Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Identification of Molecular Subtypes of Clear-Cell Renal Cell Carcinoma in Patient-Derived Xenografts Using Multi-Omics

Background/Objectives: Clear-cell renal cell carcinoma (ccRCC) is a heterogenous disease that can be classified into multiple molecular subtypes with differential prognosis and sensitivities to treatments based on their genomic, transcriptomic, proteomic, and metabolic profiles. Patient-derived xenografts (PDXs) are high-fidelity cancer models because they maintain similar genotypes and immunohistologic phenotypes to the parental tumors and respond to standard-of-care therapies as expected. However, whether the molecular subtypes identified in ccRCC patient samples are preserved in PDX models is not clear. Our objective is to compare the transcriptional and proteomic profiles of our PDX models to those of ccRCC patients and identify both similarities and distinctions between molecular profiles of PDX subtypes and corresponding ccRCC patient subtypes, so that proper PDX subtypes can be used when investigating the corresponding ccRCC patient subtypes. Methods: To match PDXs to the human ccRCC molecular subtypes, we compared the transcriptomic and proteomic profiles of five ccRCC PDX models established in our lab to those of the human ccRCC molecular subtypes reported by our group, as well as other groups, using hierarchical analysis, Principal Component Analysis (PCA), and Permutation Correlation Analysis. The enrichment of key molecular pathways in PDXs and ccRCC subtypes was determined using Gene Set Enrichment Analysis. Results: We found that each PDX resembles one of the molecular subtypes closely at both transcript and protein levels. In addition, PDXs representing different molecular subtypes show unique metabolic characteristics. Moreover, molecular subtypes of PDXs correlated with ccRCC patient subtypes in key pathway activities implicated in ccRCC progression and therapy resistance. Conclusions: Our results suggest that PDX subtypes should be used when investigating the molecular mechanism of cancer progression and therapy resistance for corresponding ccRCC patient subtypes. This "matching" strategy will greatly facilitate the clinical translation of positive findings into the optimal management of ccRCC patients.

Regulatory mechanism and prognostic value of sex hormone pathways connected with metabolism and immune signaling in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) represents a major subtype of kidney cancer with variable prognosis. A comprehensive understanding of sex hormone-related pathways could potentially refine the prediction of patient outcomes in ccRCC. Patients from TCGA-KIRC (n = 528) and GSE22541 (n = 40) cohorts were analyzed. Sex-hormone-associated pathways were manually collected and calculated with the activated score, then subtypes were identified. Differential gene expression, pathway enrichment, and tumor-infiltrating immunocytes were assessed. A prognostic signature was developed using Cox analysis and LASSO regression. Immunohistochemistry (IHC) was performed to validate the protein level of key model gene in ccRCC tissues. Three distinct subtypes (C1, C2, C3) based on sex hormone pathway activation were discovered. C1 showed the most favorable prognosis (P = 0.00029). 1,094 genes were upregulated in C1 and 197 in C3. 20 risk-associated and 172 protective genes for ccRCC prognosis were identified. LASSO regression narrowed down to 33 genes for the sex-hormone-related-gene (SHAG) prognostic model. In the TCGA-KIRC cohort, the high-SHAG score group had a worse prognosis with an HR of 3.26 (95% CI: 2.334-4.555, P < 0.001). Validation in the GSE22541 cohort corroborated these findings. The nomogram incorporating the SHAG model demonstrated robust predictive accuracy higher than 0.75. IHC validation confirmed that ARHGEF17 protein levels were higher in early-stage ccRCC (stage I-II) compared to advanced-stage (stage III) tumors, supporting its prognostic relevance. The SHAG signature serves as a promising prognostic tool for ccRCC, providing insights into the role of sex hormone-related pathways in tumor progression. Further experimental and clinical validation is warranted to explore its potential in personalized therapy.

Spatially segregated APOE+ macrophages restrict immunotherapy efficacy in clear cell renal cell carcinoma

Background: Immunotherapy has revolutionized cancer treatment and holds great potential for them, including metastatic clear cell renal cell carcinoma (ccRCC). However, immune resistance remains a major obstacle, limiting its efficacy and durability. Understanding the mechanisms of immune tolerance in the tumor microenvironment (TME) is pivotal for overcoming these challenges and enhancing therapeutic outcomes. Methods: Over 2000 samples, including a real-world cohort of 230 advanced ccRCC patients treated with immune checkpoint blockade (ICB) were analyzed. Single-cell RNA sequencing data from 13 tumor regions were categorized into ICB-exposed, ICB-resistant, and ICB-responsive groups. Multiple robust algorithms and multiplex immunofluorescence were used to explore TME composition and macrophage heterogeneity. Spatial communication dynamics were further investigated. In vitro experiments were performed to evaluate the impact of SPP1 on 786-O and 769-P cells. Co-culture experiments with THP-1-derived macrophages, followed by Western blot, flow cytometry, and functional assays, were performed to investigate SPP1-mediated macrophage polarization and its impact on tumor progression. Results: The results revealed an elevated presence of Apolipoprotein E (APOE)+ macrophages in ICB-resistant ccRCC. Notably, higher APOE+ macrophage proportion indicated shorter prognosis and worse response to ICB (P < 0.001). Elevated expression of CCAAT Enhancer Binding Protein Delta (CEBPD) was markedly linked to several immunosuppressive pathways, hindering T cell recruitment, promoting exhaustion, ultimately diminishing poorer prognosis and worse ICB efficacy. Meanwhile, upregulated Secreted Phosphoprotein 1 (SPP1) significantly enhances the proliferation, clonal formation, and migration of ccRCC cells. Tumor-derived SPP1. Additionally, SPP1 signaling from malignant cells appeared to recruit APOE+ macrophages to tumor margins, and promotes macrophage polarization into APOE+ M2-like macrophages. In the vicinity of the tumor, these APOE+ macrophages shape immunosuppressive TME by releasing abundant TGF-β signals, limiting anti-tumor effector T cells activity in ICB-resistant tumors, and contributing to tumor progression. Conclusion: This study reveals the critical role of APOE+ macrophages in promoting immune suppression and resistance to ICB therapy in ccRCC. By promoting T cell exhaustion and immunosuppressive signaling, particularly via localized TGF-β, these spatially segregated macrophages undermine treatment efficacy. Targeting APOE+ macrophages, especially in conjunction with ICB, presents a promising strategy to overcome immune resistance and enhance outcomes for ccRCC patients.

Clear Cell Renal Cell Carcinoma: Characterizing the Phenotype of Von Hippel-Lindau Mutation Using MRI

BACKGROUND

The von Hippel-Lindau (VHL) mutation is an important alteration in clear cell renal cell carcinoma (ccRCC); however, its imaging phenotype remains unclear.

PURPOSE

To investigate whether MRI features can reflect the VHL mutation status.

STUDY TYPE

Retrospective.

FIELD STRENGTH/SEQUENCE

3 T/fast spin echo T2-weighted, spin-echo echo planar diffusion-weighted, gradient recalled echo T1-weighted, gradient recalled echo chemical-shift T1-weighted, and contrast-enhanced gradient recalled echo T1-weighted sequences.

POPULATION

One hundred five patients with ccRCC who underwent preoperative contrast-enhanced MRI and subsequent genomic sequencing: 59 consecutive patients from our institution (38 [64.41%] with VHL mutations) formed a training cohort, and 46 from The Cancer Genome Atlas (TCGA) database (24 [52.17%] with VHL mutations) formed an independent test cohort.

ASSESSMENT

Two radiologists, with 23 and 33 years of experience respectively, jointly evaluated the semantic MRI features of the primary lesion in ccRCCs to propose potential features related to VHL mutations in both cohorts. Three additional readers, with 5, 7, and 10 years of experience respectively, independently reviewed all lesions to assess the interobserver agreement of MRI features. A VHL mutational likelihood score (VHL-MULIS) system was constructed using the training cohort and validated using the independent test cohort.

STATISTICAL TESTS

Fisher's test or chi-square test, t-test or Mann-Whitney U test, logistic regression, Cohen's kappa (κ), area under the receiver operating characteristic curve (AUC). A two-sided P value <0.05 was considered statistically significant.

RESULTS

In both the local and public cohorts, T2-weighted signal intensity and presence of microscopic fat from primary lesions were significantly associated with VHL mutation status. The VHL-MULIS incorporated maximum diameter, T2-weighted signal intensity, and presence of microscopic fat in the training cohort and demonstrated promising diagnostic ability (AUC, 0.82; sensitivity, 0.79; specificity, 0.82) and substantial interobserver agreement (κ, 0.787) in the test cohort.

DATA CONCLUSION

The VHL mutation exhibited a distinct MRI phenotype. Integrating multiple semantic MRI features has potential to reflect the mutation status in patients with ccRCC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Targeting cGAS-STING pathway for reprogramming tumor-associated macrophages to enhance anti-tumor immunotherapy

The cyclic GMP-AMP synthase (cGAS)-stimulator interferon genes (STING) signaling pathway plays a crucial role in activating innate and specific immunity in anti-tumor immunotherapy. As the major infiltrating cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) could be polarized into either anti-tumor M1 or pro-tumor M2 types based on various stimuli. Accordingly, targeted reprogramming TAMs to restore immune balance shows promise as an effective anti-tumor strategy. In this review, we aim to target cGAS-STING pathway for reprogramming TAMs to enhance anti-tumor immunotherapy. We investigated the double-edged sword effects of cGAS-STING in regulating TME. The regulative roles of cGAS-STING pathway in TAMs and its impact on the TME were further revealed. More importantly, several strategies of targeting cGAS-STING for reprogramming TAMs were designed for enhancing anti-tumor immunotherapy. Taken together, targeting cGAS-STING pathway for reprogramming TAMs in TME might be a promising strategy to enhance anti-tumor immunotherapy.

Multiomics Analysis of Exportin Family Reveals XPO1 as a Novel Target for Clear Cell Renal Cell Carcinoma

Background: Recently, exportin gene family members have been demonstrated to play essential roles in tumor progression. However, research on the clinical significance of exportin gene family members is limited in clear cell renal cell carcinoma (ccRCC). Methods: Pan-cancer data, ccRCC multiomics data, and single-cell sequence were included to analyze the differences in DNA methylation modification, single nucleotide variations (SNVs), copy number variations (CNVs), and expression levels of exportin gene family members. Non-negative matrix factorization was used to identify molecular subtypes based on exportin gene family members, and the prognostic and biological differences of different molecular subtypes were compared across multiple dimensions. Results: Exportin gene family members were upregulated in pan-cancer expression, and their aberrant expression was significantly influenced by DNA methylation, SNV, and CNV, particularly in ccRCC. Based on the expression matrix of exportin gene family members, two molecular subtypes, exportin famliy genes (XPO)-based subtype 1 (XPS1) and exportin famliy genes (XPO)-based subtype 2 (XPS2), were identified. The expression levels of exportin gene family members in the XPS2 subtype were significantly higher than those in XPS1, and the prognosis was poorer. The XPS2 subtype had lower immune component abundance and higher immune exhaustion scores. Its response rate to immunotherapy was significantly lower than that of the XPS1 subtype, but it was more sensitive to small molecules, including mercaptopurine and nutlin. Among them, exportin-1 (XPO1) is a potential diagnostic and therapeutic target for ccRCC, which can promote renal cancer progression by activating the PI3K-AKT-mTOR (phosphatidylinositol 3-kinase (PI3K)/AKT serine/threonine kinase (AKT)/mechanistic target of rapamycin (MTOR)) and interferon alpha pathways. Conclusion: This study analyzed the variations of exportin gene family members at the pan-cancer level and identified two distinct ccRCC subtypes, which can guide personalized management of patients.

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment.

OBJECTIVES

To identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD.

METHODS

We investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD.

RESULTS

We developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model's efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze-thaw cycles (P > 0.05).

CONCLUSIONS

This study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.

LTBR acts as a novel immune checkpoint of tumor-associated macrophages for cancer immunotherapy

Tumor-associated macrophages (TAMs) greatly contribute to immune checkpoint inhibitor (ICI) resistance of cancer. However, its underlying mechanisms and whether TAMs can be promising targets to overcome ICI resistance remain to be unveiled. Through integrative analysis of immune multiomics data and single-cell RNA-seq data (iMOS) in lung adenocarcinoma (LUAD), lymphotoxin β receptor (LTBR) is identified as a potential immune checkpoint of TAMs, whose high expression, duplication, and low methylation are correlated with unfavorable prognosis. Immunofluorescence staining shows that the infiltration of LTBR+ TAMs is associated with LUAD stages, immunotherapy failure, and poor prognosis. Mechanistically, LTΒR maintains immunosuppressive activity and M2 phenotype of TAMs by noncanonical nuclear factor kappa B and Wnt/β-catenin signaling pathways. Macrophage-specific knockout of LTBR hinders tumor growth and prolongs survival time via blocking TAM immunosuppressive activity and M2 phenotype. Moreover, TAM-targeted delivery of LTΒR small interfering RNA improves the therapeutic effect of ICI via reversing TAM-mediated immunosuppression, such as boosting cytotoxic CD8+ T cells and inhibiting granulocytic myeloid-derived suppressor cells infiltration. Taken together, we bring forth an immune checkpoint discovery pipeline iMOS, identify LTBR as a novel immune checkpoint of TAMs, and propose a new immunotherapy strategy by targeting LTBR+ TAMs.

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

A Multi-Omics Prognostic Model Capturing Tumor Stemness and the Immune Microenvironment in Clear Cell Renal Cell Carcinoma

Background: Cancer stem-like cells (CSCs), a distinct subset recognized for their stem cell-like abilities, are intimately linked to the resistance to radiotherapy, metastatic behaviors, and self-renewal capacities in tumors. Despite their relevance, the definitive traits and importance of CSCs in the realm of oncology are still not fully comprehended, particularly in the context of clear cell renal cell carcinoma (ccRCC). A comprehensive understanding of these CSCs' properties in relation to stemness, and their impact on the efficacy of treatment and resistance to medication, is of paramount importance. Methods: In a meticulous research effort, we have identified new molecular categories designated as CRCS1 and CRCS2 through the application of an unsupervised clustering algorithm. The analysis of these subtypes included a comprehensive examination of the tumor immune environment, patterns of metabolic activity, progression of the disease, and its response to immunotherapy. In addition, we have delved into understanding these subtypes' distinctive clinical presentations, the landscape of their genomic alterations, and the likelihood of their response to various pharmacological interventions. Proceeding from these insights, prognostic models were developed that could potentially forecast the outcomes for patients with ccRCC, as well as inform strategies for the surveillance of recurrence after treatment and the handling of drug-resistant scenarios. Results: Compared with CRCS1, CRCS2 patients had a lower clinical stage/grading and a better prognosis. The CRCS2 subtype was in a hypoxic state and was characterized by suppression and exclusion of immune function, which was sensitive to gefitinib, erlotinib, and saracatinib. The constructed prognostic risk model performed well in both training and validation cohorts, helping to identify patients who may benefit from specific treatments or who are at risk of recurrence and drug resistance. A novel therapeutic target, SAA2, regulating neutrophil and fibroblast infiltration, and, thus promoting ccRCC progression, was identified. Conclusions: Our findings highlight the key role of CSCs in shaping the ccRCC tumor microenvironment, crucial for therapy research and clinical guidance. Recognizing tumor stemness helps to predict treatment efficacy, recurrence, and drug resistance, informing treatment strategies and enhancing ccRCC patient outcomes.

CircSP3 encodes SP3-461aa to promote ccRCC progression via stabilizing MYH9 and activating the PI3K-Akt signaling pathway

Clear cell renal cell carcinoma (ccRCC) is a primary kidney cancer with high aggressive phenotype and extremely poor prognosis. Accumulating evidence suggests that circular RNAs (circRNAs) play pivotal roles in the occurrence and development of various human cancers. However, the expression, clinical significance and regulatory role of circRNAs in ccRCC remain largely unclear. Here we report that circSP3 to be increased in tissues from ccRCC patients and ccRCC cells, and to positively correlate with ccRCC malignant features. Knockdown of circSP3 inhibits proliferation, triggers apoptosis, and reduces migration and invasion in different ccRCC cells in vitro. Correspondingly, circSP3 overexpression Promote ccRCC tumorigenicity in a mouse xenograft model. Mechanistically, circSP3 could bind with the ribosome to initiate the translation process to encodes a novel 461-amino acid peptide referred to as SP3-461aa, which protects the MYH9 protein from proteasomal degradation. SP3-461aa played a pivotal role in mediating the oncogenic effects of circSP3 by interacting with the MYH9 protein and activating the PI3K-Akt signaling pathway. These findings suggested that circSP3 plays an important role in ccRCC development and could be a potential biomarker for the treatment and prognosis of ccRCC.

DCS, a novel classifier system based on disulfidptosis reveals tumor microenvironment heterogeneity and guides frontline therapy for clear cell renal carcinoma

Background

Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown.

Methods

In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified.

Results

We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy.

Conclusion

This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Clinical features and molecular landscape of cuproptosis signature-related molecular subtype in gastric cancer

Recent studies have highlighted the biological significance of cuproptosis in disease occurrence and development. However, it remains unclear whether cuproptosis signaling also has potential impacts on tumor initiation and prognosis of gastric cancer (GC). In this study, 16 cuproptosis-related genes (CRGs) transcriptional profiles were harnessed to perform the regularized latent variable model-based clustering in GC. A cuproptosis signature risk scoring (CSRS) scheme, based on a weighted sum of principle components of the CRGs, was used to evaluate the prognosis and risk of individual tumors of GC. Four distinct cuproptosis signature-based clusters, characterized by differential expression patterns of CRGs, were identified among 1136 GC samples across three independent databases. The four clusters were also associated with different clinical outcomes and tumor immune contexture. Based on the CSRS, GC patients can be divided into CSRS-High and CSRS-Low subtypes. We found that DBT, MTF1, and ATP7A were significantly elevated in the CSRS-High subtype, while SLC31A1, GCSH, LIAS, DLAT, FDX1, DLD, and PDHA1 were increased in the CSRS-Low subtype. Patients with CSRS-Low score were characterized by prolonged survival time. Further analysis indicated that CSRS-Low score also correlated with greater tumor mutation burden (TMB) and higher mutation rates of significantly mutated genes (SMG) in GC. In addition, the CSRS-High subtype harbored more significantly amplified focal regions related to tumorigenesis (3q27.1, 12p12.1, 11q13.3, etc.) than the CSRS-Low tumors. Drug sensitivity analyses revealed the potential compounds for the treatment of gastric cancer with CSRS-High score, which were experimentally validated using GC cells. This study highlights that cuproptosis signature-based subtyping is significantly associated with different clinical features and molecular landscape of GC. Quantitative evaluation of the CSRS of individual tumors will strengthen our understanding of the occurrence and development of cuproptosis and the treatment progress of GC.

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

OBJECTIVE

To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC).

METHODS

A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models.

RESULTS

Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group.

CONCLUSION

The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

PURPOSE

The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.

METHODS

We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.

RESULTS

Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.

CONCLUSIONS

MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Integrated single-cell and bulk characterization of branched chain amino acid metabolism-related key gene BCAT1 and association with prognosis and immunogenicity of clear cell renal cell carcinoma

BACKGROUND

The relationship between clear cell renal cell carcinoma (ccRCC) and branched-chain amino acids (BCAA) metabolism has yet to be thoroughly explored.

METHODS

The BCAA metabolism-related clusters were constructed using non-negative matrix factorization (NMF). The features of BCAA metabolism in ccRCC were evaluated by building a prognostic model using least absolute shrinkage and selection operator (LASSO) regression algorithm. Real-time quantitative PCR (RT-qPCR) was employed to analyze differential expression of branched-chain amino acid transaminase 1 (BCAT1) between cancer and paracancer tissues and between different cell lines. Cell counting kit-8, wound healing and Transwell chamber assays were conducted to determine changes in proliferative and metastatic abilities of A498 and 786-O cells.

RESULTS

Two BCAA metabolism-related clusters with distinct prognostic and immune infiltration characteristics were identified in ccRCC. The BCAA metabolic signature (BMS) was capable of distinguishing immune features, tumor mutation burden, responses to immunotherapy, and drug sensitivity among ccRCC patients. RT-qPCR revealed overexpression of BCAT1 in ccRCC tissues and cell lines. Additionally, single-gene RNA sequencing analysis demonstrated significant enrichment of BCAT1 in macrophages and tumor cells. BCAT1 played tumor-promoting role in ccRCC and was closely associated with immunosuppressive cells and checkpoints. BCAT1 promoted ccRCC cell proliferation and metastasis.

CONCLUSIONS

The BMS played a crucial role in determining the prognosis, tumor mutation burden, responses to immunotherapy and drug sensitivity of ccRCC patients, as well as the immune cell infiltration features. BCAT1 was linked to immunosuppressive microenvironments and may offer new sights into ccRCC immunotherapeutic targets.