biomarker, significantly suppressed malignant ability, including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.

Conclusions

This study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses, multiomics landscapes, and treatment options. The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.

Identification of Survival Risk and Immune-Related Characteristics of Kidney Renal Clear Cell Carcinoma

Background

Immunity exerts momentous functions in the progression and treatment of kidney renal clear cell carcinoma (KIRC). A better understanding of the relationship between KIRC and immunity may make a great contribution to evaluating the prognosis and immune-related therapeutic response of KIRC.

Methods

A series of information such as RNA sequence, clinical data, and tumor mutation burden (TMB) of KIRC patients were downloaded through The Cancer Genome Atlas (TCGA). Next, combining the survival information and gene expression data of TCGA and Gene Expression Omnibus (GEO), we established an immune gene-related prognosis model (IGRPM) and analyzed it. Then we constructed a nomogram which was convenient for clinicians to judge the prognosis of KIRC. Last but not the least, the expressions of some genes used to construct IGRPM in early KIRC, and adjacent normal tissues were verified through real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Perl (strawberry-perl-5.30.0.1-64bit), R software (4.0.3), and GraphPad Prism 7 were used to process the relevant data.

Results

The single-sample gene set enrichment analysis (ssGSEA) showed that there were significant differences in StromalScore, ImmuneScore, ESTIMATEScore, TumorPurity, 22 kinds of human immune cells infiltration, and HLA genes expression between high immunity group (Immunity_H) and low immunity group (Immunity_L). The Immunity_H expressed more immune-related genes and enriched more immune-related functions than the Immunity_L. In addition, compared with the low-risk group, the high-risk group had worse survival outcome and higher TMB. Combining IGRPM-based risk characteristic and TMB, we found that low-TMB + low-risk was the most beneficial to the survival outcome of KIRC patients. The risk characteristic based on IGRPM could be used as an independent prognostic factor for KIRC, and the nomogram constructed for evaluating the prognosis of KIRC showed excellent predictive potential. The RT-qPCR results suggested that not all the genes used to construct IGRPM showed differential expression in early KIRC compared with adjacent normal tissues, but all these genes had significant influence on the prognosis of KIRC.

Conclusion

These comprehensive immune assessments and survival predictions, integrating multiple aspects of data and clinical information, can provide additional value to the current Tumor Node Metastasis staging system for risk stratification of KIRC and may facilitate the development of KIRC immunotherapy.

Construction of a Lactate-Related Prognostic Signature for Predicting Prognosis, Tumor Microenvironment, and Immune Response in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent primary malignancies with high heterogeneity in the urological system. Growing evidence implies that lactate is a significant carbon source for cell metabolism and plays a vital role in tumor development, maintenance, and therapeutic response. However, the global influence of lactate-related genes (LRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response has not been comprehensively elucidated in patients with KIRC. In the present study, we collected RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts. Unsupervised clustering of 17 differentially expressed LRG profiles divided the samples into three clusters with distinct immune characteristics. Three genes (FBP1, HADH, and TYMP) were then identified to construct a lactate-related prognostic signature (LRPS) using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The novel signature exhibited excellent robustness and predictive ability for the overall survival of patients. In addition, the constructed nomogram based on the LRPS-based risk scores and clinical factors (age, gender, tumor grade, and stage) showed a robust predictive performance. Furthermore, patients classified by risk scores had distinguishable immune status, tumor mutation burden, response to immunotherapy, and sensitivity to drugs. In conclusion, we developed an LRPS for KIRC that was closely related to the immune landscape and therapeutic response. This LRPS may guide clinicians to make more precise and personalized treatment decisions for KIRC patients.

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data

Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.

Single-cell transcriptomics reveals a low CD8+ T cell infiltrating state mediated by fibroblasts in recurrent renal cell carcinoma

Purpose

Recurrent renal cell carcinoma(reRCC) is associated with poor prognosis and the underlying mechanism is not yet clear. A comprehensive understanding of tumor microenvironment (TME) of reRCC may aid in designing effective anticancer therapies, including immunotherapies. Single-cell transcriptomics holds great promise for investigating the TME, however, this technique has not been used in reRCC. Here, we aimed to explore the difference in the TME and gene expression pattern between primary RCC (pRCC) and reRCC at single-cell level.

Experimental design

We performed single-cell RNA sequencing analyses of 32,073 cells from 2 pRCC, 2 reRCC, and 3 adjacent normal kidney samples. 41 pairs of pRCC and reRCC samples were collected as a validation cohort to assess differences observed in single-cell sequencing. The prognostic significance of related cells and markers were studied in 47 RCC patients underwent immunotherapy. The function of related cells and markers were validated via in vitro and in vivo experiments.

Results

reRCC had reduced CD8⁺ T cells but increased cancer-associated fibroblasts (CAFs) infiltration compared with pRCC. Reduced CD8⁺ T cells and increased CAFs infiltration were significantly associated with a worse response from immunotherapy. Remarkably, CAFs showed substantial expression of LGALS1 (Gal1). In vitro, CAFs could induce CD8⁺ T cells apoptosis via Gal1. In vivo, knockdown of Gal1 in CAFs suppressed tumor growth, increased CD8⁺ T cells infiltration, reduced the proportion of apoptotic CD8⁺ T cells and enhanced the efficacy of immunotherapy.

Conclusions

We delineated the heterogeneity of reRCC and highlighted an innovative mechanism that CAFs acted as a suppressor of CD8⁺ T cells via Gal1. Targeting Gal1 combined with anti-PD1 showed promising efficacy in treating RCC.

Construction of a Nomogram Based on lncRNA and Patient's Clinical Characteristics to Improve the Prognosis of Non-Small Cell Lung Cancer

Although the American Joint Commission on Cancer (AJCC) staging has been widely used to predict the survival of cancer patients, there are still some limitations. The high accuracy of lncRNA-based signature prediction has attracted widespread attention. The data were obtained from the RNA sequencing data of nonsmall cell lung cancer (NSCLC) in the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) were identified. Using univariate Cox proportional hazard regression (CPHR) analysis, least absolute shrinkage and selection operator method, and multivariate CPHR, 5 lncRNAs (LINC00460, LINC00857, LINC01116, RP11-253E3.3, and RP11-359E19.2) related to patient survival were successfully screened. Combined with age, gender, AJCC staging, and 5 lncRNAs, a nomogram with a better prognosis prediction ability than traditional parameters was constructed. Prognostic accuracy was evaluated using the receiver operating characteristic (ROC) curve and area under the ROC value. In addition, through co-expression analysis, we found that 5 lncRNA target genes have 34 DEMs. Gene ontology function analysis showed that these DEMs were mainly enriched in enzyme inhibitor activity and other aspects. Finally, these DEMs were found to be involved in the formation of the tumor immune microenvironment. In short, the nomogram based on 5 lncRNAs can effectively predict the overall survival rate of NSCLC and may guide the formulation of treatment plans for NSCLC.

Circular RNA circSNX6 promotes sunitinib resistance in renal cell carcinoma through the miR-1184/GPCPD1/ lysophosphatidic acid axis

Sunitinib resistance is a major challenge in systemic therapy for renal cell carcinoma (RCC). The role of circular RNAs (circRNAs) in regulating sunitinib resistance of RCC is largely unknown. We established sunitinib-resistant RCC cell lines in vivo. Through RNA-sequencing, we identified circSNX6, whose expression is upregulated in sunitinib-resistant cells compared with their parental cells. High circSNX6 expression was correlated with sunitinib resistance and worse oncologic outcomes in a cohort of 81 RCC patients. In vitro and in vivo experiments confirmed that circSNX6 could promote sunitinib resistance in RCC. circSNX6 acts as a molecular "sponge" to relieve the suppressive effect of microRNA (miR)-1184 on its target gene, glycerophosphocholine phosphodiesterase 1 (GPCPD1), which increases intracellular lysophosphatidic acid (LPA) levels and, ultimately, promotes sunitinib resistance in RCC cells. Our findings demonstrated that the circSNX6/miR-1184/GPCPD1 axis had a critical role in regulation of intracellular LPA levels and sunitinib resistance in RCC; they also provide a novel prognostic indicator and promising therapeutic targets.

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

PURPOSE

This study aimed to develop a novel analytic approach based on 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/computed tomography ([¹⁸F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL).

METHODS

We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer-Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared.

RESULTS

The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P < 0.05), while no significant difference was found between TMTV- and MBV-based hybrid nomograms (P > 0.05). The Hosmer-Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI.

CONCLUSION

The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL.

TRIAL REGISTRATION

NCT04317313. Registered March 16, 2020. Public site: https://clinicaltrials.gov/ct2/show/NCT04317313.

A MUCINs expression signature impacts overall survival in patients with clear cell renal cell carcinoma

Background

Kidney cancer, especially clear cell renal cell carcinoma (ccRCC), is one of the most common cancers in the urinary system. Previous studies suggested that certain members of MUCINs could serve as independent predictors for the survival of ccRCC patients. None of them, however, is robust enough to predict prognosis accurately.

Objective

To analyze the correlation of MUCINs alterations and their expression levels with the prognosis of ccRCC patients and develop a prognosis‐related predictor.

Methods

We applied whole‐exome sequencing in samples from 22 Chinese ccRCC patients to identify genetic alterations in MUCIN genes and analyzed their genetic alterations, expression, and correlation with survival using the TCGA, GSE73731, and GSE29069 datasets.

Result

Genetic alternations in MUCINs were identified in 91% and 51% of ccRCC patients in our cohort and the TCGA database, respectively. No correlation with survival was found for the genetic alterations. Using unsupervised clustering analysis of gene expression, we identified two major clusters of MUCIN expression patterns. Cluster 1 was characterized by a global overexpression of MUC1, MUC12, MUC13, MUC16, and OVGP1; and cluster 2 was characterized by a global overexpression of MUC4, MUC5B, MUC6, MUC20, EMCN, and MCAM. Patients with cluster 1 expression pattern had significantly shorter overall survival time and worse clinical features, including higher tumor grades and metastasis. Meanwhile, they had a higher level of mutation counts and more infiltrated immune cells, but lower enrichment in angiogenesis signature genes. A five‐MUCINs expression signature was constructed from cluster 1, and notably, it was demonstrated to be associated with shorter overall survival. A similar worse clinical feature, lower angiogenesis but the more immune signature, was identified in samples presented with signature 1. In the validation data set GSE29069, patients with signature 1 were also associated with a trend of poor survival outcomes.

Conclusion

We established a five‐MUCINs expression signature as a new prognostic marker for ccRCC. The distinct tumor microenvironment feature between the two signatures may further affect ccRCC patients’ clinical management.

CYP2J2 Is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Cytochrome P450 family 2 subfamily J member 2 (CYP2J2), a member of the monooxygenase cytochrome P450 (CYP) family and the only member of the human CYP2J subfamily, has many functions, including regulation of oxidative stress, inflammation, apoptosis, and immune responses. However, its role in cancer development has not been clearly elucidated. In this study, expression levels of CYP2J2 in various cancer types were determined using the Oncomine, the Gene Expression Profiling Interactive Analysis (GEIPA), DriverDBv3, UALCAN, and Tumor Immune Estimation Resource (TIMER) databases. The prognostic value of CYP2J2 for KIRC was analyzed using GEPIA, UALCAN, OSkirc, and DriverDBv3 databases. We evaluated the expression levels of CYP2J2 transcript, protein, and promoter methylation at different clinical characteristics in KIRC through the UALCAN database. Simultaneously, CYP2J2 network-related functions were evaluated using the GeneMANIA interactive tool while the biological processes involved in CYP2J2 and its interactive genes were investigated through Metascape and FunRich. Then, we used TIMER to determine the correlation between CYP2J2 expression levels and immune infiltration levels in KIRC. In KIRC, the CYP2J2 gene, RNA, and protein were found to be overexpressed. However, the methylation level of CYP2J2 promoter in KIRC was lower than in normal tissues. Surprisingly, elevated expression levels of CYP2J2 exhibited better prognostic outcomes in KIRC. Evaluation of protein-protein interaction networks and biological processes revealed that CYP2J2 was principally involved in immune responses, apoptosis, and other metabolic processes. Moreover, we found that the expression levels of CYP2J2 were positively correlated with infiltration levels of B cells, CD8 + T cells, neutrophils, and dendritic cells in KIRC. Therefore, we speculated that the overexpression of CYP2J2 prolonged the survival outcome of KIRC patients, which may be related to the change of tumor immune microenvironment. Moreover, all these new understandings of CYP2J2 may provide important value for the early diagnosis and new targeted drug therapy of KIRC.

N6-Methyladenosine Modification of LncRNA DUXAP9 Promotes Renal Cancer Cells Proliferation and Motility by Activating the PI3K/AKT Signaling Pathway

Most localized human renal clear cell carcinoma (ccRCC)-related deaths result from cancer recurrence and metastasis. However, the precise molecular mechanisms largely remain unknown. In recent years, an increasing number of long noncoding RNAs (lncRNAs) have been shown to be vital regulators of tumorigenesis. In this study, we characterized a lncRNA DUXAP9 and the upregulation of DUXAP9 was analyzed by quantitative real-time PCR in 112 pairs of localized ccRCC tumor tissues compared with adjacent normal tissues. Kaplan–Meier curves showed that patients of localized ccRCC with high DUXAP9 expression had poorer overall survival (P<0.01) and progression-free survival (P<0.05) than cases with low DUXAP9 expression. Multivariate Cox regression analysis also showed that high DUXAP9 expression was an independent risk factor for poor prognosis in localized ccRCC (p<0.05). DUXAP9 knockdown in renal cancer cells inhibited renal cancer cells proliferation and motility capacities in vitro and reversed epithelial–mesenchymal transition (EMT), whereas overexpression of DUXAP9 promoted renal cancer cells proliferation and motility capacities in vitro and induced EMT. Pull-down, RNA immunoprecipitation and RNA stability assays (involving actinomycin D) showed that DUXAP9 was methylated at N6-adenosine and binds to IGF2BP2, which increases its stability. DUXAP9 activate PI3K/AKT pathway and Snail expression in renal cancer cells. DUXAP9 may be useful as a prognostic marker and/or therapeutic target in localized ccRCC.

Establishment and experimental validation of an immune miRNA signature for assessing prognosis and immune landscape of patients with colorectal cancer

As essential regulators of gene expression, miRNAs are engaged in the initiation and progression of colorectal cancer (CRC), including antitumour immune response. In this study, we proposed an integrated algorithm, ImmuMiRNA, for identifying miRNA modulators of immune‐associated pathways. Based on these immune‐associated miRNAs, we applied the LASSO algorithm to develop a reliable and individualized signature for evaluating overall survival (OS) and inflammatory landscape of CRC patients. An external public data set and qRT‐PCR data from 40 samples were further utilized to validate this signature. As a result, an immune‐associated miRNA prognostic signature (IAMIPS) consisting of three miRNAs (miR‐194‐3P, miR‐216a‐5p and miR‐3677‐3p) was established and validated. Patients in the high‐risk group possessed worse OS. After stratification for clinical factors, the signature remained a powerful independent predictor for OS. IAMIPS displayed much better accuracy than the traditional clinical stage in assessing the prognosis of CRC. Further analysis revealed that patients in the high‐risk group were characterized by inflammatory response, abundance immune cell infiltration, and higher immune checkpoint profiles and tumour mutation burden (TMB). In conclusion, the IAMIPS is highly predictive of OS in patients with CRC, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.

Association of IDH mutation and 1p19q co-deletion with tumor immune microenvironment in lower-grade glioma

Although the successful clinical trials of immunotherapy show promising strategies for many cancers, its application in glioma has lagged in comparison with the progress seen in other cancers. Both isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions are critical molecular alterations affecting therapeutic response in lower-grade glioma (LGG). The systematic and comprehensive characterization of the immunological phenotypes with different molecular subtypes is key to improving our understanding and application of immunotherapies in LGG. Here, we collected the RNA-sequencing, somatic mutation, and clinical data from 1,052 patients from The Cancer Genome Atlas and Chinese Glioma Genome Atlas and stratified patients into three genetic subgroups: IDH mutations with 1p/19q codeletions (IDH mut-codel), IDH mutations without 1p/19q codeletions (IDH mut-noncodel), and IDH wild-type. Our evaluations revealed that IDH mutations and 1p/19q codeletions were associated with distinct immunological tumor microenvironments in LGG. In addition, immune cell infiltration, the expression of immune checkpoint and human leukocyte antigen (HLA) gene, and the activity of immune signaling pathways shared gradual increase from IDH mut-codel to IDH wild-type. We further constructed and validated an immune-related prognostic signature that presented high value in predicting the overall survival time in LGG. In conclusion, our study may provide valuable information for immunotherapy strategies in LGG patients.

A composite single-nucleotide polymorphism prediction signature for extranodal natural killer/T-cell lymphoma

Current prognostic scoring systems based on clinicopathologic variables are inadequate in predicting the survival and treatment response of extranodal natural killer/T-cell lymphoma (ENKTL) patients undergoing non-anthracyline-based treatment. We aimed to construct a classifier based on single-nucleotide polymorphisms (SNPs) for improving predictive accuracy and guiding clinical decision-making. The data of 722 patients with ENKTL from international multicenters were analyzed. A 7-SNP-based classifier was constructed using LASSO Cox regression in the training cohort (n=336) and further validated in the internal testing (n=144) and two external validation cohorts (n=142; n=100). The 7-SNP-based classifier showed good prognostic predictive efficacy in the training cohort and the three validation cohorts. Patients with high and low risk scores calculated by the classifier exhibited significantly different progression-free survival (PFS) and overall survival (OS) (all p<0.001). The 7-SNP-based classifier was further proved to be an independent prognostic factor by multivariate analysis, and its predictive accuracy was significantly better than clinicopathological risk variables. The application of the 7-SNP-based classifier was not affected by sample types. Notably, chemotherapy combined with radiotherapy significnalty improved PFS and OS versus radiotherapy alone in high risk Ann Anbor stage I patients, while there was no statistical difference between the two therapeutic modalities among low risk patients. A nomogram was constructed comprised of the classifier and clinicopathological variables, and showed remarkably better predictive accuracy than that of each variable alone. The 7-SNP-based classifier is a complement to existing risk stratification systems in ENKTL, which could have significant implications for clinical decision-making for ENKTL patients.

Targeting a positive regulatory loop in the tumor-macrophage interaction impairs the progression of clear cell renal cell carcinoma

Although the interaction between tumors and tumor-associated macrophages (TAMs) has been reported to facilitate the targeted drug resistance and progression of clear cell renal cell carcinoma (ccRCC), the related mechanisms remain unknown. Here, we report that SOX17 serves as a novel tumor suppressor in ccRCC and a positive regulatory loop, SOX17low/YAP/TEAD1/CCL5/CCR5/STAT3, facilitates the ccRCC-TAM interaction. SOX17 expression was commonly downregulated and negatively correlated with TAM infiltration in ccRCC specimens, and the integration of SOX17 and TAMs with the existing clinical indicators TNM stage or SSIGN score achieved better accuracy for predicting the prognosis of ccRCC patients. Mechanistically, SOX17 knockdown activated YAP signaling by promoting the transcription and nuclear distribution of YAP, which recruited TEAD1 to trigger CCL5 transcription. Then, CCL5 educated macrophages toward TAMs, which reciprocally enhanced ccRCC progression through CCL5/CCR5 and activated STAT3/SOX17low/YAP. However, SOX17 overexpression in ccRCC achieved the opposite effect. Thus, a positive regulatory loop, SOX17low/YAP/TEAD1/CCL5/CCR5/STAT3, was identified in the ccRCC-TAM interaction. Furthermore, targeting tumor-TAM interactions by blocking this positive regulatory network impaired the metastasis and targeted drug resistance of ccRCC in in vivo mouse models of lung metastasis and orthotopic ccRCC. These findings provide a new mechanism underlying the tumor-TAM interplay in ccRCC progression and present a potential target for inhibiting targeted drug resistance and metastasis in advanced ccRCC.