The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor.

BACKGROUND: Lactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. METHODS: Gene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. RESULTS: The immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor's development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. CONCLUSION: This study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

A basement membrane-related signature for prognosis and immunotherapy benefit in bladder cancer based on machine learning.

BACKGROUND: Bladder cancer has a poor clinical outcome because of its high aggressiveness. Basement membrane plays vital functions in tumor invasion and migration. Invasion and distant metastasis of cancer are facilitated by degradation of the basement membrane and extracellular matrix. METHODS: Ten machine learning methods were utilized to develop the basement membrane-related signature (MRS) using datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276. Three anti-PD1 or anti-CTLA4 datasets and several predicting scores were used to investigate the performance of MRS in predicting the immunotherapy benefits. RESULTS: A predicting model based on the Enet algorithm (alpha = 0.1) was chosen as the optimal MRS since it had the highest average C-index being 0.72. According to TCGA data, the MRS showed good performance in predicting bladder cancer patients' clinical outcomes, with area under curves of 0.744, 0.766 and 0.817 for 1, 3, and 5-year receiver operating characteristic curve, respectively. PD1 and CTLA4 immunophenoscopes were associated with a low MRS score, as well as a lower tumor immune dysfunction and exclusion score. As MRS score increased, immune-activated cells levels decreased, tumor immune dysfunction and exclusion score decreased, immune escape score decreased, intratumor heterogeneity score decreased, PD1&CTLA4 immunophenoscore increased, and tumor mutational burden score increased in bladder cancer, suggesting better immunotherapy benefits. Bladder cancer cases with high MRS score was correlated with higher cancer related hallmark scores, including NOTCH and glycolysis signaling. CONCLUSION: A new MRS has been developed for bladder cancer, which could be used to predict prognosis and the success of immunotherapy.

Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis.

BACKGROUND: Mitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes. METHODS: RNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database. RESULTS: A predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted. CONCLUSION: The mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.

Integrating Bioinformatics and Experimental Validation to Identify Mitochondrial Permeability Transition-Driven Necrosis-Related lncRNAs that can Serve as Prognostic Biomarkers and Therapeutic Targets in Endometrial Carcinoma.

Endometrial carcinoma (EC) is a common malignant tumor in women with high mortality and relapse rates. Mitochondrial permeability transition (MPT)-driven necrosis is a novel form of programmed cell death. The MPT-driven necrosis related lncRNAs (MRLs) involved in EC development remain unclear. We aimed to predict the outcomes of patients with EC by constructing a novel prognostic model based on MRLs and explore potential molecular functions. A risk prognostic model was developed utilizing multi-Cox regression in conjunction with the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, which was based on MRLs. The predictive efficacy of the model was evaluated through receiver operating characteristic (ROC) curve analysis, as well as nomogram and concordance index (C-index) assessments. Patients were categorized into high- and low-risk groups based on their median risk scores. Notably, the high-risk group exhibited significantly poorer overall survival (OS) outcomes. Gene ontology (GO) and Gene set enrichment analysis (GSEA) demonstrated that Hedgehog and cell cycle pathways were enriched in the high-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) displayed that patients in the high-risk group showed a high likelihood of immune evasion and less effective immunotherapy. A significant disparity in immune function was also observed between two groups. Based on the nine-MRLs, drug sensitivity analysis identified several anticancer drugs with potential efficacy in prognosis. Meanwhile, the results demonstrated that OGFRP1 plays a carcinogenic role by affecting mitochondrial membrane permeability in EC. Therefore, the risk model constructed by nine MRLs could be used to predict the clinical outcomes and therapeutic responses in patients with EC effectively.

A novel prognostic risk score model based on RNA editing level in lower-grade glioma.

BACKGROUND: Lower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. METHODS: Survival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) RESULTS: A risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. CONCLUSION: In conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.

Background: Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature. Methods: Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. In vitro and in vivo experiments were conducted to validate the role of TUBA1C, a key gene within the signature. Results: A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion in vitro, and reduced tumor growth in vivo. Conclusion: This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.

CD8+ T cell associated scoring model helps prognostic diagnosis and immunotherapy selection in patients with colon adenocarcinoma.

Objective: T cell-mediated immunity plays a crucial role in the immune response against tumors, with CD 8+ T cells playing a leading role in the eradication of cancer cells. Material and methods: A total of 5 datasets were included in this study. Single cell transcriptome data were used to discover CD8+ T cell marker genes, and Bulk transcriptome data from TCGA and GEO were jointly analyzed to screen candidate prognostic genes. lasso regression was performed to construct prognostic models. Immunotherapy cohort (IMvigor 210 and GSE78220) was applied to validate the diagnostic power of markers. Result: Single-cell transcriptome data identified 65 CD8+ T cell marker genes, highlighting their importance in T cell-mediated immune responses. Among these, 11 genes were identified as CD8+ T-associated differential genes through analysis of bulk data from TCGA and GEO. A prognostic model for 5 genes was identified based on Lasso regression, dividing colon adenocarcinoma (COAD) patients into high- and low-risk groups. This model exhibited higher prognostic accuracy compared to traditional clinicopathological characteristics (age, pathological stage, histological grading). Moreover, the risk score derived from this model successfully differentiated patient responses to immunotherapy, as validated in the IMvigor 210 and GSE78220 cohorts. Conclusion: Our research introduces a novel prognostic signature based on CD8+ T cell marker genes, demonstrating significant predictive power for prognosis and immunotherapy response in COAD patients. This model offers a potential tool for improving patient stratification and personalizing treatment strategies.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort's AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer.

Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.

Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma.

Background: Protein information is often replaced by RNA data in studies to understand cancer-related biological processes or molecular functions, and proteins of prognostic significance in Kidney clear cell carcinoma (KIRC) remain to be mined. Methods: The cancer genome atlas program (TCGA) data was utilized to screen for proteins that are prognostically significant in KIRC. Machine learning algorithms were employed to develop protein prognostic models. Additionally, immune infiltration abundance, somatic mutation differences, and immunotherapeutic responses were analyzed in various protein risk subgroups. Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). Results: The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. Conclusion: In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. Additionally, we have developed a reliable prognostic model with excellent predictive capabilities.

Unveiling the role of PSMA5 in glioma progression and prognosis.

Glioma is the most aggressive intracranial malignancy and is associated with poor survival rates and limited quality of life, impairing neuropsychological function and cognitive competence in survivors. The Proteasome Subunit Alpha Type-5 (PSMA5) is a multicatalytic proteinase complex that has been linked with tumor progression but is rarely reported in glioma. This study investigates the expression pattern, prognostic characteristics, and potential biological functions of PSMA5 in glioma. PSMA5 was significantly overexpressed in 28 types of cancer when compared to normal tissue. Furthermore, elevated levels of PSMA5 were observed in patients with wild-type isocitrate dehydrogenase 1 and exhibited a positive correlation with tumor grade. It was also found to be a standalone predictor of outcomes in glioma patients. Additionally, inhibiting PSMA5-induced cell cycle arrest may provide a therapeutic option for glioma.

A cuproptosis-related prognostic signature for guiding clinical diagnosis and treatment in uveal melanoma patients.

Background: Cuproptosis, one of the most recently discovered forms of cell death, is induced by the disruption of copper binding to the mitochondrial respiratory acylation components. However, the mechanism underlying cuproptosis in uveal melanoma (UM) has not yet been adequately studied. Methods: RNA and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed cuproptosis-related genes were identified by R software. A prognostic signature was constructed by applying LASSO regression and Cox regression models. The associations between the signature and the immune microenvironment, overall survival, and drug sensitivity were studied. In addition, qPCR and Western blotting were performed on UM cells and RPE cell lines to verify the expression levels of the genes encoding dihydrolipoamide dehydrogenase (DLD) and dihydrolipoamide S-succinyltransferase (DLST) in UM cases. Results: Using a cuproptosis-related prognostic signature, UM samples were classified into high- and low-risk groups. A significant difference in overall survival between the two risk groups was evident. Receiver operating characteristic curves demonstrated that the signature is a reliable predictor of prognosis. Immune cell infiltration, drug sensitivity, and immune checkpoint expression were analysed. Significant immune difference between the two high-risk groups was found, and the high expression of immune checkpoints in high-risk groups suggests significant immunotherapy potential. In addition, drug sensitivity analysis experiments suggest that erlotinib may be a potential treatment for high-risk patients. The results of in vitro experiments confirmed that DLD and DLST had higher expression levels in UM cell lines. Conclusions: The prognostic signature developed in this study is a reliable biomarker for predicting the prognosis of UM and may serve as a tool for personalised treatment of patients with UM.

Analysis of metastasis­related risk factors and clinical relevance in adult soft­tissue sarcoma.

Metastasis occurs in nearly 50% of cases of adult soft-tissue sarcoma (ASTS), leading to a dismal prognosis, with a 2-year survival rate of ~30%. Consequently, a prognostic model that incorporates metastatic characteristics may be instrumental in predicting survival time and in crafting optimal personalized therapeutic strategies for patients with ASTS. In the present study, a prognostic prediction model for ASTS was developed by examining genes that are differentially expressed between non-metastatic and metastatic patients in the Gene Expression Omnibus dataset. The prognostic model, which includes five featured genes [actin γ2 (ACTG2), apolipoprotein D, coatomer protein complex subunit γ2 imprinted transcript 1, collagen type VI α6 chain and osteomodulin], was further validated in patients with ASTS from the Cancer Genome Atlas dataset. Based on these five-gene signatures, patients were categorized into high- and low-risk groups. Functional and pathway analyses revealed disparities in stemness, extracellular matrix and cell adhesion-related pathways between the two risk groups, particularly noting the activation of the PI3K-Akt pathway in high-risk cases. Analysis of immune infiltration also revealed variations in immune microenvironment changes between the two risk groups. Immunohistochemical staining substantiated the prognostic significance of these gene signatures in a specific sarcoma subtype. Additionally, wound-healing and Transwell assays demonstrated that inhibition of ACTG2 by shRNAs curbed cell migration and invasion in a sarcoma HOS cell line, underscoring its role in sarcoma metastasis. In conclusion, the present study successfully developed and validated a metastasis-based prognosis prediction model. This model not only reliably forecasts the survival of patients with ASTS, but also may pave the way for further investigation into the processes underlying sarcoma metastasis, ultimately aiding in the design of tailored therapeutic regimens.

A Bruton tyrosine kinase inhibitor-resistance gene signature predicts prognosis and identifies TRIP13 as a potential therapeutic target in diffuse large B-cell lymphoma.

Bruton tyrosine kinase inhibitor (BTKi) combined with rituximab-based chemotherapy benefits diffuse large B-cell lymphoma (DLBCL) patients. However, drug resistance is the major cause of relapse and death of DLBCL. In this study, we conducted a comprehensive analysis BTKi-resistance related genes (BRRGs) and established a 10-gene (CARD16, TRIP13, PSRC1, CASP1, PLBD1, CARD6, CAPG, CACNA1A, CDH15, and NDUFA4) signature for early identifying high-risk DLBCL patients. The resistance scores based on the BRRGs signature were associated with prognosis. Furthermore, we developed a nomogram incorporating the BRRGs signature, which demonstrated excellent performance in predicting the prognosis of DLBCL patients. Notably, tumor immune microenvironment, biological pathways, and chemotherapy sensitivity were different between high- and low-resistance score groups. Additionally, we identified TRIP13 as a key gene in our model. TRIP13 was found to be overexpressed in DLBCL and BTKi-resistant DLBCL cell lines, knocking down TRIP13 suppresses cell proliferation, promotes cell apoptosis, and enhances the apoptosis effect of BTKi on DLBCL cells by regulating the Wnt/ß-catenin pathway. In conclusion, our study presents a novel BRRGs signature that could serve as a promising prognostic marker in DLBCL, and TRIP13 might be a potential therapeutic target for resistant DLBCL.

Liquid-liquid phase separation-related genes associated with prognosis, tumor microenvironment characteristics, and tumor cell features in bladder cancer.

OBJECTIVE: This study aimed to explore the Liquid-liquid phase separation (LLPS)-related genes associated with the prognosis of bladder cancer (BCa) and assess the potential application of LLPS-related prognostic signature for predicting prognosis in BCa patients. METHODS: Clinical information and transcriptome data of BCa patients were extracted from the Cancer Genome Atlas-BLCA (TCGA-BLCA) database and the GSE13507 database. Furthermore, 108 BCa patients who received treatment at our institution were subjected to a retrospective analysis. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an LLPS-related prognostic signature for BCa. The CCK8, wound healing and Transwell assays were performed. RESULTS: Based on 62 differentially expressed LLPS-related genes (DELRGs), three DELRGs were screened by LASSO analysis including kallikrein-related peptidase 5 (KLK5), monoacylglycerol O-acyltransferase 2 (MOGAT2) and S100 calcium-binding protein A7 (S100A7). Based on three DELRGs, a novel LLPS-related prognostic signature was constructed for individualized prognosis assessment. Kaplan-Meier curve analyses showed that LLPS-related prognostic signature was significantly correlated with overall survival (OS) of BCa. ROC analyses demonstrated the LLPS-related prognostic signature performed well in predicting the prognosis of BCa patients in the training group (the area under the curve (AUC) = 0.733), which was externally verified in the validation cohort 1 (AUC = 0.794) and validation cohort 2 (AUC = 0.766). Further experiments demonstrated that inhibiting KLK5 could affect the proliferation, migration, and invasion of BCa cells. CONCLUSIONS: In this study, a novel LLPS-related prognostic signature was successfully developed and validated, demonstrating strong performance in predicting the prognosis of BCa patients.

A Circadian Rhythm-related Signature to Predict Prognosis, Immunei Infiltration, and Drug Response in Breast Cancer.

PURPOSE: Circadian rhythm genes (CRRGs) play essential roles in cancer occurrence and development. However, the prognostic significance of CRRGs in breast cancer (BC) has not been fully elucidated. Our study aimed to develop a prognostic gene signature based on CRRGs that can accurately and stably predict the prognosis of BC. METHODS: The transcriptome data and clinical information for BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus unsupervised clustering analysis was carried out to investigate the roles of CRRGs in BC. A CRRGs-related prognostic risk model was established by using logistic least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox regression analyses. Kaplan-Meier (KM) curves analysis, time-dependent receptor operation characteristics (ROC) curves analysis, and nomogram were plotted to evaluate the predictive efficacy of the model. The relevance of risk score to the immune cell infiltration, tumor burden mutation (TMB), and therapeutic response was assessed. RESULTS: A risk model comprising six CRRGs (SLC44A4, SLC16A6, TPRG1, FABP7, GLYATL2, and FDCSP) was constructed and validated, demonstrating a good predictor of BC. The low-risk group displayed a higher number of immune activities and immune checkpoint expression and a lower burden of tumor mutation. Additionally, drug sensitivity analysis demonstrated the prognostic signature may serve as a potential chemosensitivity predictor. CONCLUSION: We established 6 CRRGs-related risk signatures for the prognosis of BC, which is of great value in predicting the prognosis of patients with BC and guiding the treatment for BC.

The novel prognostic analysis of AML based on ferroptosis and cuproptosis related genes.

BACKGROUND: Acute myeloid leukemia (AML) is a hematological malignancy. The aim of this research was to develop a ferroptosis and cuproptosis related novel prognostic signature associated with AML. METHODS: The ferroptosis and cuproptosis related genes correlated with the prognosis of AML were identified by univariate Cox analysis. The consistent cluster analysis was performed for 150 AML patients in TCGA dataset. The key module genes associated with GSVA score of ferroptosis and cuproptosis were identified by WGCNA. univariate Cox and LASSO regression analysis were adopted to build a ferroptosis and cuproptosis AML prognostic signature. Finally, the expression of five prognostic genes in clinical tissue samples were verified by RT-qPCR. RESULTS: A grand total of 27 FCRGs associated with AML prognosis were identified.Then, two AML sub-types with significantly different survival were obtained. We found 3 significantly differential expressed immune cells (naive CD4 cells, regulatory T cells and resting mast cells) between two risk sub-groups. Meanwhile, 'IL6 JAK STAT3 signaling' and 'P53 pathway' were enriched in low-risk group. A ferroptosis and cuproptosis related prognostic signature was build based on 8 prognostic genes. RT-qPCR results indicated that there was no significant difference in the expression of OLFML2A and CD109 between AML and normal samples. However, compared to the control group, LGALS1, SOCS1, and RHOC showed significantly lower expression in the AML group. CONCLUSION: The prognostic signature comprised of OLFML2A, LGALS1, ABCB11, SOCS1, RHOC, CD109, RD3L and PTPN13 based on ferroptosis and cuproptosis was established, which provided theoretical basis for the research of AML.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM­related gene prediction model and independent external validation.

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

A platelet-related signature for predicting the prognosis and immunotherapy benefit in bladder cancer based on machine learning combinations.

Background: Bladder cancer carries a large societal burden, with over 570,000 newly diagnosed cases and 210,000 deaths globally each year. Platelets play vital functions in tumor progression and therapy benefits. We aimed to construct a platelet-related signature (PRS) for the clinical outcome of bladder cancer cases. Methods: Ten machine learning techniques were used in the integrative operations to build PRS using the datasets from The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32894 and GSE48276. A number of immunotherapy datasets and prediction scores, including GSE91061, GSE78220, and IMvigor210, were utilized to assess how well the PRS predicted the benefit of immunotherapy. Vitro experiment was performed to verify the role of α1C-tubulin (TUBA1C) in bladder cancer. Results: Enet (alpha =0.4) algorithm-based PRS had the highest average C-index of 0.73 and it was suggested as the optimal PRS. PRS acted as an independent risk factor for bladder cancer and patients with high PRS score portended a worse overall survival rate, with the area under the curve of 1-, 3- and 5-year operating characteristic curve being 0.754, 0.779 and 0.806 in TCGA dataset. A higher level of immune-activated cells, cytolytic function and T cell co-stimulation was found in the low PRS score group. Low PRS score demonstrated a higher tumor mutation burden score and programmed cell death protein 1 & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, lower tumor immune dysfunction and exclusion score, intratumor heterogeneity score and immune escape score in bladder cancer, suggesting the PRS as an indicator for predicting immunotherapy benefits. Vitro experiment showed that TUBA1C was upregulated in bladder cancer and knockdown of TUBA1C obviously suppressed tumor cell proliferation. Conclusions: The present study developed an ideal PRS for bladder cancer, which may be used as a predictor of prognosis, a risk classification system, and a therapy guide.

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma.

BACKGROUND: Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients. METHODS: We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan-Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response. RESULTS: We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes. CONCLUSIONS: Our findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations.