Neutrophil-Related Genes Predict Prognosis and Contribute to Immunosuppression in Acute Myeloid Leukemia

Pyroptosis-related genes (PRGs) have been reported to be associated with prognosis of lung adenocarcinoma (LUAD). Until now, the relationship of PRGs to the prognosis of LUAD patients and its underlying mechanisms have been poorly elucidated. Using The Cancer Genome Atlas (TCGA) LUAD cohort, a prior bioinformatics analysis constructed a prognostic signature incorporating 5 PRGs (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for predicting prognosis of LUAD patients. However, it has not been validated by the Gene Expression Omnibus (GEO) LUAD cohort yet. We implemented a modified bioinformatics analysis to, respectively, construct one prognostic signature with the TCGA cohort and with the GEO cohort and attempted to perform cross-validations by the GEO cohort and the TCGA cohort alternately in turn. Univariate and multivariate Cox regression analysis screened PRGs and constructed 2 prognostic signatures with the TCGA and GEO cohorts. All LUAD samples were classified into high- and low-risk groups according to the median risk score that was generated by regression formula. Kaplan-Meier survival analysis compared the overall survival rate between the 2 risk groups, and receiver operating characteristic curve analysis evaluated predictive performance of the 2 signatures. Additionally, risk score, combined with clinicopathological features, was subjected to multivariate Cox regression analysis, to evaluate independent prognostic value of the 2 signatures. Finally, the 2 signatures received cross-validations by the GEO and TCGA cohorts, alternately. The TCGA cohort yielded a 3-gene signature (PYCARD, NLRP1, and NLRC4), whereas the GEO cohort built a 7-gene signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) for predicting the prognosis of LUAD patients. Multivariate analysis proved independent prognostic value of risk score in the TCGA cohort (hazard ratio, = 1.939,; P = 8.43 × 10−4) and the GEO cohort (hazard ratio, = 2.291,; P = 4.34 × 10−9). Cross-validations confirmed prognostic value for the 7-gene signature from the GEO cohort by the TCGA cohort but not for the 3-gene signature from the TCGA cohort by the GEO cohort. We develop and validate a 7-gene prognostic signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) with independent prognostic value for patients with LUAD.

BackgroundFerroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined.MethodsFRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens.ResultsThirty-two significantly different FRGs were identified from TCGA–BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan–Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA.ConclusionHerein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

BackgroundThe study aimed to establish a prognostic survival model with 8 pyroptosis-and-cuproptosis-related genes to examine the prognostic effect in patients of hepatocellular carcinoma (HCC).MethodsWe downloaded gene expression data and clinical information of HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The clustering analysis and cox regression with LASSO were used for constructing an 8 PCmRNAs survival model. Using TCGA, ICGC and GEO cohort, the overall survival (OS) between high- and low- risk group was determined. We also evaluated independent prognostic indicators using univariate and multivariate analyses. The relatively bioinformatics analysis, including immune cell infiltration, function enrichment and drug sensitivity analyses, was performed as well. The gene expression of 8 PCmRNAs in vitro were validated in several HCC cell lines by qRT-PCR and Western blot. The relationship between GZMA and Fludarabine were further checked by CCK-8 assay.ResultsThe survival prognostic model was constructed with ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA using data from TCGA cohort. The ICGC and GEO cohort were used for model validation. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Risk scores had the highest predictable value for survival among Stage, Age, Gender and Grade. Most Immune cells and immune functions were decreased in high-risk group. Besides, function enrichment analyses showed that steroid metabolic process, hormone metabolic process, collagen − containing extracellular matrix, oxidoreductase activity and pyruvate metabolism were enriched. Potential drugs targeted different PCDEGs like Nelarabine, Dexamethasone and Fludarabine were found as well. ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NOD1 were upregulated while NLRP6 and GZMA were downregulated in most HCC cell lines. The potential therapy of Fludarabine was demonstrated when GZMA was low expressed in Huh7 cell line.ConclusionWe constructed a novel 8-gene (ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA) prognostic model and explored potential functional information and microenvironment of HCC, which might be worthy of clinical application. In addition, several potential chemotherapy drugs were screened and Fludarabine might be effective for HCC patients whose GZMA was low expressed.

BackgroundLactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).MethodsA gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.ResultsIn total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.ConclusionThe newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with poor prognosis. Increasing evidence has revealed that immune cells and checkpoints in the tumor microenvironment (TME) and aging are associated with the prognosis of HCC. However, the association between aging and the tumor immune microenvironment (TIME) in HCC is still unclear.MethodsRNA expression profiles and clinical data concerning HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on differentially expressed aging-related genes (DEAGs), unsupervised clustering was used to identify a novel molecular subtype in HCC. The features of immune cell infiltration and checkpoints were further explored through CIBERSORTx. Enrichment analysis and both univariate and multivariate Cox analyses were conducted to construct a 3-gene model for predicting prognosis and chemosensitivity. Finally, the mRNA and protein expression levels of the 3 genes were verified in HCC and other cancers through database searches and experiments.ResultsEleven differentially expressed AGs (GHR, APOC3, FOXM1, PON1, TOP2A, FEN1, HELLS, BUB1B, PPARGC1A, PRKDC, and H2AFX) correlated with the prognosis of HCC were used to divide HCC into two subtypes in which the prognosis was different. In cluster 2, which had a poorer prognosis, the infiltration of naive B cells and monocytes was lower in the TCGA and GEO cohorts, while the infiltration of M0 macrophages was higher. In addition, the TCGA cohort indicated that the microenvironment of cluster 2 had more immunosuppression through immune checkpoints. Enrichment analysis suggested that the MYC and E2F targets were positively associated with cluster 2 in the TCGA and GEO cohorts. Additionally, 3 genes (HMGCS2, SLC22A1, and G6PD) were screened to construct the prognostic model through univariate/multivariate Cox analysis. Then, the model was validated through the TCGA validation set and GEO dataset (GSE54236). Cox analysis indicated that the risk score was an independent prognostic factor and that patients in the high-risk group were sensitive to multiple targeted drugs (sorafenib, gemcitabine, rapamycin, etc.). Finally, significantly differential expression of the 3 genes was detected across cancers.ConclusionWe systematically described the immune differences in the TME between the molecular subtypes based on AGs and constructed a novel three-gene signature to predict prognosis and chemosensitivity in patients with HCC.

Objectives Most researches have shown that neutrophils are closely related to bladder urothelial carcinoma (BLCA), especially its occurrence and development. Although tumor microenvironment (TME) related genes have an impact on prognosis, the role of neutrophil related genes in BLCA adjuvant therapy is not clear. Methods We used sample information from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. And we utilized the CIBERSORT algorithm to obtain the tumor immune microenvironment (TIME) landscape and weighted gene co-expression network analysis (WCGNA) to detect neutrophil-related gene modules. We used univariate Cox regression, multivariate Cox regression, and lasso regression analyses to identify genes that have a relationship with BLCA prognosis. Using the median risk score (RS), we classified the cohort into a high-risk group (HRG) and low-risk group (LRG). External validation of RS was performed via GEO data feeds. Prognostic nomograms were constructed with reference to RS and clinically relevant information and validated using calibration curves. We analyzed the latent connections between RS and tumor mutational burden. Finally, the latent associations between risk markers and chemotherapy prognosis were explored using the pRRophetic algorithm. Results In this study, 10 TME-related genes with important prognostic value were screened. Then, by deriving the RS, we constructed a prognostic risk prediction nomogram using parameters such as sex, age, TNM stage, clinical stage, and RS. The area under the receiver operating characteristic curve showed that the predictive accuracy of the constructed nomogram was high. We found that using immunotherapy with new immune checkpoint inhibitors (ICIs) was more beneficial for patients in the LRG. In addition, we can learn from the chemotherapeutic drug model that patients with LRG are more sensitive to cisplatin and imatinib. Conclusions In short, the prognostic prediction model based on neutrophil-related genes will help to predict the prognosis and guide the precise treatment of BLCA.

Immune-related genetic prognostic index to predict prognosis and immune checkpoint inhibitor therapy efficiency in acute myeloid leukemia.

Head and neck squamous cell carcinoma (HNSCC) is a malignancy of epithelial origin and with poor prognosis. Exploring the biomarkers and prognostic models that can contribute to early tumor detection is meaningful. A comprehensive analysis was conducted according to the stage-related signature genes of HNSCC, and a prognostic model was developed to validate their ability to predict the prognosis. The transcriptome profiles and clinical information of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. mRNA expressions of differentially expressed genes (DEGs) were analyzed in stage I-II patients and stage III-IV patients from TCGA by R packages. A protein-protein interaction (PPI) network and core-gene network map were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to examine pathway enrichment. Kaplan-Meier, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were applied to establish a stage-associated signature model. A Spearman analysis was conducted to examine the correlations between the characteristic genes and immune cell infiltration. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. Univariate multivariate Cox regression analyses were used to assess whether the risk score was an independent prognostic indicator for HNSCC. In TCGA cohort, 5 genes (i.e., BRINP1, IL17A, ALB, FOXA2, and ZCCHC12) in the constructed prognostic risk model were associated with prognosis. Patients in the low-risk group had a better prognosis outcome than those in the high-risk group. The predictive power was good because all the area under the curve (AUC) of the risk score was higher than 0.6. Risk score [hazard ratio (HR) =1.985; P<0.001] was an independent risk factor for the prognosis of HNSCC. The results in the GEO cohort were consistent with those in the TCGA cohort. We constructed and verified a prognostic risk model of stage-related signature genes for HNSCC based on the GEO and TCGA data. Due to the good predictive accuracy of this model, the prognosis of and the tumor immune cell infiltration with patients can be estimated.

Integrative Analysis of Lincrna Expression and Clinical Annotations Reveals a Signature of 17 Genes with Prognostic Significance in Acute Myeloid Leukemia (AML)

Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26–2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20–3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.

e21051 Background: Tumor-infiltrating B cells accompanying several widely used B cell-related biomarkers, such as CD19 and CD20, have inconsistent clinical prognostic values in non-small-cell lung cancer (NSCLC) patients. Considering only one B cell-related biomarker could not distinguish the anti-tumor and pro-tumor B cell subsets in tumor, we aimed to construct a more accurate B-cells related gene pairs (BRGPs) prognostic model and evaluate its potential predictive ability to immunotherapy in NSCLC patients. Methods: Using public single-cell RNA sequencing data, the B cells-related genes (BRGs) in NSCLC samples were identified. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were utilized to construct the BRGPs model which was not affected by the technical bias of different platforms. With no dependence upon specific gene expression levels, a novel signature based on BRGPs was established in this study. In addition, the prognostic value and immunotherapeutic response for this signature with regard to the TME components and potential molecular mechanism were explored. Results: Based on the TCGA database, we built a novel prognostic signature of 23 BRGPs comprising 28 unique BRGs. This risk model showed significant power in distinguishing good or poor prognosis and could serve as an independent prognostic factor for NSCLC patients in the TCGA cohort. The prognostic accuracy of the model was further verified in the GSE31210 dataset from the GEO database. Likewise, the prognostic value of the risk score for the BRGPs model was demonstrated in the GEO cohorts by the univariate and multivariate cox regression analysis. In addition, the risk model was significantly associated with sex, TNM stage, immune score, tumor purity and various tumor-infiltrating immune cells. GSEA analysis indicated that low-risk group enriched with several immune-related pathways, such as activation of immune response, antigen receptor mediated signaling pathway, B cell activation and B cell mediated immunity, whereas several proliferation-related pathways, such as nuclear chromosome segregation, sister chromatid segregation and mitotic sister chromatid segregation were most enriched in high-risk group. Besides, the tumor mutational burden (TMB) score rather than CD274(PD-L1 mRNA) expression was positively correlated with the risk score (P＜0.001; P = 0.94, respectively). NSCLC patients with high-risk exhibited significantly higher TMB score compared with low-risk patients (P &lt; 0.001). Correspondingly, we demonstrated that immune checkpoint blockade therapy may be more efficacious in high-risk group NSCLC patients according to TIDE method (P＜0.01). Conclusions: This novel BRGPs model can assess the prognosis of patients with NSCLC, and may be helpful to guide immune checkpoint inhibitors treatment in our clinical practice.

Hepatocellular carcinoma (HCC) is one of the most important causes of cancer-related deaths and remains a major public health challenge worldwide. Considering the extensive heterogeneity of HCC, more accurate prognostic models are imperative. The circadian genes regulate the daily oscillations of key biological processes, such as nutrient metabolism in the liver. Circadian rhythm disruption has recently been recognized as an independent risk factor for cancer. In this study, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were compared and 248 differentially expressed genes (DEGs) of the circadian rhythm were identified. HCC was classified into two subtypes based on these DEGs. The prognostic value of each circadian rhythm-associated gene (CRG) for survival was assessed by constructing a multigene signature from TCGA cohort. A 6-gene signature was created by applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all patients in TCGA cohort were divided into high- and low-risk groups according to their risk scores. The survival rate of patients with HCC in the low-risk group was significantly higher than that in the high-risk group (p < 0.001). The patients with HCC in the Gene Expression Omnibus (GEO) cohort were also divided into two risk subgroups using the risk score of TCGA cohort, and the overall survival time (OS) was prolonged in the low-risk group (p = 0.012). Based on the clinical characteristics, the risk score was an independent predictor of OS in the patients with HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that multiple metabolic pathways, cell cycle, etc., were enhanced in the high-risk group. Using the metabolic pathway single-sample gene set enrichment analysis (ssGSEA), it was found that the metabolic pathways in the high- and low-risk groups between TCGA and GEO cohorts were altered essentially in the same way. In conclusion, the circadian genes play an important role in HCC metabolic rearrangements and can be further used to predict the prognosis the patients with HCC.

BackgroundBreast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Natural killer (NK) cells play a crucial role in the innate immune system and exhibit significant anti-tumor activity. However, the role of NK cell-related genes (NRGs) in BC diagnosis and prognosis remains underexplored. With the advent of machine learning (ML) techniques, predictive modeling based on NRGs may offer a new avenue for precision oncology.MethodsWe collected transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were identified, and key prognostic NRGs were selected using univariate and multivariate Cox regression analyses. We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. Additionally, a prognostic risk model was developed using LASSO-Cox regression, and its performance was validated in independent cohorts. To explore the potential mechanisms underlying the prognostic differences between high-risk and low-risk patient groups, as well as their drug treatment sensitivities, we conducted functional enrichment analysis, tumor microenvironment analysis, immunotherapy prediction, drug sensitivity analysis, and mutation analysis.ResultsULBP2, CCL5, PRDX1, IL21, NFATC2, CD2, and VAV3 were identified as key NRGs for the construction of ML models. Among the 12 ML diagnostic models, the Random Forest (RF) model demonstrated the best performance, which demonstrated robust performance in distinguishing BC from normal tissues in both training (TCGA) and validation (GEO) cohorts. In terms of the prognostic model, the risk score based on LASSO-Cox regression effectively distinguished between high-risk and low-risk patients, with patients in the high-risk group exhibiting significantly poorer overall survival (OS) compared to those in the low-risk group, and was validated in the GEO cohorts. Patients in the high-risk group displayed increased tumor proliferation, immune evasion, and reduced immune cell infiltration, correlating with poorer prognosis and lower response rates to immunotherapy. Furthermore, drug sensitivity analysis indicated that high-risk patients were more sensitive to Thapsigargin, Docetaxel, AKT inhibitor VIII, Pyrimethamine, and Epothilone B, while showing higher resistance to drugs such as I-BET-762, PHA-665752, and Belinostat.ConclusionThis study provides a comprehensive analysis of NRGs in BC and establishes reliable ML-based diagnostic and prognostic models. The findings highlight the clinical relevance of NRGs in BC progression, immune regulation, and therapy response, offering potential targets for personalized treatment strategies.

BackgroundApoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes.MethodsMessenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan–Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model’s predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM.ResultsSeveral apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model’s score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival.ConclusionOurestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.

BackgroundSialyltransferases are enzymes involved in the addition of sialic acid to glycoproteins and glycolipids, influencing various physiological and pathological processes. The expression and function of sialyltransferases in tumors, particularly in kidney renal clear cell carcinoma (KIRC) remained underexplored. This study aimed to develop a prognostic model based on sialyltransferase-related genes (SRGs) to predict the prognosis and treatment response of patients with KIRC.MethodsWe utilized RNA-Seq data of KIRC from The Cancer Genome Atlas (TCGA) database, selecting samples with survival data and clinical outcomes. Somatic mutation and neoantigen data were analyzed using the "maftools" package, and genes involved in the sialylation process were identified through the Molecular Signatures Database. Validation cohorts of KIRC samples were obtained from the International Cancer Genome Consortium (ICGC) database. Single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) platform, and preprocessing, normalization, and dimensionality reduction analyses were conducted using the "Seurat" package. Differentially expressed sialylation genes were identified using the "limma" package, and their functional enrichment was assessed via Gene Ontology GO and KEGG analyses. Consensus clustering analysis was performed to identify molecular subtypes of KIRC based on sialylation, and drug sensitivity of different subtypes was evaluated using the "pRRophetic" package. A risk signature model comprising 5 SRGs was constructed through univariate and multivariate Cox regression analyses and validated in both the TCGA and ICGC cohorts. The "estimate" package was utilized to calculate immune and stromal scores for each KIRC sample, assessing the tumor immune microenvironment characteristics of different subtypes.ResultsAnalysis of scRNA-seq data identified 25 cell subtypes, categorized into 9 cell types. CD4 + memory cells exhibited the highest potential interactions with other cell subtypes. We identified 14 differentially expressed sialylation genes and confirmed their enrichment in various biological pathways through GO and KEGG analyses. Consensus clustering analysis based on sialylation identified 2 molecular subtypes: C1 and C2. The C2 subtype demonstrated higher sialylation scores and poorer prognosis. Drug sensitivity analysis indicated that the C1 subtype had better responses to Dasatinib and Lapatinib, whereas the C2 subtype was more sensitive to Epothilone B and Vinorelbine. The risk signature model, constructed with five distinct SRGs, exhibited strong predictive accuracy, as indicated by Area Under the Curve (AUC) values of 0.68, 0.69, and 0.70 for 1-, 3-, and 5-year survival, respectively, across both the TCGA and ICGC validation cohorts. Immune microenvironment analysis revealed that the C1 subtype exhibited higher immune and stromal scores, while the C2 subtype showed significantly enhanced expression of immune checkpoint genes.ConclusionThis study successfully developed a prognostic model based on SRGs, effectively predicting the prognosis and drug response of KIRC patients. The model demonstrated significant predictive performance and potential clinical application value. Furthermore, the study highlighted the critical role of sialylation in KIRC, offering new insights into its underlying mechanisms in tumor biology. These findings could guide personalized treatment strategies for KIRC patients, emphasizing the importance of sialylation in cancer prognosis and therapy.