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

Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

Bridging the divide: unveiling mutual immunological pathways of cancer and pregnancy

The juxtaposition of two seemingly disparate physiological phenomena within the human body-namely, cancer and pregnancy-may offer profound insights into the intricate interplay between malignancies and the immune system. Recent investigations have unveiled striking similarities between the pivotal processes underpinning fetal implantation and successful gestation and those governing tumor initiation and progression. Notably, a confluence of features has emerged, underscoring parallels between the microenvironment of tumors and the maternal-fetal interface. These shared attributes encompass establishing vascular networks, cellular mobilization, recruitment of auxiliary tissue components to facilitate continued growth, and, most significantly, the orchestration of immune-suppressive mechanisms.Our particular focus herein centers on the phenomenon of immune suppression and its protective utility in both of these contexts. In the context of pregnancy, immune suppression assumes a paramount role in shielding the semi-allogeneic fetus from the potentially hostile immune responses of the maternal host. In stark contrast, in the milieu of cancer, this very same immunological suppression fosters the transformation of the tumor microenvironment into a sanctuary personalized for the neoplastic cells.Thus, the striking parallels between the immunosuppressive strategies deployed during pregnancy and those co-opted by malignancies offer a tantalizing reservoir of insights. These insights promise to inform novel avenues in the realm of cancer immunotherapy. By harnessing our understanding of the immunological events that detrimentally impact fetal development, a knowledge grounded in the context of conditions such as preeclampsia or miscarriage, we may uncover innovative immunotherapeutic strategies to combat cancer.

Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication

BACKGROUND

Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.

METHODS

Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.

RESULTS

The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.

CONCLUSIONS

We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

Investigating the causal associations between metabolic biomarkers and the risk of kidney cancer

Metabolic reprogramming plays an important role in kidney cancer. We aim to investigate the causal effect of 249 metabolic biomarkers on kidney cancer from population-based data. This study extracts data from previous genome wide association studies with large sample size. The primary endpoint is random-effect inverse variance weighted (IVW). After completing 249 times of two-sample Mendelian randomization analysis, those significant metabolites are included for further sensitivity analysis. According to a strict Bonferrion-corrected level (P < 2e-04), we only find two metabolites that are causally associated with renal cancer. They are lactate (OR:3.25, 95% CI: 1.84-5.76, P = 5.08e-05) and phospholipids to total lipids ratio in large LDL (low density lipoprotein) (OR: 0.63, 95% CI: 0.50-0.80, P = 1.39e-04). The results are stable through all the sensitivity analysis. The results emphasize the central role of lactate in kidney tumorigenesis and provide novel insights into possible mechanism how phospholipids could affect kidney tumorigenesis.

A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer

Introduction

Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response.

Methods

Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor.

Results

This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.

Decreased expression of TXNIP is associated with poor prognosis and immune infiltration in kidney renal clear cell carcinoma

The most prevalent and insidious type of kidney cancer is kidney clear cell carcinoma (KIRC). Thioredoxin-interacting protein (TXNIP) encodes a thioredoxin-binding protein involved in cellular energy metabolism, redox homeostasis, apoptosis induction and inflammatory responses. However, the relationship between TXNIP, immune infiltration and its prognostic value in KIRC remains unclear. Thus, the present study evaluated the potential for TXNIP as a prognostic marker in patients with KIRC. Data from The Cancer Genome Atlas were used to assess relative mRNA expression levels of TXNIP in different types of cancer. The protein expression levels of TXNIP were evaluated using the Human Protein Atlas. Enrichment analysis of genes co-expressed with TXNIP was performed to assess relevant biological processes that TXNIP may be involved in. CIBERSORT was used to predict the infiltration of 21 tumor-infiltrating immune cells (TIICs). Univariate and multivariate Cox regression analyses were used to assess the relationship between TXNIP expression and prognosis. Single-cell RNA-sequencing datasets were used to evaluate the mRNA expression levels of TXNIP in certain immune cells in KIRC. The CellMiner database was used to analyze the relationship between TXNIP mRNA expression and drug sensitivity in KIRC. The results from the present study demonstrated that TXNIP expression was significantly decreased in KIRC tissue compared with that in normal tissue, as confirmed by western blotting and reverse transcription-quantitative PCR. In addition, downregulated TXNIP expression was significantly associated with poor prognosis, a high histological grade and an advanced stage. The Cell Counting Kit-8 assay demonstrated that TXNIP overexpression significantly suppressed tumor cell proliferation. Univariate and multivariate Cox regression analyses indicated that TXNIP served as a separate prognostic factor in KIRC. Moreover, TXNIP expression was significantly correlated with the accumulation of several TIICs and its overexpression significantly downregulated the mRNA expression levels of CD25 and cytotoxic T-lymphocyte-associated protein 4, immune cell surface markers in CD4+ T lymphocytes. In conclusion, TXNIP may be used as a possible biomarker to assess unfavorable prognostic outcomes and identify immunotherapy targets in KIRC.

Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis

Objectives

The most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism.

Methods

To identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA.

Results

A sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways 'antigen processing and presentation' and 'NOD-like receptor signaling pathway'. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well.

Conclusion

Overall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Multiple databases analyzed the prognosis prediction of renin secretion pathway-related genes in renal clear cell carcinoma and immunotherapy

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant kidney tumour and its progression is associated with the renin secretion pathway, so this study aimed to develop a prognostic model based on renin secretion pathway-related genes.

Methods

First, 453 renin secretion pathway-related genes were acquired [|log fold change (FC)| >1.5, false discovery rate (FDR) <0.05] from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The data were combined and further screened for 188 genes associated with ccRCC prognosis (P<0.05) by univariate independent prognostic analysis. These genes were subjected to least absolute shrinkage and selection operator regression to identify potential prognostic genes to construct the prognostic model. The stability of the model was externally validated. Combined risk scores and clinical information were used to create nomograms to accurately reflect patient survival. The model-related genes were further mined for subsequent analysis.

Results

A prognostic model of six renin secretion pathway genes (IGFBP3, PLAUR, CHKB-CPT1B, HOXA13, CDH13, and CDC20) was developed. Its reliability in predicting disease prognosis was confirmed by survival analysis, receiver operating characteristic (ROC) curve analysis and a risk curve. The nomogram and calibration curve showed good accuracy. The immune-related analyses revealed that the low-risk group would benefit more from immunotherapy.

Conclusions

The prognostic model of ccRCC based on six renin secretion pathway-related genes can be used to guide the precise treatment of ccRCC patients.

Prognostic implication of a novel lactate score correlating with immunotherapeutic responses in pan-cancer

A thorough assessment of lactate-related genes (LRGs) in different types of human cancers is currently lacking. To elucidate the molecular landscape of LRGs, we conducted a comprehensive analysis using genomic, mRNA, and microRNA expression profiles and developed a lactate score model using the least absolute shrinkage and selection operator (LASSO) algorithm. We found that our lactate score could be a prognostic marker instead of LDHA for several cancer patients who possess high-frequency variants in LRGs. The lactate score also demonstrated an association with CD8+ T cells infiltration in multiple cancer types. Furthermore, our findings indicate that the lactate score holds promise as a potential biomarker for immunotherapy in patients with bladder cancer (BLCA) and skin cutaneous melanoma (SKCM). Among the seventeen genes of the lactate score model, PDP1 showed the strongest positive correlation with lactate score and the potential as a standalone biomarker for prognosis. In general, our study has yielded crucial insights into the potential application of the lactate score as a predictive biomarker for both survival outcomes and the response to immunotherapy. By recognizing the prognostic significance of lactate metabolism, we open avenues for further investigations aimed at harnessing the therapeutic potential of lactate.

Lipid metabolism-related gene expression in the immune microenvironment predicts prognostic outcomes in renal cell carcinoma

Background

Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases.

Methods

The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays.

Results

Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration.

Conclusion

In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.

Identification of lactate-related subgroups and prognostic model in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that exhibits elevated glycolytic capacity. Lactate, as a byproduct of glycolysis, is considered a major oncometabolite that plays an important role in oncogenesis and remodeling of the tumor microenvironment. However, the potential roles of lactate in TNBC are not yet fully understood. In this study, our goal was to identify prognosis-related lactate genes (PLGs) and construct a lactate-related prognostic model (LRPM) for TNBC.

METHODS

First, we applied lactate-related genes to classify TNBC samples using a hierarchical clustering algorithm. Then, we performed the log-rank analysis and the least absolute shrinkage and selection operator analysis to screen PLGs and construct the LRPM. The biological functions of the identified PLGs in TNBC were investigated using CCK8 assay and clone formation assay. Finally, we constructed a nomogram based on the lactate-risk score and tumor clinical stage. We used the operating characteristic curve and decision curve analysis to evaluate the predictive capability of the nomogram.

RESULTS

Our results showed that the TNBC samples could be classified into two subgroups with different survival probabilities. Three genes (NDUFAF3, CARS2 and FH), which can suppress TNBC cell proliferation, were identified as PLGs. Moreover, the LRPM and nomogram exhibited excellent predictive performance for TNBC patient prognosis.

CONCLUSION

We have developed a novel LRPM that enables risk stratification and identification of poor molecular subtypes in TNBC patients, showing great potential in clinical practice.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

Cellular specificity of lactate metabolism and a novel lactate-related gene pair index for frontline treatment in clear cell renal cell carcinoma

Background

Although lactate metabolism-related genes (LMRGs) have attracted attention for their effects on cancer immunity, little is known about their function in clear cell renal cell carcinoma (ccRCC). The aim of this study was to examine the cellular specificity of lactate metabolism and how it affected the first-line treatment outcomes in ccRCC.

Methods

GSE159115 was used to examine the features of lactate metabolism at the single-cell level. Utilizing the transcriptome, methylation profile, and genomic data from TCGA-KIRC, a multi-omics study of LMRG expression characteristics was performed. A prognostic index based on a gene-pair algorithm was created to assess how LMRGs affected patients' clinical outcomes. To simulate the relationship between the prognostic index and the frontline treatment, pRRophetic and Subclass Mapping were used. E-MTAB-1980, E-MTAB-3267, Checkmate, and Javelin-101 were used for external validation.

Results

The variable expression of some LMRGs in ccRCC can be linked to variations in DNA copy number or promoter methylation levels. Lactate metabolism was active in tumor cells and vSMCs, and LDHA, MCT1, and MCT4 were substantially expressed in tumor cells, according to single-cell analysis. The high-risk patients would benefit from immune checkpoint blockade monotherapy (ICB) and ICB plus tyrosine kinase inhibitors (TKI) therapy, whereas the low-risk individuals responded to mTOR-targeted therapy.

Conclusions

At the single-cell level, our investigation demonstrated the cellular specificity of lactate metabolism in ccRCC. We proposed that the lactate-related gene pair index might be utilized to identify frontline therapy responders in ccRCC patients as well as predict prognosis.

The signature of cuproptosis-related immune genes predicts the tumor microenvironment and prognosis of prostate adenocarcinoma

Background

Cuproptosis plays a crucial role in cancer, and different subtypes of cuproptosis have different immune profiles in prostate adenocarcinoma (PRAD). This study aimed to investigate immune genes associated with cuproptosis and develop a risk model to predict prognostic characteristics and chemotherapy/immunotherapy responses of patients with PRAD.

Methods

The CIBERSORT algorithm was used to evaluate the immune and stromal scores of patients with PRAD in The Cancer Genome Atlas (TCGA) cohort. Validation of differentially expressed genes DLAT and DLD in benign and malignant tissues by immunohistochemistry, and the immune-related genes of DLAT and DLD were further screened. Univariable Cox regression were performed to select key genes. Least absolute shrinkage and selection operator (LASSO)-Cox regression analyse was used to develop a risk model based on the selected genes. The model was validated in the TCGA, Memorial Sloan-Kettering Cancer Center (MSKCC) and Gene Expression Omnibus (GEO) datasets, as well as in this study unit cohort. The genes were examined via functional enrichment analysis, and the tumor immune features, tumor mutation features and copy number variations (CNVs) of patients with different risk scores were analysed. The response of patients to multiple chemotherapeutic/targeted drugs was assessed using the pRRophetic algorithm, and immunotherapy was inferred by the Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS).

Results

Cuproptosis-related immune risk scores (CRIRSs) were developed based on PRLR, DES and LECT2. High CRIRSs indicated poor overall survival (OS), disease-free survival (DFS) in the TCGA-PRAD, MSKCC and GEO datasets and higher T stage and Gleason scores in TCGA-PRAD. Similarly, in the sample collected by the study unit, patients with high CRIRS had higher T-stage and Gleason scores. Additionally, higher CRIRSs were negatively correlated with the abundance of activated B cells, activated CD8+ T cells and other stromal or immune cells. The expression of some immune checkpoints was negatively correlated with CRIRSs. Tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH) and copy number variation (CNV) scores were all higher in the high-CRIRS group. Multiple chemotherapeutic/targeted drugs and immunotherapy had better responsiveness in the low-CRIRS group.

Conclusion

Overall, lower CRIRS indicated better response to treatment strategies and better prognostic outcomes.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework

Background

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods

Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results

We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion

Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00327-3.

Expression and clinical significance of DOK3 in renal clear cell carcinoma

OBJECTIVES

Docking Protein 3 (DOK3) is an adapter protein that has been implicated in various cellular processes relevant to diseases, such as cancer. In this study, we aimed to evaluate the role of DOK3 in kidney renal clear cell carcinoma (KIRC) by examining how its expression levels are correlated with patient characteristics and prognosis.

METHODS

We analyzed KIRC-related data from The Cancer Genome Atlas and used several bioinformatics tools, such as LinkedOmics and Oncomine, to evaluate DOK3 mRNA expression in KIRC. DOK3 protein expression was examined in 150 clinical KIRC samples and 100 non-cancerous renal tissues with immunohistochemistry assays. The prognostic value of DOK3 mRNA expression on patient overall survival was analyzed retrospectively using Kaplan-Meier survival and Cox regression analyses.

RESULTS

DOK3 mRNA expression was notably higher in KIRC samples compared with normal tissues. Significant correlations were found between DOK3 mRNA expression levels and tumor size, lymph node metastasis, distant metastasis, and pathological grade using the bioinformatics data. This was confirmed at the protein level with immunohistochemistry data. Survival analyses indicated that elevated DOK3 expression is linked to a lower overall survival rate in KIRC patients.

CONCLUSIONS

DOK3 is a potential biomarker for determining KIRC patient clinical prognosis.

New insights into fibrotic signaling in renal cell carcinoma

Fibrotic signaling plays a pivotal role in the development and progression of solid cancers including renal cell carcinoma (RCC). Intratumoral fibrosis (ITF) and pseudo-capsule (PC) fibrosis are significantly correlated to the disease progression of renal cell carcinoma. Targeting classic fibrotic signaling processes such as TGF-β signaling and epithelial-to-mesenchymal transition (EMT) shows promising antitumor effects both preclinically and clinically. Therefore, a better understanding of the pathogenic mechanisms of fibrotic signaling in renal cell carcinoma at molecular resolution can facilitate the development of precision therapies against solid cancers. In this review, we systematically summarized the latest updates on fibrotic signaling, from clinical correlation and molecular mechanisms to its therapeutic strategies for renal cell carcinoma. Importantly, we examined the reported fibrotic signaling on the human renal cell carcinoma dataset at the transcriptome level with single-cell resolution to assess its translational potential in the clinic.

Mitochondrial metabolic reprogramming-mediated immunogenic cell death reveals immune and prognostic features of clear cell renal cell carcinoma

Background

Mitochondrial metabolic reprogramming (MMR)-mediated immunogenic cell death (ICD) is closely related to the tumor microenvironment (TME). Our purpose was to reveal the TME characteristics of clear cell renal cell carcinoma (ccRCC) by using them.

Methods

Target genes were obtained by intersecting ccRCC differentially expressed genes (DEGs, tumor VS normal) with MMR and ICD-related genes. For the risk model, univariate COX regression and K-M survival analysis were used to identify genes most associated with overall survival (OS). Differences in the TME, function, tumor mutational load (TMB), and microsatellite instability (MSI) between high and low-risk groups were subsequently compared. Using risk scores and clinical variables, a nomogram was constructed. Predictive performance was evaluated by calibration plots and receiver operating characteristics (ROC).

Results

We screened 140 DEGs, including 12 prognostic genes for the construction of risk models. We found that the immune score, immune cell infiltration abundance, and TMB and MSI scores were higher in the high-risk group. Thus, high-risk populations would benefit more from immunotherapy. We also identified the three genes (CENPA, TIMP1, and MYCN) as potential therapeutic targets, of which MYCN is a novel biomarker. Additionally, the nomogram performed well in both TCGA (1-year AUC=0.862) and E-MTAB-1980 cohorts (1-year AUC=0.909).

Conclusions

Our model and nomogram allow accurate prediction of patients' prognoses and immunotherapy responses.

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.

Chromatin regulators-related lncRNA signature predicting the prognosis of kidney renal clear cell carcinoma and its relationship with immune microenvironment: A study based on bioinformatics and experimental validation

Background: Kidney Renal Clear cell carcinoma (KIRC) is a major concern in the urinary system. A lot of researches were focused on Chromatin Regulators (CRs) in tumors. In this study, CRs-related lncRNAs (CRlncRNAs) were investigated for their potential impact on the prognosis of KIRC and the immune microenvironment.

Methods: The TCGA database was used to obtain transcriptome and related clinical information. CRs were obtained from previous studies, whereas CRlncRNAs were obtained by differential and correlation analysis. We screened the lncRNAs for the signature construction using regression analysis and LASSO regression analysis. The effectiveness of the signature was evaluated using the Kaplan-Meier (K-M) curve and Receiver Operating Characteristic curve (ROC). Additionally, we examined the associations between the signature and Tumor Microenvironment (TME), and the efficacy of drug therapy. Finally, we further verified whether these lncRNAs could affect the biological function of KIRC cells by functional experiments such as CCK8 and transwell assay.

Results: A signature consisting of 8 CRlncRNAs was constructed to predict the prognosis of KIRC. Quantitative Real-Time PCR verified the expression of 8 lncRNAs at the cell line and tissue level. The signature was found to be an independent prognostic indicator for KIRC in regression analysis. This signature was found to predict Overall Survival (OS) better for patients in the subgroups of age, gender, grade, stage, M, N0, and T. Furthermore, a significant correlation was found between riskScore and immune cell infiltration and immune checkpoint. Finally, we discovered several drugs with different IC50 values in different risk groups using drug sensitivity analysis. And functional experiments showed that Z97200.1 could affect the proliferation, migration and invasion of KIRC cells.

Conclusion: Overall, the signature comprised of these 8 lncRNAs were reliable prognostic biomarkers for KIRC. Moreover, the signature had significant potential for assessing the immunological landscape of tumors and providing individualized treatment.

Short-chain L-3-hydroxyacyl-CoA dehydrogenase: A novel vital oncogene or tumor suppressor gene in cancers

The reprogramming of cellular metabolism is frequently linked to tumorigenesis. Glucose, fatty acids, and amino acids are the specific substrates involved in how an organism maintains metabolic equilibrium. The HADH gene codes for the short-chain L-3-hydroxyacyl-CoA dehydrogenase (HADH), a crucial enzyme in fatty acid oxidation that catalyzes the third phase of fatty acid oxidation in mitochondria. Increasing data suggest that HADH is differentially expressed in various types of malignancies and is linked to cancer development and progression. The significance of HADH expression in tumors and its potential mechanisms of action in the onset and progression of certain cancers are summarized in this article. The possible roles of HADH as a target and/or biomarker for the detection and treatment of various malignancies is also described here.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses

Background

Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients.

Methods

RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the “edgeR” R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses.

Results

We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P &lt; 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs.

Conclusions

We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer

Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer

Kidney cancer is one of the most common urological cancers worldwide, and kidney renal clear cell cancer (KIRC) is the major histologic subtype. Our previous study found that von-Hippel Lindau (VHL) gene mutation, the dominant reason for sporadic KIRC and hereditary kidney cancer-VHL syndrome, could affect VHL disease-related cancers development by inducing telomere shortening. However, the prognosis role of telomere-related genes in kidney cancer has not been well discussed. In this study, we obtained the telomere-related genes (TRGs) from TelNet. We obtained the clinical information and TRGs expression status of kidney cancer patients in The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Totally 353 TRGs were differential between tumor and normal tissues in the TCGA-KIRC dataset. The total TCGA cohort was divided into discovery and validation TCGA cohorts and then using univariate cox regression, lasso regression, and multivariate cox regression method to conduct data analysis sequentially, ten TRGs (ISG15, RFC2, TRIM15, NEK6, PRKCQ, ATP1A1, ELOVL3, TUBB2B, PLCL1, NR1H3) risk model had been constructed finally. The kidney patients in the high TRGs risk group represented a worse outcome in the discovery TCGA cohort (p<0.001), and the result was validated by these four cohorts (validation TCGA cohort, total TCGA cohort, ICGC cohort, and CPTAC cohort). In addition, the TRGs risk score is an independent risk factor for kidney cancer in all these five cohorts. And the high TRGs risk group correlated with worse immune subtypes and higher tumor mutation burden in cancer tissues. In addition, the high TRGs risk group might benefit from receiving immune checkpoint inhibitors and targeted therapy agents. Moreover, the proteins NEK6, RF2, and ISG15 were upregulated in tumors both at the RNA and protein levels, while PLCL1 and PRKCQ were downregulated. The other five genes may display the contrary expression status at the RNA and protein levels. In conclusion, we have constructed a telomere-related genes risk model for predicting the outcomes of kidney cancer patients, and the model may be helpful in selecting treatment agents for kidney cancer patients.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes

Background

The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).

Methods

Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.

Results

A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).

Conclusions

We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.

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.

Comprehensive analysis of fatty acid and lactate metabolism–related genes for prognosis value, immune infiltration, and therapy in osteosarcoma patients

Osteosarcoma is the most frequent bone tumor. Notwithstanding that significant medical progress has been achieved in recent years, the 5-year overall survival of osteosarcoma patients is inferior. Regulation of fatty acids and lactate plays an essential role in cancer metabolism. Therefore, our study aimed to comprehensively assess the fatty acid and lactate metabolism pattern and construct a fatty acid and lactate metabolism–related risk score system to predict prognosis in osteosarcoma patients. Clinical data and RNA expression data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We used the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic risk score model. Relationships between the risk score model and age, gender, tumor microenvironment characteristics, and drug sensitivity were also explored by correlation analysis. We determined the expression levels of prognostic genes in osteosarcoma cells via Western blotting. We developed an unknown fatty acid and lactate metabolism–related risk score system based on three fatty acid and lactate metabolism–related genes (SLC7A7, MYC, and ACSS2). Survival analysis showed that osteosarcoma patients in the low-risk group were likely to have a better survival time than those in the high-risk group. The area under the curve (AUC) value shows that our risk score model performs well in predicting prognosis. Elevated fatty acids and lactate risk scores weaken immune function and the environment of the body, which causes osteosarcoma patients’ poor survival outcomes. In general, the constructed fatty acid and lactate metabolism–related risk score model can offer essential insights into subsequent mechanisms in available research. In addition, our study may provide rational treatment strategies for clinicians based on immune correlation analysis and drug sensitivity in the future.

Expression of lactate-related signatures correlates with immunosuppressive microenvironment and prognostic prediction in ewing sarcoma

Objectives: Ewing sarcoma (EWS) is an aggressive tumor of bone and soft tissue. Growing evidence indicated lactate as a pivotal mediator of crosstalk between tumor energy metabolism and microenvironmental regulation. However, the contribution of lactate-related genes (LRGs) in EWS is still unclear.

Methods: We obtained the transcriptional data of EWS patients from the GEO database and identified differentially expressed-LRGs (DE-LRGs) between EWS patient samples and normal tissues. Unsupervised cluster analysis was utilized to recognize lactate modulation patterns based on the expression profile of DE-LRGs. Functional enrichment including GSEA and GSVA analysis was conducted to identify molecular signaling enriched in different subtypes. ESTIMATE, MCP and CIBERSORT algorithm was used to explore tumor immune microenvironment (TIME) between subtypes with different lactate modulation patterns. Then, lactate prognostic risk signature was built via univariate, LASSO and multivariate Cox analysis. Finally, we performed qPCR analysis to validate candidate gene expression.

Result: A total of 35 DE-LRGs were identified and functional enrichment analysis indicated that these LRGs were involved in mitochondrial function. Unsupervised cluster analysis divided EWS patients into two lactate modulation patterns and we revealed that patients with Cluster 1 pattern were linked to poor prognosis and high lactate secretion status. Moreover, TIME analysis indicated that the abundance of multiple immune infiltrating cells were dramatically elevated in Cluster 1 to Cluster 2, including CAFs, endothelial cells, Macrophages M2, etc., which might contribute to immunosuppressive microenvironment. We also noticed that expression of several immune checkpoint proteins were clearly increased in Cluster 1 to Cluster 2. Subsequently, seven genes were screened to construct LRGs prognostic signature and the performance of the resulting signature was validated in the validation cohort. Furthermore, a nomogram integrating LRGs signature and clinical characteristics was developed to predict effectively the 4, 6, and 8-year prognosis of EWS patients.

Conclusion: Our study revealed the role of LRGs in immunosuppressive microenvironment and predicting prognosis in EWS and provided a robust tool to predict the prognosis of EWS patients.

The clinical significance, immune infiltration, and tumor mutational burden of angiogenesis-associated lncRNAs in kidney renal clear cell carcinoma

Background

Poor prognosis of kidney renal clear cell carcinoma (KIRC) is often related to angiogenesis. The lncRNAs that regulate angiogenesis could also affect the prognosis of KIRC. It is meaningful for us to use lncRNAs related to angiogenesis to construct a generic, individualized prognostic signature for patients with KIRC.

Methods

We identified eight angiogenesis-associated genes (AAGs) by differential expression analysis and univariate Cox regression from The Cancer Genome Atlas dataset, including 537 KIRC samples and 72 normal samples. In total, 23 prognostic lncRNAs were screened out after Pearson correlation analysis and univariate Cox regression analysis. Then, we performed least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to establish a four-AAG-related lncRNA prognostic signature.

Results

The risk score was calculated for each KIRC patients by using a four-AAG-related lncRNA prognostic signature. We divided the KIRC patients into high- and low-risk groups by the median of the risk score. It was confirmed that the AAG-related lncRNA prognostic signature has good prognostic value for KIRC patients by time-dependent receiver operating characteristic and Kaplan–Meier survival analysis. We identified 3,399 differentially expressed genes between the high- and low-risk groups and performed their functional enrichment analyses. The AAG-related lncRNA prognostic signature was an independent prognostic predictor for KIRC patients and was used to perform a combined nomogram. We reevaluated them in terms of survival, clinic characteristics, tumor-infiltrating immune cells and tumor mutation burden.

Conclusion

Our research indicates that the AAG-related lncRNA prognostic signature is a promising and potential independent prognostic indicator for KIRC patients. Then, it could offer new insights into the prognosis assessment and potential treatment strategies of KIRC patients.

Effects and Prognostic Values of Circadian Genes CSNK1E/GNA11/KLF9/THRAP3 in Kidney Renal Clear Cell Carcinoma via a Comprehensive Analysis

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent and deadly types of renal cancer in adults. Recent research has identified circadian genes as being involved in the development and progression of KIRC by altering their expression. This study aimed to identify circadian genes that are differentially expressed in KIRC and assess their role in KIRC progression. In KIRC, there were 553 differentially expressed rhythm genes (DERGs), with 300 up-regulated and 253 down-regulated DERGs. Functional enrichment analyses showed that DERGs were greatly enriched in the circadian rhythm and immune response pathways. Survival analyses indicated that higher expression levels of CSNK1E were related to shorter overall survival of KIRC patients, whereas lower expression levels of GNA11, KLF9, and THRAP3 were associated with shorter overall survival of KIRC patients. Through cell assay verification, the mRNA level of CSNK1E was significantly up-regulated, whereas the mRNA levels of GNA11, KLF9, and THRAP3 were dramatically down-regulated in KIRC cells, which were consistent with the bioinformatics analysis of KIRC patient samples. Age, grade, stage, TM classification, and CSNK1E expression were all shown to be high-risk variables, whereas GNA11, KLF9, and THRAP3 expression were found to be low-risk factors in univariate Cox analyses. Multivariate Cox analyses showed that CSNK1E and KLF9 were also independently related to overall survival. Immune infiltration analysis indicated that the proportion of immune cells varied greatly between KIRC tissues and normal tissue, whereas CSNK1E, GNA11, KLF9, and THRAP3 expression levels were substantially linked with the infiltration abundance of immune cells and immunological biomarkers. Moreover, interaction networks between CSNK1E/GNA11/KLF9/THRAP3 and immune genes were constructed to explore the stream connections. The findings could help us better understand the molecular mechanisms of KIRC progression, and CSNK1E/GNA11/KLF9/THRAP3 might be used as molecular targets for chronotherapy in KIRC patients in the near future.

Characterization of Lactate Metabolism Score in Breast and Thyroid Cancers to Assist Immunotherapy via Large-Scale Transcriptomic Data Analysis

Breast cancer (BC) and thyroid cancer (TC) have the highest rate of incidence, especially in women. Previous studies have revealed that lactate provides energetic and anabolic support to cancer cells, thus serving as an important oncometabolite with both extracellular and intracellular signaling functions. However, the correlation of lactate metabolism scores with thyroid and breast cancer immune characteristics remains to be systematically analyzed. To investigate the role of lactate at the transcriptome level and its correlation with the clinical outcome of BC and TC, transcriptome data of 1,217 patients with breast cancer (BC) and 568 patients with thyroid cancer (TC) were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets with their corresponding clinical and somatic mutation data. The lactate metabolism score was calculated based on a single-sample gene set enrichment analysis (ssGSEA). The results showed that lactate metabolism-related genes and lactate metabolism scores was significantly associated with the survival of patients with BRCA and THCA. Notably, the lactate metabolism scores were strongly correlated with human leukocyte antigen (HLA) expression, tumor-infiltrating lymphocyte (TIL) infiltration, and interferon (IFN) response in BC and TC. Furthermore, the lactate metabolism score was an independent prognostic factor and could serve as a reliable predictor of overall survival, clinical characteristics, and immune cell infiltration, with the potential to be applied in immunotherapy or precise chemotherapy of BC and TC.

Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients

Background

Glioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas.

Materials and Methods

We investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms.

Results

In total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients.

Conclusion

We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.