Construction of a Cancer Stem Cell Marker Genes-Related 22-Gene Signature for Overall Survival Prediction in High-Risk Wilms' Tumor.

Background: This study aimed to investigate the comprehensive expression profile of cancer stem cell (CSC)-related genes and construct a prognostic signature for overall survival (OS) prediction in high-risk Wilms' tumor (WT). Materials and methods: Gene expression and survival data from 120 high-risk WT cases in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-WT were used. Results: In total, 229 CSC-related genes were found to be significantly dysregulated in WT compared to tumor-adjacent normal tissues, among which 34 were associated with OS. Using LASSO regression, a 22-gene signature was developed, which exhibited excellent performance in 3-, 5-, and 10-year OS predictions (AUC > 0.86). The high-risk score group showed markedly poorer OS compared to the low-risk score group (median separation, HR = 6.41, 95% CI: 3.18-12.92, p = 3.2e - 9). The 22-gene signature was an independent prognostic factor for OS (HR = 5.086, 95% CI: 3.019-8.568, p < 0.001). Conclusion: This study identified a robust prognostic signature that can effectively support OS prediction.

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma.

PURPOSE: NK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL. METHODS: Bioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples. RESULTS: We identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased. CONCLUSION: The NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

The prognostic implications of cuproptosis-related gene signature and the potential of PPIC as a promising biomarker in cutaneous melanoma.

Cutaneous melanoma is the most lethal of all skin tumors. Recently, cuproptosis, a novel form of cell death linked to oxidative phosphorylation, has emerged as an important factor. However, the precise role of cuproptosis in melanoma remains unclear. Our research explored the potential links between cuproptosis-related genes, prognosis, immune microenvironments, and melanoma treatments. Significantly, cuproptosis regulators showed remarkable differences between melanoma and normal tissues, establishing their relevance to melanoma. The newly developed cuproptosis-related gene signature (CGS) demonstrated a robust ability to predict overall survival (OS) in melanoma. We constructed a novel nomogram that combined clinical features with CGS to improve predictive accuracy. In addition, the study revealed correlations between CGS and immune cell populations, including CD8+T cells, Tfh cells, B cells, and myeloid-derived suppressor cells. Within the CGS, Peptidylprolyl isomerase C (PPIC) emerged as the most strongly associated with poor prognosis and drug resistance in melanoma. PPIC was identified as a promoter of melanoma progression, enhancing cell invasiveness while concurrently suppressing CD8+T cell activation. This comprehensive study not only elucidated the intricate connections between CGS, melanoma prognosis, immune microenvironment, and drug resistance but also provided compelling evidence supporting PPIC as a promising biomarker for predicting OS in melanoma treatment.

Deciphering the Role of Necroptosis-Related Long Non-coding RNAs in Hepatocellular Carcinoma: A Necroptosis-Related lncRNA-Based Signature to Predict the Prognosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer, characterized by a high morbidity rate. Long non-coding RNAs (lncRNAs) play an important role in regulating various cellular processes and diseases, including cancer. However, their specific roles and mechanisms in HCC are not fully understood. This study used a multi-cohort design to investigate necroptosis-related lncRNAs (NRLs) in patients with HCC. We curated a list of 1095 NRLs and 838 genes showing differential expression between tumor and normal tissues. Among them, we found 105 NRLs closely associated with the prognosis of HCC patients. The 10 lncRNAs (AC100803.3, AC027237.2, AL158166.1, LINC02870, AC026412.3, LINC02159, AC027097.1, AC139887.4, AC007405.1, AL023583.1) generated by LASSO-Cox regression analysis were used to create a prognostic risk model for HCC and group patients into groups based on risk. The KEGG analysis revealed distinct pathway enrichments in high-risk (H-R) and low-risk (L-R) subgroups. According to GO analysis, this study identified 230 differentially expressed genes (DEGs) that were significantly enriched in specific biological processes. Comparison of immune checkpoint-related genes (MCPGs) between H-R and L-R patients revealed significant differences. Moreover, we established a correlation between the risk scores of patients with liver cancer and their sensitivity to 16 chemotherapeutic agents. Employing protein-protein interaction (PPI) analysis, we identified 10 hub genes that potentially regulate the molecular networks involved in HCC development. This study is a pioneering effort to investigate the roles of NRLs in HCC. It opens a new avenue for potential targeted therapies and provides insights into the molecular mechanisms of HCC.

Identification of a prognosis-related phagocytosis regulator gene signature in medulloblastoma.

Objectives: The aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes. Methods: Differentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed. Results: In total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages. Conclusion: Ten key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

5-Fluorouracil resistance-based immune-related gene signature for COAD prognosis.

Background: Drug resistance is the primary obstacle to advanced tumor therapy and the key risk factor for tumor recurrence and death. 5-Fluorouracil (5-FU) chemotherapy is the most common chemotherapy for individuals with colorectal cancer, despite numerous options. Methods: The Gene Expression Omnibus database was utilized to extract expression profile data of HCT-8 human colorectal cancer wild-type cells and their 5-FU-induced drug resistance cell line. These data were used to identify 5-FU resistance-related differentially expressed genes (5FRRDEGs), which intersected with the colorectal adenocarcinoma (COAD) transcriptome data provided by the Cancer Genome Atlas Program database. A prognostic signature containing five 5FRRDEGs (GOLGA8A, KLC3, TIGD1, NBPF1, and SERPINE1) was established after conducting a Cox regression analysis. We conducted nomogram development, drug sensitivity analysis, tumor immune microenvironment analysis, and mutation analysis to assess the therapeutic value of the prognostic qualities. Results: We identified 166 5FRRDEGs in patients with COAD. Subsequently, we created a prognostic model consisting of five 5FRRDEGs using Cox regression analysis. The patients with COAD were divided into different risk groups by risk score; the high-risk group demonstrated a worse prognosis than the low-risk group. Conclusion: In summary, the 5FRRDEG-based prognostic model is an effective tool for targeted therapy and chemotherapy in patients with COAD. It can accurately predict the survival prognosis of these patients as well as to provide the direction for exploring the resistance mechanism underlying COAD.

Characterization of a novel mitophagy-related 5-genes signature for diagnosis of acute myocardial infarction.

Myocardial infarction (MI) and the ensuing heart failure (HF) remain the main cause of morbidity and mortality worldwide. One of the strategies to combat MI and HF lies in the ability to accurately predict the onset of these disorders. Alterations in mitochondrial homeostasis have been reported to be involved in the pathogenesis of various cardiovascular diseases (CVDs). In this regard, perturbations to mitochondrial dynamics leading to impaired clearance of dysfunctional mitochondria have been previously established to be a crucial trigger for MI/HF. In this study, we found that MI patients could be classified into three clusters based on the expression levels of mitophagy-related genes and consensus clustering. We identified a mitophagy-related diagnostic 5-genes signature for MI using support vector machines-Recursive Feature Elimination (SVM-RFE) and random forest, with the area under the ROC curve (AUC) value of the predictive model at 0.813. Additionally, the single-cell transcriptome and pseudo-time analyses showed that the mitoscore was significantly upregulated in macrophages, endothelial cells, pericytes, fibroblasts and monocytes in patients with ischemic cardiomyopathy, while sequestosome 1 (SQSTM1) exhibited remarkable increase in the infarcted (ICM) and non-infarcted (ICMN) myocardium samples dissected from the left ventricle compared with control samples. Lastly, through analysis of peripheral blood from MI patients, we found that the expression of SQSTM1 is positively correlated with troponin-T (P < 0.0001, R = 0.4195, R2 = 0.1759). Therefore, this study provides the rationale for a cell-specific mitophagy-related gene signature as an additional supporting diagnostic for CVDs.

Identification of Copper Homeostasis-Related Gene Signature for Predicting Prognosis in Patients with Epithelial Ovarian Cancer.

Objectives: This research aims to establish a copper homeostasis-related gene signature for predicting the prognosis of epithelial ovarian cancer and to investigate its underlying mechanisms. Methods: We mainly constructed the copper homeostasis-related gene signature by LASSO regression analysis. Then multiple methods were used to evaluate the independent predictive ability of the model and explored the mechanisms. Results: The 15-copper homeostasis-related gene (15-CHRG) signature was successfully established. Utilizing an optimal cut-off value of 0.35, we divided the training dataset into high-risk and low-risk subgroups. Kaplan-Meier analysis revealed that survival times for the high-risk subgroup were significantly shorter than those in the low-risk group (P < .05). Additionally, the Area Under the Curve (AUC) of the 15-CHRG signature achieved 0.822 at 1 year, 0.762 at 3 years, and 0.696 at 5 years in the training set. COX regression analysis confirmed the 15-CHRG signature as both accurate and independent. Gene set enrichment (GSEA), Kyoto Encyclopedia of Gene and Genome (KEGG) and Gene Ontology (GO) analysis showed that there were significant differences in apoptosis, p53 pathway, protein synthesis, hydrolase and transport-related pathways between high-risk group and low-risk group. In tumor immune cell (TIC) analysis, the increased expression of resting mast cells was positively correlated with the risk score. Conclusion: Consequently, the 15-CHRG signature shows significant potential as a method for accurately predicting clinical outcomes and treatment responses in patients with epithelial ovarian cancer.

Identification of Immune Gene Signature Associated with T Cells and Natural Killer Cells in Type 1 Diabetes.

Purpose: This study aimed to investigate the abnormal infiltration of immune cells in type 1 diabetes mellitus (T1D) and elucidate their regulatory mechanisms. Methods: Public T1D-related gene expression data were obtained from the Gene Expression Omnibus database.The GSE123658 dataset analyzed whole blood RNA-seq data from type 1 diabetic patients and healthy volunteers. The GSE110914 dataset analyzed neutrophils purified from peripheral blood of patients with symptomatic and pre-symptomatic type 1 diabetes (T1D), at risk of T1D, and healthy controls. Immune cell infiltration analysis was performed to identify abnormally infiltrating immune cells. Differentially expressed immune genes (DEIGs) in T1D samples were identified, followed by the construction of an immune gene signature (IGS) using a protein-protein interaction (PPI) network and Least absolute shrinkage and selection operator Cox regression analyses (LASSO Cox regression analyses). The regulatory mechanisms underlying IGS were explored using gene set enrichment analysis. Furthermore, expression validation, diagnostic efficacy evaluation, and upstream miRNA prediction of hub signature genes were performed. We verified the miRNA expression of the key gene colony stimulating factor 1 (CSF1) and microRNA-326 (miR-326) by reverse transcription-quantitative PCR (RT‒qPCR). Results: The proportion of infiltrating T and natural killer (NK) cells differed between the T1D and control samples, and 207 immune genes (IGs) related to these immune cells were extracted. After differential expression, PPI, and LASSO Cox regression analyses, four signature DEIGs were identified for IGS construction: notch receptor 1 (NOTCH1), Janus kinase 3 (JAK3), tumor necrosis factor receptor superfamily member 4(TNFRSF4), and CSF1. Key pathways such as the Toll-like receptor signaling pathway were significantly activated in the high-risk group. Moreover, the upregulation of CSF1 in T1D samples was confirmed using a validation dataset, and CSF1 showed high diagnostic efficacy for T1D. Furthermore, CSF1 was targeted by miR-326.We used validated key genes in T1D patients, several of which were confirmed by RT‒qPCR. Conclusion: In conclusion, the identified key IGs may play an important role in T1D. CSF1 can be developed as a novel diagnostic biomarker for T1D.

Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma.

Background: Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses. Methods: Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814). Results: Functional analysis revealed an enrichment of genes related to the TGF-ß signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score. Conclusion: This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-ß pathway and provided insights into LUAD prognosis and immune activities.

Predictive Biomarkers for Response to TGF- ß Inhibition in Resensitizing Chemo(radiated) Esophageal Adenocarcinoma.

Epithelial-mesenchymal transition (EMT) has been identified as a driver of therapy resistance, particularly in esophageal adenocarcinoma (EAC), where transforming growth factor beta (TGF-ß) can induce this process. Inhibitors of TGF-ß may counteract the occurrence of mesenchymal, resistant tumor cell populations following chemo(radio)therapy and improve treatment outcomes in EAC. Here, we aimed to identify predictive biomarkers for the response to TGF-ß targeting. In vitro approximations of neoadjuvant treatment were applied to publicly available primary EAC cell lines. TGF-ß inhibitors fresolimumab and A83-01 were employed to inhibit EMT, and mesenchymal markers were quantified via flow cytometry to assess efficacy. Our results demonstrated a robust induction of mesenchymal cell states following chemoradiation, with TGF-ß inhibition leading to variable reductions in mesenchymal markers. The cell lines were clustered into responders and non-responders. Genomic expression profiles were obtained through RNA-seq analysis. Differentially expressed gene (DEG) analysis identified 10 positively- and 23 negatively-associated hub genes, which were bioinformatically identified. Furthermore, the correlation of DEGs with response to TGF-ß inhibition was examined using public pharmacogenomic databases, revealing 9 positively associated and 11 negatively associated DEGs. Among these, ERBB2, EFNB1, and TNS4 were the most promising candidates. Our findings reveal a distinct gene expression pattern associated with the response to TGF-ß inhibition in chemo(radiated) EAC. The identified DEGs and predictive markers may assist patient selection in clinical studies investigating TGF-ß targeting.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer.

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

A Novel Signature Based on Angiogenesis-Related Genes Predicts the Prognosis and Immunotherapy Response in HER2-Positive Breast Cancer.

Background: HER2-positive breast cancer is one of the most prevalent subtypes of breast cancer and represents a significant health concern for women worldwide due to its high morbidity and mortality rates. Recent studies have consistently underscored the pivotal role of angiogenesis in the development and progression of HER2-positive breast cancer. Here, we developed a prognostic signature based on angiogenesis-related genes (ARGs) to categorize HER2-positive breast cancer patients and provide insights into their survival outcomes. Methods: Kaplan-Meier survival curve, time-dependent receiver operating characteristic (ROC) and nomogram were performed to investigate the prognostic performance of the signature. In addition, we comprehensively analyzed the correlation of the prognostic signature with immune cell infiltration, immune checkpoint inhibitors (ICIs) therapy. Finally, Immunohistochemistry (IHC) and immunoblotting were used to investigate XBP1 expression in HER2-positive breast cancer tissues. Colony formation assay was performed to examine cell proliferation of HER2-positive breast cancer cells. Results: The Kaplan-Meier curves and the ROC curves demonstrated that the ARGs had good performance in predicting the prognosis of HER2-positive breast cancer patients. In addition, we observed that the low-risk group was remarkably associated with immune infiltration and better response to ICIs. Further experimental results show that XBP1 is upregulated in human HER2-positive breast cancer, and its knockdown significantly inhibited cell proliferation. Conclusions: Our study demonstrated that the ARGs could serve as a novel biomarker for predicting the prognosis of patients with HER2-positive breast cancer and providing new insights into immunotherapy strategies for these patients.

The classification of melanocytic gene signatures.

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

A Core NRF2 Gene Set Defined Through Comprehensive Transcriptomic Analysis Predicts Selective Drug Resistance and Poor Multicancer Prognosis.

Aims: The nuclear factor erythroid 2-related factor 2-Kelch-like ECH-associated protein 1 (NRF2-KEAP1) pathway plays an important role in the cellular response to oxidative stress but may also contribute to metabolic changes and drug resistance in cancer. However, despite its pervasiveness and important role, most of nuclear factor erythroid 2-related factor 2 (NRF2) target genes are defined in context-specific experiments and analysis, making it difficult to translate from one situation to another. Our study investigates whether a core NRF2 gene signature can be derived and used to represent NRF2 activation in various contexts, allowing better reproducibility and understanding of NRF2. Results: We define a core set of 14 upregulated NRF2 target genes from 7 RNA-sequencing datasets that we generated and analyzed. This NRF2 gene signature was validated using analyses of published datasets and gene sets. An NRF2 activity score based on expression of these core target genes correlates with resistance to drugs such as PX-12 and necrosulfonamide but not to paclitaxel or bardoxolone methyl. We validated these findings in our Kelch-like ECH-associated protein 1 (KEAP1) knockout cancer cell lines. Finally, our NRF2 score is prognostic for cancer survival and validated in additional independent cohorts for lung adenocarcinoma and also novel cancer types not associated with NRF2-KEAP1 mutations such as clear cell renal carcinoma, hepatocellular carcinoma, and acute myeloid leukemia. Innovation and Conclusions: These analyses define a core NRF2 gene signature that is robust, versatile, and useful for evaluating NRF2 activity and for predicting drug resistance and cancer prognosis. Using this gene signature, we uncovered novel selective drug resistance and cancer prognosis associated with NRF2 activation.

Comprehensive Analysis of a Six-Gene Signature Predicting Survival and Immune Infiltration of Liposarcoma Patients and Deciphering Its Therapeutic Significance.

BACKGROUND: As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is essential to evaluate the prognosis of LPS patients. METHODS: By comprehensive analysis of data derived from GEO datasets, differentially expressed genes (DEGs) were obtained. Univariate and Lasso Cox regressions were subsequently employed to reveal distant recurrence-free survival (DRFS)-associated DEGs and develop a prognostic gene signature, which was assessed by Kaplan-Meier survival and ROC curve. GSEA and immune infiltration analyses were conducted to illuminate molecular mechanisms and immune correlations of this model in LPS progression. Furthermore, a correlation analysis was involved to decipher the therapeutic significance of this model for LPS. RESULTS: A six-gene signature was developed to predict DRFS of LPS patients and showed higher precision performance in more aggressive LPS subtypes. Then, a nomogram was further established for clinical application based on this risk model. Via GSEA, the high-risk group was significantly enriched in cell cycle-related pathways. In the LPS microenvironment, neutrophils, memory B cells and resting mast cells exhibited significant differences in cell abundance between high-risk and low-risk patients. Moreover, this model was significantly correlated with therapeutic targets. CONCLUSION: A prognostic six-gene signature was developed and significantly associated with cell cycle pathways and therapeutic target genes, which could provide new insights into risk assessment of LPS progression and therapeutic strategies for LPS patients to improve their prognosis.

Unveiling a cuproptosis-related risk model and the role of FARSB in hepatocellular carcinoma.

Background: Cuproptosis, a type of regulated cell death that was recently identified, has been linked to the development of a variety of diseases, among them being cancers. Nevertheless, the prognostic significance and therapeutic implications of the cuproptosis potential index in hepatocellular carcinoma (HCC) remain uncertain. Methods: Single-sample gene set enrichment analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA) methodology was conducted to ascertain the identification of modular genes that are closely linked to cuproptosis. In addition, the gene signature indicative of prognosis was formulated by employing univariate Cox regression analysis in conjunction with a random forest algorithm. The efficacy of this gene signature in predicting outcomes was confirmed through validation in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Furthermore, a study was undertaken to evaluate the association between the risk score and various clinical-pathological characteristics, explore the biological processes linked to the gene signature, and analyze tumor mutational burden and somatic mutations. Lastly, potential drugs targeting the identified gene signature were identified through screening. Results: The results of our comprehensive analysis across multiple cancer types demonstrated a positive correlation between an elevated cuproptosis potential index (CPI) and an accelerated rate of tumor progression. Furthermore, employing the WGCNA technique, we successfully identified 640 genes associated with cuproptosis. Among these genes, we meticulously screened and validated a seven-gene signature (TCOF1, NOP58, TMEM69, FARSB, DHX37, SLC16A3, and CBX2) that exhibited substantial prognostic significance. Using the median risk score, the division of HCC patients into cohorts with high- and low-risk highlighted significant disparities in survival results, wherein the group with higher risk exhibited a less favorable overall survival. The risk score exhibited commendable predictive efficacy. Moreover, the in vitro knockdown of FARSB significantly hindered cell viability, induced G1 phase arrest, increased apoptosis, and impaired migration in HepG2 and Huh7 cells. Conclusion: Our research has successfully identified a strong seven-gene signature linked to cuproptosis, which could be utilized for prognostic evaluation and risk stratification in patients with HCC. Furthermore, the discovered gene signature, coupled with the functional analysis of FARSB, presents promising prospects as potential targets for therapeutic interventions in HCC.

Bioinformatics prediction and experimental verification identify a cuproptosis-related gene signature as prognosis biomarkers of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) of which its prognostic prediction is still unclarified is a highly heterogeneous disease. Cuproptosis is a form of cell death that depends on copper regulation. Whether the cuproptosis-related genes can be the prognostic indicators of HCC is yet to be elucidated. The aim of this study is to investigate whether cuproptosis-related genes play a role in HCC and can be used as a diagnostic index to predict the occurrence of liver cancer. Methods: We downloaded HCC patients' gene expression profiles and their corresponding clinical data from a public database. To screen data, we used single factor Cox regression analysis, meanwhile, polymerase chain reaction (PCR) was used for the verification. After that, the risk score was calculated and the relationship between risk score and clinical factors was analyzed. Besides, a nomogram map was constructed for predicting the prognosis of HCC, and calibration map and decision curve analysis (DCA) map were used to test the model. Results: Compared to the high expression group of four cuproptosis-related genes, the low expression group showed better overall survival (OS) [hazard ratio (HR) =2.58; 95% confidence interval (CI): 1.72-3.89, P<0.01]. The expression of the four cuproptosis-relate genes increased in liver cancer cell lines compared to liver cell lines (P<0.05). Based on these four genes, we calculated the risk score and divided them into two groups as high-risk group and low-risk group. The risk factor map showed the high-risk group had shorter survival time and the four genes were highly expressed. The area under curve (AUC) of receiver operating characteristic (ROC) prediction curve for the first year was 0.726. Risk scores were closely related to clinical factors and immune cells. Finally, we constructed a nomogram for predicting the prognosis of HCC. Conclusions: The risk score for cuproptosis-related genes was established and involved in the construction of the nomogram, providing a new perspective on the prognosis and copper metabolism of HCC.

Prostate cancer cancer-associated fibroblasts with stable markers post-androgen deprivation therapy associated with tumor progression and castration resistant prostate cancer.

The specificity and clinical relevance of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa), as well as the effect of androgen deprivation therapy (ADT) on CAFs, remain to be fully elucidated. Using cell lineage diversity and weighted gene co-expression network analysis (WGCNA), we pinpointed a unique CAF signature exclusive to PCa. The specificity of this CAF signature was validated through single-cell RNA sequencing (scRNA-seq), cell line RNA sequencing, and immunohistochemistry. This signature associates CAFs with tumor progression, elevated Gleason scores, and the emergence of castration resistant prostate cancer (CRPC). Using scRNA-seq on collected samples, we demonstrated that the CAF-specific signature is not altered by ADT, maintaining its peak signal output. Identifying a PCa-specific CAF signature and observing signaling changes in CAFs after ADT lay essential groundwork for further PCa studies.

Identifying an immunosenescence-associated gene signature in gastric cancer by integrating bulk and single-cell sequencing data.

It has been believed that immunosenescence plays a crucial role in tumorigenesis and cancer therapy. Nevertheless, there is still a lack of understanding regarding its role in determining clinical outcomes and therapy selection for gastric cancer patients, due to the lack of a feasible immunosenescence signature. Therefore, this research aims to develop a gene signature based on immunosenescence, which is used for stratification of gastric cancer. By integrative analysis of bulk transcriptome and single-cell data, we uncovered immunosenescence features in gastric cancer. Random forest algorithm was used to select hub genes and multivariate Cox algorithm was applied to construct a scoring system to evaluate the prognosis and the response to immunotherapy and chemotherapy. The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) cohort was implemented as the training cohort and two independent cohorts from the Gene Expression Omnibus (GEO) database were used for validation. The model was further tested by our Fudan cohort. In this study, immunosenescence was identified as a hallmark of gastric cancer that is linked with transcriptomic features, genomic variations, and distinctive tumor microenvironment (TME). Four immunosenescence genes, including APOD, ADIPOR2, BRAF, and C3, were screened out to construct a gene signature for risk stratification. Higher risk scores indicated strong predictive power for poorer overall survival. Notably, the risk score signature could reliably predict response to chemotherapy and immunotherapy, with patients with high scores benefiting from immunotherapy and patients with low scores responding to chemotherapy. We report immunosenescence as a hitherto unheralded hallmark of gastric cancer that affects prognosis and treatment efficiency.