Significant Predictor Of Prognosis Research Articles

Prognostic Value of Coronary Microvascular Dysfunction Assessed by Coronary Angiography-Derived Index of Microcirculatory Resistance in Patients With ST-Segment Elevation Myocardial Infarction.

CaIMR is proposed as a novel angiographic index designed to assess microcirculation without the need for pressure wires or hyperemic agents. We aimed to investigate the impact of caIMR on predicting clinical outcomes in STEMI patients. One hundred and forty patients with STEMI who received PCI in Putuo Hospital of Shanghai from October 2021 to September 2022 were categorized into CMD and non-CMD groups according to the caIMR value. The baseline information, patient-related examinations, and the occurrence of MACE at the 12-month follow-up were collected to investigate risk factors in patients with STEMI. We divided 140 patients with STEMI enrolled into two groups according to caIMR results, including 61 patients diagnosed with CMD and 79 patients diagnosed with non-CMD. A total of 21 MACE occurred during the 1 year of follow-up. Compared with non-CMD group, patients with CMD showed a significantly higher risk of MACE. A multivariate Cox regression model was conducted for the patients, and it was found thatcaIMR was a significant predictor of prognosis in STEMI patients (HR: 8.921). Patients with CMD were divided into culprit vascular CMD and non-culprit vascular CMD, and the result found that culprit vascular CMD was associated with the incidence of MACE (OR: 4.75) and heart failure (OR: 7.50). CaIMR is a strong predictor of clinical outcomes and can provide an objective risk stratification for patients with STEMI. There is a strong correlation among leukocyte index, the use of furosemide, Killips classification, and clinical outcomes.

Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer.

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC). Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram. Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy. We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.

Construction of a novel disulfidptosis-related lncRNAs signature for prognosis prediction and anti-tumor immunity in laryngeal squamous cell carcinoma

Disulfidptosis, an innovative type of controlled cellular death linked to metabolic dysfunction, has garnered attention. However, there is limited knowledge regarding the involvement of disulfidptosisrelated lnRNAs (DRlncRNAs) in laryngeal squamous cell carcinoma (LSCC). The objective of our team in this study seeks to establish a DRlncRNAs signature, investigate their prognostic value in LSCC, and explore their associations with immune cell subpopulations, biological signaling pathways, and exploring implications for drug sensitivity. We accessed LSCC patients’ RNA-seq data and pertinent clinical data for subsequent further analysis from The Cancer Genome Atlas (TCGA) portal. A literature search was conducted focusing on disulfidptosis-related genes. Pearson correlation coefficients were calculated to identify DRlncRNAs. Differential expression analysis of lncRNAs was performed. Utilizing univariate Cox regression analysis, we identified disulfidptosis-associated prognostic lncRNAs. The LASSO-Cox regression analysis was employed to refine this set of lncRNAs and construct a disulfidptosis-related lncRNAs signature. Various statistical techniques were employed to appraise model predictive performance. Subsequently, risk groups were stratified based on the risk score derived from the DRlncRNAs signature. The superiority of the risk score in prognostication over traditional clinicopathological features in LSCC patients was demonstrated. Evident distinctions emerged between risk groups, particularly in immune cell subpopulations like activated mast cells, eosinophils, and activated NK cells. Finally, the low-risk group demonstrated reduced IC50 values for specific chemotherapeutics like cisplatin and gemcitabine. The in vitro experiments indicated differential behavior of our DRlncRNAs. The DRlncRNAs signature can serve as a robust biomarker with the ability to predict both prognosis and therapeutic responses among patients with LSCC.

A hypoxia–glycolysis–lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BackgroundLiver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC.MethodsThis study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.ResultsWe classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.ConclusionThe hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

Identification of a DNA-methylome-based signature for prognosis prediction in driver gene-negative lung adenocarcinoma

“Driver gene-negative” lung adenocarcinoma (LUAD) was of rare treatment options and a poor prognosis. Presently, for them, few biomarkers are available for stratification analysis to make appropriate treatment strategy. This study aimed to develop a DNA-methylome-based signature to realize the precise risk-stratifying. Here, an Illumina MethylationEPIC Beadchip was applied to obtain differentially methylated CpG sites (DMCs). A four-CpG-based signature, named as TLA, was successfully established, whose prognosis-predicting power was well verified in one internal (n = 78) and other external (n = 110) validation cohorts. Patients with high-risk scores had shorter overall survival (OS) in all cohorts [hazard ratio (HR): 11.79, 5.16 and 2.99, respectively]. Additionally, it can effectively divide patients into low-risk and high-risk groups, with significantly different OS in the diverse subgroups stratified by the standard clinical parameters. As an independent prognostic factor, TLA may assist in improving the nomogram's 5-year OS-predicting ability (AUC 0.756, 95% CI:0.695–0.816), superior to TNM alone (AUC 0.644, 95% CI: 0.590–0.698). Additionally, the relationship of TLA-related genes, TAC1, LHX9, and ALX1, with prognosis and tumour invasion made them serve as potential therapy targets for driver gene-negative LUAD.

Abstract 7663: Identification of high-risk patients in multiple myeloma using a clonal gene signature

Abstract Introduction: Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients associated with poor prognosis. The identification of these patients is crucial for treatment management and strategic decisions. Methods: We developed a novel computational framework that only requires gene expression profiles to define prognostic gene signatures by selecting genes with expression driven by inferred clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of only 22 genes. This prognostic signature was evaluated in 5 independent datasets with gene expression data of a total of 2,155 MM samples. For each patient, we calculated a prognostic score based on the expression of the signature genes. Multivariate Cox regression analyses were performed to investigate the association of these signature scores with patient prognosis after adjusting established clinical factors. The performances of CGS and the only commercialized MM gene signatures, GEP70 and SKY92, in identifying high-risk patients were carefully compared by multiple different approaches. Results: Our results indicated that CGS provided significant prognostic values after adjusting for well-established prognostic factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). In addition, it showed significant prognostic associations in patient subsets stratified by these conventional prognostic factors. Importantly, CGS demonstrated a better performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures, which have been recommended for prognostic stratification in MM. Moreover, CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high-risk and low-risk groups already identified by GEP70 and SKY92. The higher CGS score of patients is significantly associated with poor response to dexamethasone, a commonly used drug for MM treatment. Conclusions: In summary, we proposed a computational framework that only requires gene expression data to identify clonal gene signatures for prognosis prediction. The CGS provides a useful biomarker for improving prognostic stratification in MM, especially for the identification of patients with the highest risk. The computational framework used for defining this signature provides a useful tool, which can be readily applied to many other cancer types. Citation Format: Jian-Rong Li, Christiana Wang, Chao Cheng. Identification of high-risk patients in multiple myeloma using a clonal gene signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7663.

Comprehensive analyses of solute carrier family members identify SLC12A2 as a novel therapy target for colorectal cancer

As the largest transporter family impacting on tumor genesis and development, the prognostic value of solute carrier (SLC) members has not been elucidated in colorectal cancer (CRC). We aimed to identify a prognostic signature from the SLC members and comprehensively analyze their roles in CRC. Firstly, we downloaded transcriptome data and clinical information of CRC samples from GEO (GSE39582) and TCGA as training and testing dataset, respectively. We extracted the expression matrix of SLC genes and established a prognostic model by univariate and multivariate Cox regression. Afterwards, the low-risk and high-risk group were identified. Then, the differences of prognosis traits, transcriptome features, clinical characteristics, immune infiltration and drug sensitivity between the two groups were explored. Furthermore, molecular subtyping was also implemented by non-negative matrix factorization (NMF). Finally, we studied the expression of the screened SLC genes in CRC tumor tissues and normal tissues as well as investigated the role of SLC12A2 by loss of function and gain of function. As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2). Both in the training and testing sets, CRC patients in the high-risk group had the poorer prognosis and were in the more advanced pathological stage. What’s more, the high-risk group were enriched with CRC progression signatures and immune infiltration. Two groups showed different drug sensitivity. On the other hand, two distinct subclasses (C1 and C2) were identified based on the 6 SLC genes. CRC patients in the high-risk group and C1 subtype had a worse prognosis. Furthermore, we found and validated that SLC12A2 was steadily upregulated in CRC. A loss-of-function study showed that knockdown of SLC12A2 expression restrained proliferation and stemness of CRC cells while a gain-of-function study showed the contrary results. Hence, we provided a 6-SLC gene signature for prognosis prediction of CRC patients. At the same time, we identified that SLC12A2 could promote tumor progression in CRC, which may serve as a potential therapeutic target.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer.

Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body's anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments. GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells. This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

Exploration and validation of a combined Hypoxia and m6A/m5C/m1A regulated gene signature for prognosis prediction of liver cancer

BackgroundIt is widely acknowledged that hypoxia and m6A/m5C/m1A RNA modifications promote the occurrence and development of tumors by regulating the tumor microenvironment. This study aimed to establish a novel liver cancer risk signature based on hypoxia and m6A/m5C/m1A modifications.MethodsWe collected data from The Cancer Genome Atlas (TCGA-LIHC), the National Omics Data Encyclopedia (NODE-HCC), the International Cancer Genome Consortium (ICGC), and the Gene Expression Omnibus (GEO) databases for our study (GSE59729, GSE41666). Using Cox regression and least absolute shrinkage and selection operator (LASSO) method, we developed a risk signature for liver cancer based on differentially expressed genes related to hypoxia and genes regulated by m6A/m5C/m1A modifications. We stratified patients into high- and low-risk groups and assessed differences between these groups in terms of gene mutations, copy number variations, pathway enrichment, stemness scores, immune infiltration, and predictive capabilities of the model for immunotherapy and chemotherapy efficacy.ResultsOur analysis revealed a significantly correlated between hypoxia and methylation as well as m6A/m5C/m1A RNA methylation. The three-gene prognosis signature (CEP55, DPH2, SMS) combining hypoxia and m6A/m5C/m1A regulated genes exhibited strong predictive performance in TCGA-LIHC, NODE-HCC, and ICGC-LIHC-JP cohorts. The low-risk group demonstrated a significantly better overall survival compared to the high-risk group (p < 0.0001 in TCGA, p = 0.0043 in NODE, p = 0.0015 in ICGC). The area under the curve (AUC) values for survival at 1, 2, and 3 years are all greater than 0.65 in the three cohorts. Univariate and Multivariate Cox regression analyses of the three datasets indicated that the signature could serve as an independent prognostic predictor (p < 0.001 in the three cohorts). The high-risk group exhibited more genome changes and higher homologous recombination deficiency scores and stemness scores. Analysis of immune infiltration and immune activation confirmed that the signature was associated with various immune microenvironment characteristics. Finally, patients in the high-risk group experienced a more favorable response to immunotherapy, and various common chemotherapy drugs.ConclusionOur prognostic signature which integrates hypoxia and m6A/m5C/m1A-regulated genes, provides valuable insights for clinical prediction and treatment guidance for liver cancer patients.

A pilot study of S100A4, S100A8/A9, and S100A12 in dilated cardiomyopathy: novel biomarkers for diagnosis or prognosis?

Circulating biomarkers can provide important information for the diagnosis and prognosis of dilated cardiomyopathy (DCM). We explored novel biomarkers for the diagnosis and prognosis of DCM to improve clinical decision-making. A total of 238 DCM patients and 65 control were consecutively enrolled at Zhongshan Hospital between January 2017 and January 2019. In the screening set, four DCM patients and four controls underwent measurements of serum proteomic analysis. Seventy-six differentially expressed circulating proteins were screened by data-independent acquisition proteomics, and three of these proteins (S100A4, S100A8/A9, and S100A12) were validated by multiple-reaction monitoring-mass spectrometry. In the validation set, subsequently, a total of 234 DCM patients and 61 control subjects were evaluated by enzyme-linked immunosorbent assay. Circulating S100A4, S100A8/A9, and S100A12 were significantly increased in DCM patients (P<0.001). These three proteins were significant positively correlated with other parameters, such as Lg (NT-proBNP), IL-1β, TGF-β, CRP, left ventricular end-diastolic diameter, and left ventricular end-systolic diameter, whereas they were negatively correlated with left ventricular ejection fraction, respectively (P<0.05). The receiver operator characteristic curve showed the combination of S100A4, S100A8/A9, and S100A12 [area under curve (AUC) 0.88, 95% confidence interval (CI) 0.84-0.93] was better than single S100A4 (AUC 0.74, 95% CI 0.68-0.81), S100A8/A9 (AUC 0.82, 95% CI 0.77-0.88), or S100A12 (AUC 0.80, 95% CI 0.72-0.88) in the diagnosis of DCM (P<0.01). After a median follow-up period of 33.5months, 110 patients (47.01%) experienced major adverse cardiac events (MACEs), including 46 who had cardiac deaths and 64 who had heart failure rehospitalizations. Kaplan-Meier analysis indicated that the DCM patients with ≥75th percentile level of S100A4 had a significantly higher incidence of MACEs than those with <75th percentile level of S100A4 (61.40% vs. 42.37%, P<0.05). There were no significant differences of MACE rate among DCM patients with different concentrations of S100A8/A9 and S100A12 (P>0.05). Cox proportional hazards regression analysis revealed that S100A4 [≥75th percentile vs. <75th percentile: hazard ratio (HR) 1.65; 95% CI 1.11-2.45] remained significant independent predictors for MACEs (P<0.05); however, S100A8/A9 and S100A12 were not independent factors for predicting MACE (P≥0.05). S100A4, S100A8/A9, and S100A12 may be additional diagnostic tools for human DCM recognition, and the combination of these three indicators helped to improve the accuracy of a single index to diagnose DCM. Additionally, S100A4 was identified as a significant predictor of prognosis in patients with DCM.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

backgroundStomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.MethodsWe collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.ResultsThe 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.ConclusionOur study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

An immunogenic cell death-related signature for prediction of prognosis and response to immunotherapy in breast cancer.

An immunogenic cell death-related signature for prediction of prognosis and response to immunotherapy in breast cancer.

Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes.

As a complex group of malignancies, head and neck squamous cell carcinoma (HNSC) is one of the leading causes of cancer mortality. This study aims to establish a reliable clinical classification and gene signature for HNSC prognostic prediction and precision treatments. A consensus clustering analysis was performed to group HNSC patients in The Cancer Genome Atlas (TCGA) database based on genes linked to programmed cell death (PCD). Differentially expressed genes (DEGs) between subtypes were identified using the "limma" R package. The TCGA prognostic signature and PCD-related prognostic genes were found using a least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis. The robustness of the LASSO analysis was validated using datasets GSE65858 and GSE41613. A cell counting kit-8 (CCK-8) test, Western blot, and real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were used to evaluate the expression and viability of prognostic genes. Four molecular subtypes were identified in PCD-related genes. Subtype C4 had the best prognosis and the highest immune score, while subtype C1 exhibited the most unfavorable outcomes. Three hundred shared DEGs were identified among the four subtypes, and four prognostic genes (CTLA4, CAMK2N1, PLAU and CALML5) were used to construct a TCGA-HNSC prognostic model. High-risk patients manifested poorer prognosis, more inflammatory pathway enrichment, and lower immune cell infiltration. High-risk patients were more prone to immune escape and were more likely to be resistant to Cisplatin and 5-Fluorouracil. Prognosis prediction was validated in external datasets. The expression of CTLA4, CAMK2N1, PLAU and CALML5 was enhanced in CAL-27 and SCC-25 cell lines, and CALML5 inhibited CAL-27 and SCC-25 cell viability. This study shares novel insights into HNSC classification and provides a reliable PCD-related prognostic signature for prognosis prediction and treatment for patients with HNSC.

Construction and validation of a transient receptor potential-related long noncoding RNA signature for prognosis prediction in breast cancer patients.

Breast cancer (BC) is the most commonly diagnosed malignancy in women around the world. Accumulating evidence suggests that transient receptor potential (TRP) channels play a significant role in tumor progression and immune cell infiltration. Hence, we conducted the study to investigate the correlation between TRP-associated lncRNAs and the prognosis of breast carcinoma. In the current study, 33 TRP-associated genes were selected from a review published by Amrita Samanta et al, and the TRP-related lncRNAs were identified by Pearson analysis. Based on the sum of the expression levels of 12 lncRNAs provided by the Cancer Genome Atlas (TCGA), a TRP-associated lncRNA signature was established by using Cox regression analysis. According to the median value of the risk score in the training set, BC patients were separated into high- and low-risk groups. Subsequently, functional enrichment analysis was conducted on the differential expression genes (DEGs) between different risk groups. The Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression (ESTIMATE) Score was calculated by ESTIMATE, and the immune cell infiltration was evaluated by ssGSEA. Finally, the immune checkpoint gene expression levels, microsatellite instability (MSI), and immunophenoscore (IPS) were further assessed. The high-risk groups exhibited lower survival rates, while the low-risk groups showed higher survival rates. As a result, the DEGs between different risk groups were highly enriched in immune cell activation and immunoregulation. Besides, the ESTIMATE scores of patients in low-risk groups were higher than those in high-risk groups. The infiltration levels of several immune cells were remarkably elevated in low-risk groups, and various immune signatures were activated with a decreased risk score. Eventually, the TRP-associated lncRNA signature was confirmed with a highly potential ability to evaluate the immunotherapy response in breast carcinoma patients. The outcomes of the current study indicated that the 12-TRP-associated-lncRNA risk model was an independent prognostic risk factor for BC patients. This risk model could be closely related to the tumor immune microenvironment in BC. Our findings will provide new insights for future immunotherapy for BC treatment.

A novel hypoxia-associated gene signature for prognosis prediction in head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of head and neck, which seriously threatens human life and health. However, the mechanism of hypoxia-associated genes (HAGs) in HNSCC remains unelucidated. This study aims to establish a hypoxia-associated gene signature and the nomogram for predicting the prognosis of patients with HNSCC.MethodsPrevious literature reports provided a list of HAGs. The TCGA database provided genetic and clinical information on HNSCC patients. First, a hypoxia-associated gene risk model was constructed for predicting overall survival (OS) in HNSCC patients and externally validated in four GEO datasets (GSE27020, GSE41613, GSE42743, and GSE117973). Then, immune status and metabolic pathways were analyzed. A nomogram was constructed and assessed the predictive value. Finally, experimental validation of the core genes was performed by qRT-PCR.ResultsA HNSCC prognostic model was constructed based on 8 HAGs. This risk model was validated in four external datasets and exhibited high predictive value in various clinical subgroups. Significant differences in immune cell infiltration levels and metabolic pathways were found between high and low risk subgroups. The nomogram was highly accurate for predicting OS in HNSCC patients.ConclusionsThe 8 hypoxia-associated gene signature can serve as novel independent prognostic indicators in HNSCC patients. The nomogram combining the risk score and clinical stage enhanced predictive performance in predicting OS compared to the risk model and clinical characteristics alone.

Layer-specific circumferential strain using fast strain-encoded cardiac magnetic resonance imaging in acute myocardial injury - a prospective study of diagnostic and prognostic accuracy

Abstract Background In patients presenting acutely with chest pain or shortness of breath, an acute myocardial injury diagnosed through modern troponin assays is common. Yet, a differential diagnosis is often demanding and must weigh the timely treatment of an ischemic cause against the risk of an invasive procedure without intervention. Modern cardiac magnetic resonance (CMR) strain sequences such as fast-SENC (fSENC) might support clinical decision-making in identifying predictors of myocardial dysfunction (1). Furthermore, their resolution should be able to differentiate epicardial from endocardial function impairment. This study sought to assess a layer-specific strain approach's diagnostic and prognostic accuracy using circumferential strain. Methods In this board-approved prospective observational study, patients from the emergency department fulfilling rule-in criteria for NSTEMI according to current guidelines received an ultra-fast fSENC CMR independently of standard care (2,3). fSENC was acquired in a single heartbeat acquisition, and global/segmental circumferential strain (GCS) as well as dysfunctional segments were measured. GCS was measured in two layers (epicardial/endocardial = GCSepi/GCSendo). Patients were followed up for two years for the occurrence of cardiovascular death and divided into groups according to their final diagnosis upon discharge. Results 64 patients (51 male) were included, consisting of 25 with NSTEMI, 16 with myocarditis and 23 with other causes of myocardial injury (OMI). GCS was reduced in all patients but with no significant difference between groups (NSTEMI: -14.3±3.8; myocarditis: 15.9±4.6; OMI: 13.9±3.8; p&amp;gt;0.05). GCSendo (-16.3±4.0) was generally lower than GCSepi (-12.5±3.1) in all groups (p &amp;lt; 0.001), but did not differ between groups (p&amp;gt;0.05). Overall, 2-year mortality was low (4.7%). Nevertheless, GCSendo predicted outcome (p = 0.03), unlike GCS. 2-year survival in patients with normal GCS (&amp;gt;-17%) and ≤5 dysfunctional segments was 100.0%. Conclusions Layer-specific circumferential strain is a significant predictor of prognosis. Endo- and epicardial strain is reduced in all types of myocardial injury. For the identification of underlying pathology however, layer-specific circumferential strain did not add incremental value.

Development and validation of an m6A RNA methylation regulator-based signature for the prediction of prognosis and immunotherapy in cutaneous melanoma.

Development and validation of an m6A RNA methylation regulator-based signature for the prediction of prognosis and immunotherapy in cutaneous melanoma.

P2.05-18 A Seven-Lnc RNA Signature for Prognosis Prediction of Patients with Lung Squamous Cell Carcinoma through Tumor Immune Escape

P2.05-18 A Seven-Lnc RNA Signature for Prognosis Prediction of Patients with Lung Squamous Cell Carcinoma through Tumor Immune Escape

Identification of specific prognostic markers for lung squamous cell carcinoma based on tumor progression, immune infiltration, and stem index.

Lung squamous cell carcinoma (LUSC) is a unique subform of nonsmall cell lung cancer (NSCLC). The lack of specific driver genes as therapeutic targets leads to worse prognoses in patients with LUSC, even with chemotherapy, radiotherapy, or immune checkpoint inhibitors. Furthermore, research on the LUSC-specific prognosis genes is lacking. This study aimed to develop a comprehensive LUSC-specific differentially expressed genes (DEGs) signature for prognosis correlated with tumor progression, immune infiltration,and stem index. RNA sequencing data for LUSC and lung adenocarcinoma (LUAD) were extracted from The Cancer Genome Atlas (TCGA) data portal, and DEGs analyses were conducted in TCGA-LUSC and TCGA-LUAD cohorts to identify specific DEGs associated with LUSC. Functional analysis and protein-protein interaction network were performed to annotate the roles of LUSC-specific DEGs and select the top 100 LUSC-specific DEGs. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were performed to select prognosis-related DEGs. Overall, 1,604 LUSC-specific DEGs were obtained, and a validated seven-gene signature was constructed comprising FGG, C3, FGA, JUN, CST3, CPSF4, and HIST1H2BH. FGG, C3, FGA, JUN, and CST3 were correlated with poor LUSC prognosis, whereas CPSF4 and HIST1H2BH were potential positive prognosis markers in patients with LUSC. Receiver operating characteristic analysis further confirmed that the genetic profile could accurately estimate the overall survival of LUSC patients. Analysis of immune infiltration demonstrated that the high risk (HR) LUSC patients exhibited accelerated tumor infiltration, relative to low risk (LR) LUSC patients. Molecular expressions of immune checkpoint genes differed significantly between the HR and LR cohorts. A ceRNA network containing 19 lncRNAs, 50 miRNAs, and 7 prognostic DEGs was constructed to demonstrate the prognostic value of novel biomarkers of LUSC-specific DEGs based on tumor progression, stemindex, and immune infiltration. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. Our study demonstrated the potential clinical implication of the 7- DEGs signature for prognosis prediction of LUSC patients based on tumor progression, immune infiltration, and stem index. And the FGG could be an independent prognostic biomarker of LUSC promoting cell proliferation, migration, invasion, THP-1 cell infiltration, and stem cell maintenance.