Median Risk Score Research Articles

Identification of prognostic and diagnostic signatures for cancer and acute myocardial infarction: multi-omics approaches for deciphering heterogeneity to enhance patient management.

Patients diagnosed with cancer face an increased risk of cardiovascular events in the short term, while those experiencing acute myocardial infarction (AMI) have a higher incidence of cancer. Given limitations in clinical resources, identifying shared biomarkers offers a cost-effective approach to risk assessment by minimizing the need for multiple tests and screenings. Hence, it is crucial to identify common biomarkers for both cancer survival and AMI prediction. Our study suggests that monocyte-derived biomarkers, specifically WEE1, PYHIN1, SEC61A2, and HAL, hold potential as predictors for cancer prognosis and AMI. We employed a novel formula to analyze mRNA levels in clinical samples from patients with AMI and cancer, resulting in the development of a new risk score based on expression profiles. By categorizing patients into high-risk and low-risk groups based on the median risk score, we observed significantly poorer overall survival among high-risk patients in cancer cohorts using Kaplan-Meier analysis. Furthermore, calibration curves, decision curve analysis (DCA), and clinical impact curve analyses provided additional evidence supporting the robust diagnostic capacity of the risk score for AMI. Noteworthy is the shared activation of the Notch Signaling pathway, which may shed light on common high-risk factors underlying both AMI and cancer. Additionally, we validated the differential expression of these genes in cell lines and clinical samples, respectively, reinforcing their potential as meaningful biomarkers. In conclusion, our study demonstrates the promise of mRNA levels as biomarkers and emphasizes the significance of further research for validation and refinement.

Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data

BackgroundGene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.MethodsRadio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The “DESeq2” R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.ResultsBased on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan–Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.ConclusionOur study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.

Stroke Riskometer Application (SRA™) influence on lifestyle changes of home bound familial Malaysian stroke caregivers: a randomised controlled trial in a primary care based longer term stroke care facility

BackgroundIn countries where access to Specialist stroke care services are limited, primary care physicians often manage stroke patients and the caregiving family members. This study aimed to evaluate the impact of Stroke Riskometer Application (SRA™) on promoting healthier lifestyles among familial stroke caregivers for primary prevention.MethodsA parallel, open-label, 2-arm prospective, pilot randomised controlled trial was conducted at a long-term stroke service at a university based primary care clinic. All stroke caregivers aged ≥ 18 years, proficient in English or Malay and smartphone operation were invited. From 147 eligible caregivers, 76 participants were randomised to either SRA™ intervention or conventional care group (CCG) after receiving standard health counselling. The intervention group had additional SRA™ installed on their smartphones, which enabled self-monitoring of modifiable and non-modifiable stroke risk factors. The Stroke Riskometer app (SRATM) and Life's Simple 7 (LS7) questionnaires assessed stroke risk and lifestyle practices. Changes in clinical profile, lifestyle practices and calculated stroke risk were analysed at baseline and 3 months.The trial was registered in the Australia-New Zealand Clinical Trial Registry, ACTRN12618002050235.ResultsThe demographic and clinical characteristics of the intervention and control group study participants were comparable. Better improvement in LS7 scores were noted in the SRA™ arm compared to CCG at 3 months: Median difference (95% CI) = 0.88 (1.68–0.08), p = 0.03. However, both groups did not show significant changes in median stroke risk and relative risk scores at 5-, 10-years (Stroke risk 5-years: Median difference (95% CI) = 0.53 (0.15–1.21), p = 0.13, 10-years: Median difference (95% CI) = 0.81 (0.53–2.15), p = 0.23; Relative risk 5-years: Median difference (95% CI) = 0.84 (0.29–1.97), p = 0.14, Relative risk 10-years: Median difference (95% CI) = 0.58 (0.36–1.52), p = 0.23).ConclusionSRA™ is a useful tool for familial stroke caregivers to make lifestyle changes, although it did not reduce personal or relative stroke risk after 3 months usage.Trial registrationNo: ACTRN12618002050235 (Registration Date: 21st December 2018).

Validation of prediction algorithm for risk estimation of intracranial aneurysm development using real-world data

Intracranial aneurysm (IA) is difficult to detect, and most patients remain undiagnosed, as screening tests have potential risks and high costs. Thus, it is important to develop risk assessment system for efficient and safe screening strategy. Through previously published research, we have developed a prediction model for the incidence risk of IA using cohort observational data. This study was designed to verify whether such a prediction model also demonstrates sufficient clinical performance in predicting the prevalence risk at the point of health screening, using cross-sectional data. The study population comprised individuals who visited the Chonnam National University Hwasun Hospital Health Promotion Center in Korea for voluntary medical checkups between 2007 and 2019. All participants had no history of cerebrovascular disease and underwent brain CTA for screening purpose. Presence of IA was evaluated by two specialized radiologists. The risk score was calculated using the previously developed AI model, and 0 point represents the lowest risk and 100 point represents the highest risk. To compare the prevalence according to the risk, age-sex standardization using national database was performed. A study collected data from 5942 health examinations, including brain CTA data, with participants ranging from 20 to 87 years old and a mean age of 52 years. The age-sex standardized prevalence of IA was 3.20%. The prevalence in each risk group was 0.18% (lowest risk, 0–19), 2.12% (lower risk, 20–39), 2.37% (mid-risk, 40–59), 4.00% (higher risk, 60–79), and 6.44% (highest risk, 80–100). The odds ratio between the lowest and highest risk groups was 38.50. The adjusted proportions of IA patients in the higher and highest risk groups were 26.7% and 44.5%, respectively. The median risk scores among IA patients and normal participants were 74 and 54, respectively. The optimal cut-off risk score was 60.5 with an area under the curve of 0.70. We have confirmed that the incidence risk prediction model built through machine learning also shows viable clinical performance in predicting prevalence risk. By utilizing this prediction system, we can effectively predict not only the incidence risk but also the prevalence risk, which is the probability of already having the disease, using health screening data. This may enable us to consider strategies for the early detection of intracranial aneurysms.

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.

Construction of a New Prognostic Model for Oral Squamous Cell Carcinoma Based on Telomere-Related Genes

We investigated the prognostic value of telomere-related genes in oral squamous cell carcinoma (OSCC) using the TCGA-OSCC dataset and GSE41613 external validation set. We identified differentially expressed genes (DEGs) between OSCC and control samples and intersected them with telomere-related genes. Three risk model genes (IGF2BP2, EIF5A2, and PLOD2) were obtained through Cox and LASSO analyses. A risk model was constructed based on the expression of these genes. The OSCC samples were divided into high and low-risk groups using the median risk score. Univariate and multivariate Cox analyses identified risk score and age as independent prognostic factors. Gene set enrichment analysis revealed enrichment in chemical stimulus and appendage development pathways. We constructed a transcription factor (TF)-mRNA network involving two mRNAs (EIF5A2, PLOD2) and 17 TFs, including STAT1-EIF5A2 and TEAD1-EIF5A2. Immune-infiltration analysis showed significant differences in the abundance of 11 immune cells between the high and low-risk groups, including T cells CD8, activated mast cells, and macrophages M0. Our findings contribute to the development of a telomere-related risk model (including IGF2BP2, EIF5A2, and PLOD2) for predicting the prognosis of OSCC, providing new insights for further studies in this area.

Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures

BackgroundLung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance. MethodsMultiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed. ResultsThe kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lasso-penalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration. ConclusionThis study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.

Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors. MethodsThe study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted. ResultsIn this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score. ConclusionThis study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy.

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.

Prognostic characteristics of T-cell mediated cell killing-related genes in lung adenocarcinoma

Constituted by various heterogeneous cells, the tumor microenvironment (TME) is capable of promoting tumor proliferation, invasion, and metastasis through extensive crosstalk. The pivotal factor influencing the survival time of patients and their response to immunotherapy lies in the intratumoral immune environment. We obtained 112 differential genes related to T cell-mediated tumor killing in LUAD by employing bioinformatics analysis on the basis of the TCGA and TISIDB databases. Then the 6-gene prognostic risk score model (CA9, OIP5, TIMP1, SEC11C, FURIN, and TLR10) was constructed by conducting univariate LASSO as well as multivariate Cox regression analyses. The median risk score was taken as the threshold to classify the samples into two groups. Survival analysis revealed that the low-risk group exhibited a more favorable prognosis. Subsequently, the Cox regression analysis combined with clinical information (age, gender, and pathological stage) and the risk score of LUAD patients demonstrated the potential of this model as an independent prognostic factor. The nomogram established based on clinical information and a risk score in combination with the calibration curve indicated that this model had good predictive ability. Notable enrichment of the differential genes from the high- and low-risk groups was discovered in immune-associated processes or pathways, as shown by the GO and KEGG enrichment analyses. The combined use of single-sample gene enrichment analysis (ssGSEA) and immunophenoscore (IPS) demonstrated heightened immune infiltration and IPS scores in the low-risk group, indicating that immunotherapy was likely to show good efficacy in patients from this group. To sum up, the prognostic model of LUAD constructed based on T-cell-mediated cell killing-related genes was not only capable of screening the prognosis of LUAD patients but was also used for screening those LUAD patients with high sensitivity to immunotherapy. Our study offered novel insights into the clinical treatment and prognostic prediction of LUAD patients.

Single-Cell RNA Sequencing Pro-angiogenic Macrophage Profiles Reveal Novel Prognostic Biomarkers and Therapeutic Targets for Osteosarcoma.

Osteosarcoma (OS) is a malignant bone tumor that most commonly occurs in children and adolescents. OS patients have a poor prognosis, and 5-year survival rates have rarely improved significantly over the past few decades. OS prognosis may be related to the infiltration of tumor-associated macrophages (TAMs). However, the role of proangiogenic macrophages, a subtype of TAMs, in OS prognosis has not been reported. In this study, seven subtypes of TAMs were identified from single-cell RNA sequencing (scRNA-seq) data that we propose defining as proangiogenic TAMs (Angio-TAMs), interferon-primed TAMs (IFN-TAMs), inflammatory cytokine-enriched TAMs (Inflam-TAMs), immune regulatory TAMs (Reg-TAMs), lipid-associated TAMs (LA-TAMs), and resident-tissue macrophages like TAMs (RTM-TAMs) (containing two subcellular types). In the survival analysis of each macrophage subtype, it was found that patients with Angio-TAMs had the most significant difference in survival. Eight genes associated with Angio-TAMs were obtained by differential expression analysis, and these genes were built into a prognostic model using the LASSO algorithm. Clinical OS case samples were categorized into high-risk and low-risk subgroups using median risk scores. In comparison to the low-risk subgroup, the survival time of the high-risk subgroup was much shorter. Additional studies on immune cell infiltration and immune checkpoint molecule expression in the two risk subgroups were carried out. In immunotherapy response prediction, the Angio-TAM-associated gene risk signature was found to be negatively correlated with immune checkpoint responses. In addition, the associated enriched GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were mainly involved in the malignant progression of tumors. As suggested by these findings, the Angio-TAM gene risk signature may be an underlying prognostic biomarker and novel therapeutic target for OS patients.Kindly check and confirm whether the ESM file is correctly identifiedWe have checked this file and confirmed that it can be correctly identified.

Analysis between macrophage-related genes with prognosis and tumor microenvironment in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a highly morbid and fatal disease that exhibits individualized differences in prognosis and drug efficacy. Therefore, understanding the molecular mechanism of the occurrence and progression of lung cancer can improve early diagnosis, treatment and prognosis. Macrophages are a crucial component of the tumor microenvironment (TME) due to their high plasticity and heterogeneity. They play a multifaceted role in tumor initiation and progression. In order to elucidate the pathogenesis of tumor-associated macrophages (TAMs) related genes in NSCLC, transcriptomic sequencing, univariate COX regression, LASSO regression and multivariate COX regression analyses were conducted to identify the 11 genes that have the most significant association with prognosis. These genes include FCRLA, LDHA, LMOD3, MAP3K8, NT5E, PDGFB, S100P, SFXN1, TDRD1, TFAP2A and TUBB6. The risk score (RS) was computed, and all samples were split into high- and low-risk groups based on the median RS. The correlation of RS and 11 genes with macrophages was verified by the CIBERSORT deconvolution algorithm. These above results suggest that the risk score developed in this study can be utilized for predicting patients' prognosis and evaluating their immune infiltration status. This study can serve as a guide for subsequent tumor immunotherapy and gene targeting therapy.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma.

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC). Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments. This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration. PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes.

Cellular senescence occurs throughout life and can play beneficial roles in a variety of physiological processes, including embryonic development, tissue repair, and tumor suppression. However, the relationship between cellular senescence-related genes (CSRGs) and immunotherapy in esophageal carcinoma (ECa) remains poorly defined. The data set used in the analysis was retrieved from TCGA (Research Resource Identifier (RRID): SCR_003193), GEO (RRID: SCR_005012), and CellAge databases. Data processing, statistical analysis, and diagram formation were conducted in R software (RRID: SCR_001905) and GraphPad Prism (RRID: SCR_002798). Based on CSRGs, we used the TCGA database to construct a prognostic signature for ECa and then validated it in the GEO database. The predictive efficiency of the signature was evaluated using receiver operating characteristic (ROC) curves, Cox regression analysis, nomogram, and calibration curves. According to the median risk score derived from CSRGs, patients with ECa were divided into high- and low-risk groups. Immune infiltration and immunotherapy were also analyzed between the two risk groups. Finally, the hub genes of the differences between the two risk groups were identified by the STRING (RRID: SCR_005223) database and Cytoscape (RRID: SCR_003032) software. A six-gene risk signature (DEK, RUNX1, SMARCA4, SREBF1, TERT, and TOP1) was constructed in the TCGA database. Patients in the high-risk group had a worse overall survival (OS) was disclosed by survival analysis. As expected, the signature presented equally prognostic significance in the GSE53624 cohort. Next, the Area Under ROC Curve (AUC=0.854) and multivariate Cox regression analysis (HR=3.381, 2.073-5.514, P<0.001) also proved that the risk signature has a high predictive ability. Furthermore, we can more accurately predict the prognosis of patients with ECa by nomogram constructed by risk score. The result of the TIDE algorithm showed that ECa patients in the high-risk group had a greater possibility of immune escape. At last, a total of ten hub genes (APOA1, MUC5AC, GC, APOA4, AMBP, FABP1, APOA2, SOX2, MUC8, MUC17) between two risk groups with the highest interaction degrees were identified. By further analysis, four hub genes (APOA4, AMBP, FABP1, and APOA2) were related to the survival differences of ECa. Our study reveals comprehensive clues that a novel signature based on CSRGs may provide reliable prognosis prediction and insight into new therapy for patients with ECa.

Clinical neutrophil-related gene helps treat bladder urothelial carcinoma

Abstract 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.

Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas.

Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas. The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature. The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells. Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes.

To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRG). The clinical information and the expression of ICRG mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and real-time fluorescence quantitative PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median of risk score. The immune cell infiltration abundance was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic curve (ROC curve) were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed. By comparing the expression levels of ICRG in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were screened out, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve (AUC) of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRG-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration levels of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-and 5-year overall survival rate of bladder cancer patients. The study shows the potential value of ICRG in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRG risk score has high accuracy in predicting the prognosis of bladder cancer patients.

Establishing a prognostic model with ferroptosis-related long non-coding RNAs in bladder cancer.

Ferroptosis is a distinct form of cell death that has the potential to supersede the drug resistance that is commonly observed with current chemotherapeutic agents. As a result, ferroptosis presents a new and innovative therapeutic pathway for cancer treatment. The current understanding regarding the expression of genes associated with ferroptosis in bladder cancer (BLCA) and their prognostic implications remains unclear. Consequently, this study aimed to examine the potential prognostic value of ferroptosis-associated long non-coding RNAs (lncRNAs) in BLCA. The Cancer Genome Atlas (TCGA) was accessed to download RNA sequencing data and clinicopathological features of BLCA while accessing the FerrDb database to download ferroptosis-associated genes. The study calculated risk scores for ferroptosis-associated lncRNAs, and subsequently divided patients with BLCA into two groups, namely high- and low-risk, on the basis of the median risk score. Moreover, Kaplan-Meier (K-M) curves, Cox regression analysis, and column plots were utilized for evaluating the risk score prognostic value. Subsequently, the involvement of ferroptosis-associated mRNA, N6-methyladenosine (m6A) mRNA status, and immune responses was investigated for BLCA prognosis. Thirty-six lncRNAs were identified to be differently expressed and linked to the prognosis of BLCA. The findings from the K-M curve analysis indicated a significant association between a high-risk lncRNA profile and poor BLCA prognosis. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.810. The risk assessment model exhibited superior performance in predicting prognosis for BLCA compared to conventional clinicopathological features. Thirty-six lncRNAs were found to be linked to ferroptosis for the prognosis of patients with BLCA, and these results may provide new insights for treating BLCA.

A novel tetraspanin-related gene signature for predicting prognosis and immune invasion status of lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD), the most common subtype of lung cancer, is the primary contributor to cancer-linked fatalities. Dysregulation in the proliferation of cells and death is primarily involved in its development. Recently, tetraspanins, a group of transmembrane proteins, have gained increasing attention for their potential role in the progression of LUAD. Hence, our endeavor involved the development of a novel tetraspanin-based model for the prognostication of lung cancer.MethodsA comprehensive set of bioinformatics tools was utilized to evaluate the expression of tetraspanin-related genes and assess their significance regarding prognosis. Hence, a robust risk signature was established through machine learning. The prognosis-predictive value of the signature was evaluated in terms of clinical application, functional enrichment, and the immune landscape.ResultsThe research first identified differential expression of tetraspanin genes in patients with LUAD via publicly available databases. The resulting data were indicative of the value that nine of them held regarding prognosis. Five distinct elements were utilized in the establishment of a tetraspanin-related model (TSPAN7, TSPAN11, TSPAN14, UPK1B, and UPK1A). Furthermore, as per the median risk scores, the participants were classified into high- and low-risk groups. The model was validated using inner and outer validation sets. Notably, consensus clustering and prognostic score grouping analysis revealed that tetraspanin-related features affect tumor prognosis by modulating tumor immunity. A nomogram based on the tetraspanin gene was constructed with the aim of enhancing the poor prognosis of high-risk groups and facilitating clinical application.ConclusionThrough machine learning algorithms and in vitro experiments, a novel tetraspanin-associated signature was developed and validated for survival prediction in patients with LUAD that reflects tumor immune infiltration. This could potentially provide new and improved measures for diagnosis and therapeutic interventions for LUAD.

The activity of cuproptosis pathway calculated by AUCell algorithm was employed to construct cuproptosis landscape in lung adenocarcinoma

Cuproptosis is a recently described copper-dependent cell death pathway. Consequently, there are still few studies on lung adenocarcinoma (LUAD)-related cuproptosis, and we aimed to deepen in this matter. In this study, data from 503 patients with lung cancer from the TCGA-LUAD cohort data collection and 11 LUAD single-cells from GSE131907 as well as from 10 genes associated with cuproptosis were analyzed. The AUCell R package was used to determine the copper-dependent cell death pathway activity for each cell subpopulation, calculate the CellChat score, and display cell communication for each cell subpopulation. The PROGENy score was calculated to show the scores of tumor-related pathways in different cell populations. GO and KEGG analyses were used to calculate pathway activity. Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. The ssGSEA and xCell algorithms were used to calculate the immunocyte infiltration score. Based on data from the GDSC database, the drug sensitivity score was calculated using oncoPredict. Finally, in vitro experiments were performed to determine the role of TLE1, the most important gene in the prognostic model. The 11 LUAD single-cell samples were classified into 8 different cell populations, from which epithelial cells showed the highest copper-dependent cell death pathway activity. Epithelial cell subsets were significantly positively correlated with MAKP, hypoxia, and other pathways. In addition, cell subgroup communication showed highly active collagen and APP pathways. Using the Findmark algorithm, differentially expressed genes (DEGs) between epithelial and other cell types were identified. Combined with the bulk data in the TCGA-LUAD database, DEGs were enriched in pathways such as EGFR tyrosine kinase inhibitor resistance, Hippo signaling pathway, and tight junction. Subsequently, we selected 4 genes (out of 112) with prognostic significance, ANKRD29, RHOV, TLE1, and NPAS2, and used them to construct a prognostic model. The high- and low-risk groups, distinguished by the median risk score, showed significantly different prognoses. Finally, we chose TLE1 as a biomarker based on the relative importance score in the prognostic model. In vitro experiments showed that TLE1 promotes tumor proliferation and migration and inhibits apoptosis.