Prognostic Risk Model Research Articles

Identification and validation of basement membrane-associated gene AGRN as prognostic and immune-associated biomarkers in colorectal cancer patients.

Colorectal cancer (COCA) has a poor prognosis, with growing evidence implicating basement membranes (BMs) in cancer progression. Our goal was to investigate the role and predictive significance of BMs in COCA patients. We obtained BMs-related genes from cutting-edge research and used TCGA and GTEx databases for mRNA expression and patient information. Cox regression and LASSO regression were used for prognostic gene selection and risk model construction. We compared prognosis using Kaplan-Meier analysis and examined drug sensitivity differences. The CMAP dataset identified potential small molecule drugs. Invitro tests involved suppressing a crucial gene to observe its impact on tumour metastasis. We developed a 12 BMs-based approach, finding it to be an independent prognostic factor. Functional analysis showed BMs concentrated in cancer-associated pathways, correlating with immune cell infiltration and immune checkpoint activation. High-risk individuals exhibited increased drug sensitivity. AGRN levels were linked to decreased progression-free survival (p < 0.001). AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma.

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing.

CD8+ T cells have been recognized as crucial factors in the prognosis of melanoma. However, there is currently a lack of gene markers that accurately describe their characteristics and functions in acral melanoma (AM), which hinders the development of personalized medicine. Firstly, we explored the composition differences of immune cells in AM using single-cell RNA sequencing (scRNA-seq) data and comprehensively characterized the immune microenvironment of AM in terms of composition, developmental differentiation, function, and cell communication. Subsequently, we constructed and validated a prognostic risk scoring model based on differentially expressed genes (DEGs) of CD8+ T cells using the TCGA-SKCM cohort through Lasso-Cox method. Lastly, immunofluorescence staining was performed to validate the expression of four genes (ISG20, CCL4, LPAR6, DDIT3) in AM and healthy skin tissues as included in the prognostic model. The scRNA-seq data revealed that memory CD8+ T cells accounted for the highest proportion in the immune microenvironment of AM, reaching 70.5%. Cell-cell communication analysis showed extensive communication relationships among effector CD8+ T cells. Subsequently, we constructed a prognostic scoring model based on DEGs derived from CD8+ T cell sources. Four CD8+ T cell-related genes were included in the construction and validation of the prognostic model. Additionally, immunofluorescence results demonstrated that ISG20 and CCL4 were downregulated, while LPAR6 and DDIT3 were upregulated in AM tissues compared to normal skin tissues. Identifying biomarkers based on the expression levels of CD8+ T cell-related genes may be an effective approach for establishing prognostic models in AM patients. The independently prognostic risk evaluation model we constructed provides new insights and theoretical support for immunotherapy in AM.

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD). We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments. Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells. Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

LncRNA signature mediates mitochondrial permeability transition-driven necrosis in regulating the tumor immune microenvironment of cervical cancer

Mitochondrial permeability transition (MPT)-driven necrosis (MPTDN) was a regulated variant of cell death triggered by specific stimuli. It played a crucial role in the development of organisms and the pathogenesis of diseases, and may provide new strategies for treating various diseases. However, there was limited research on the mechanisms of MPTDN in cervical cancer (CESC) at present. In this study, Weighted Gene Co-expression Network Analysis (WGCNA) was performed on differentially expressed genes in CESC. The module MEyellow, which showed the highest correlation with the phenotype, was selected for in-depth analysis. It was found that the genes in the MEyellow module may be associated with the tumor immune microenvironment (TIME). Through COX univariate regression and LASSO regression analysis, 6 key genes were identified. These genes were further investigated from multiple perspectives, including their independent diagnostic value, prognostic value, specific regulatory mechanisms in the tumor immune microenvironment, drug sensitivity analysis, and somatic mutation analysis. This study provided a comprehensive exploration of the mechanisms of action of these 6 key genes in CESC patients. And qRT-PCR validation was also conducted. Through COX univariate regression and LASSO coefficient screening of the MEyellow module, 6 key genes were identified: CHRM3-AS2, AC096734.1, BISPR, LINC02446, LINC00944, and DGUOK-AS1. Evaluation of the independent diagnostic value of these 6 key genes revealed that they can serve as independent diagnostic biomarkers. Through correlation analysis among these 6 genes, a potential regulatory mechanism among them was identified. Therefore, a risk prognostic model was established based on the collective action of these 6 genes, and the model showed good performance in predicting the survival period of CESC patients. By studying the relationship between these 6 key genes and the tumor microenvironment of CESC patients from multiple angles, it was found that these 6 genes are key regulatory factors in the tumor immune microenvironment of CESC patients. Additionally, 16 drugs that are associated with these 6 key genes were identified, and 8 small molecule drugs were predicted based on the lncRNA-mRNA network. The 6 key genes can serve as independent biomarkers for diagnosis, and the Risk score of these genes when acting together can be used as an indicator for predicting the clinical survival period of CESC patients. Additionally, these 6 key genes were closely related to the tumor immune microenvironment of CESC patients and were the important regulatory factors in the tumor immune microenvironment of CESC patients.

Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

Considering the key role of myeloid cell differentiation—related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, “edgeR” R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation—related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients.

Integrative analysis of aging-related genes reveals CEBPA as a novel therapeutic target in non-small cell lung cancer

BackgroundTo explore the impact of ARGs on the prognosis of NSCLC, and its correlation with clinicopathological parameters and immune microenvironment. Preliminary research on the biological functions of CEBPA in NSCLC.MethodsUsing consensus clustering analysis to identify molecular subtypes of ARGs in NSCLC patients; employing LASSO regression and multivariate Cox analysis to select 7 prognostic risk genes and construct a prognostic risk model; validating independent prognostic factors of NSCLC using forest plot analysis; analyzing immune microenvironment correlations using ESTIMATE and ssGSEA; assessing correlations between prognostic risk genes via qPCR and Western blot in NSCLC; measuring mRNA and protein expression levels of knocked down and overexpressed CEBPA in NSCLC using CCK-8 and EdU assays; evaluating the effects of knocked down and overexpressed CEBPA on cell proliferation using Transwell experiments; examining the correlation of CEBPA with T cells and B cells using mIHC analysis.ResultsConsensus clustering analysis identified three molecular subtypes, suggesting significant differential expression of these ARGs in NSCLC prognosis and clinical pathological parameters. There was significant differential expression between the two risk groups in the prognostic risk model, with P < 0.001. The risk score of the prognostic risk model was also P < 0.001. CEBPA exhibited higher mRNA and protein expression levels in NSCLC cell lines. Knockdown of CEBPA significantly reduced mRNA and protein expression levels of CEBPB, YWHAZ, ABL1, and CDK1 in H1650 and A549 cells. siRNA-mediated knockdown of CEBPA markedly inhibited proliferation, migration, and invasion of NSCLC cells, whereas overexpression of CEBPA showed the opposite trend. mIHC results indicated a significant increase in CD3 + CD4+, CD3 + CD8+, and CD20 + cell counts in the high CEBPA expression group.ConclusionsThe risk score of the prognostic risk model can serve as an independent prognostic factor, guiding the diagnosis and treatment of NSCLC. CEBPA may serve as a potential tumor biomarker and immune target, facilitating further exploration of the biological functions and immunological relevance in NSCLC.

Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes

BackgroundGiven the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction.MethodsThe Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes.ResultsCluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer.ConclusionThe study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

PRF1 as a prognostic gene for lymphatic metastasis in skin melanoma

BackgroundMelanoma is a highly aggressive tumor, predominantly found in the skin, recognized as skin cutaneous melanoma (SKCM). Lymph node metastasis is commonly used as the route of metastasis in SKCM, necessitating the discovery of prognostic genes associated with this process for improved prognosis. MethodsThe prognostic significance of lymph node metastasis in SKCM was assessed through Kaplan-Meier analysis in SEER and TCGA-SKCM datasets. Prognostic genes were identified and a prognostic risk model was constructed Enrichment analysis and immune cell infiltration analysis were also carried out.Moreover, a validation in vitro and in vivo were conducted by CCK8,flow cytometry, transwell and animal study. ResultsThe Kaplan-Meier survival curve revealed that patients with lymph node metastasis had a worse prognosis compared to those without. FCGR3B and PRF1 were screened by TCGA analysis.Additionally, significant differences in nine immune cell types were observed between the two risk groups. Notably, a strong positive association with CD8 T cells and a negative relationship with M2 macrophages were exhibited by PRF1. The validation of our nomogram were conducted in vitro and in vivo, and the results showed the correlations between CD8+ T cell and PRF1. ConclusionIn summary, two prognostic genes (FCGR3B and PRF1) were identified, and a prognostic risk model was developed for SKCM. These findings provide a novel approach for the diagnosis and treatment of SKCM.

Identification of N6-Methyladenosine-Associated lncRNAs and Analysis of Prognostic Signature in Breast Cancer.

Breast cancer represents the predominant malignant neoplasm in women, posing significant threats to both life and health. N6-methyladenosine (m6A) methylation, the most prevalent RNA modification, plays a crucial role in cancer development. This study aims to delineate the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and identify potential m6AlncRNA candidates as novel therapeutic targets for breast cancer. Through univariate Cox, Least Absolute Shrinkage and Selection Operator and multiple Cox regression analysis, m6AlncRNA was analyzed and a risk-prognosis model was constructed. Kaplan-Meier analysis, principal component analysis and nomogram were used to evaluate the risk model. Finally, we screened candidate lncRNAs and validated them in breast cancer cell lines. m6AlncRNAs were stratified into three subtypes, and their associations with survival outcomes and immune infiltrating capacities were systematically analyzed. Subsequently, breast cancer patients were stratified into high and low-risk groups based on median risk scores, revealing distinct clinical characteristics, tumor immunoinvasive profiles, tumor mutation burden, and survival probabilities. Additionally, a prognostic model was established, highlighting three promising candidate lncRNAs: ECE1-AS1, NDUFA6-DT, and COL4A2-AS1. This study investigated the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and developed a prognostic risk model to identify three potential m6AlncRNA candidates. These findings provide valuable insights into the potential application of these m6AlncRNAs in guiding immunotherapeutic strategies for breast cancer.

Prognostic Significance of SASP-Related Gene Signature of Radiation Therapy in Head and Neck Squamous Cell Carcinoma.

In this study, we developed and validated the clinical significance of senescence-associated secretory phenotype (SASP)-related gene signature and explored its association with radiation therapy (RT) in patients with head and neck squamous cell carcinoma (HNSCC). First, we searched the three published review literature associated with SASP and selected all 81 genes to develop SASP-related gene signature. Then, 81 SASP-related genes were adapted to gene expression dataset from The Cancer Genome Atlas (TCGA). Patients with HNSCC of TCGA were classified into clusters 1 and 2 via unsupervised clustering according to SASP-related gene signature. Kaplan-Meier plot survival analysis showed that cluster 1 had a poorer prognosis than cluster 2 in 5-year overall survival and recurrence-free survival. Similarly, cluster 1 showed a worse prognosis than cluster 2 in three validation cohorts (E-MTAB-8588, FHCRC, and KHU). Cox proportional hazards regression observed that the SASP-related signature was an independent prognostic factor for patients with HNSCC. We also established a nomogram using a relevant clinical parameter and a risk score. Time-dependent receiver operating characteristic analysis was carried out to assess the accuracy of the prognostic risk model and nomogram. Senescence SASP-related gene signature was associated with the response to RT. Therefore, subsequent, in vitro experiments further validated the association between SASP-related gene signature and RT in HNSCC. In conclusion, we developed a SASP-related gene signature, which could predict survival of patients with HNSCC, and this gene signature provides new clinical evidence for the accurate diagnosis and targeted RT of HNSCC.

Integrated analysis of methylation and transcriptome identifies a novel risk model for diagnosis, prognosis, and immune characteristics in head and neck squamous cell carcinoma.

DNA methylation is an important epigenetic modification that plays a crucial role in the development and progression of various tumors. However, the association between methylation‑driven genes and diagnosis, prognosis, and immune characteristics of head and neck squamous cell carcinoma (HNSCC) remains unclear. We obtained transcriptome, methylation, and clinical data from HNSCC patients in TCGA database, and used MethylMix algorithm to identify methylation-driven genes. A methylation driven gene-related risk model was constructed using Lasso regression analysis, and validated using data from GEO database. Immune infiltration and immune function analysis of the expression profiles were conducted using ssGSEA. Differences in immune checkpoint-related genes were analyzed, and the efficacy of immunotherapy was evaluated using TCIA database. Finally, a series of cell functional experiments were conducted to validate the results. Five methylation-driven genes were identified and utilized to construct a prognostic risk model. Based on the median risk score, all patients were categorized into high-risk and low-risk groups. The K-M analysis revealed that patients in the high-risk group have a worse prognosis. Additionally, the risk model demonstrated better prognostic predictive value as indicated by ROC analysis. GSEA enrichment analysis indicated that gene sets in the high and low-risk groups were primarily enriched in pathways associated with tumor immunity and metabolism. Our subsequent investigations showed that high-risk patients exhibited more immunosuppressive phenotypes, while low-risk patients were more likely to respond positively to immunotherapy. These findings of our research have the potential to improve patient stratification, guide treatment decisions, and advance the development of personalized therapies for HNSCC.

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

ABSTRACT Objectives To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value. Methods Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts. Results Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion. Conclusion A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

Prognostic analysis of anoikis-related genes in bladder cancer: An observational study.

Anoikis is proved to play a crucial role in the development of cancers. However, the impact of anoikis on the prognosis of bladder cancer (BLCA) is currently unknown. Thus, this study aimed to find potential effect of anoikis in BLCA. The Cancer Genome Atlas (TCGA)-BLCA and GSE13507 cohorts were downloaded from TCGA and the Gene Expression Omnibus (GEO) databases, respectively. Differentially expressed genes (DEGs) were screened between BLCA and normal groups, which intersected with anoikis-related genes to yield anoikis-related DEGs (AR DEGs). Univariate COX, rbsurv, and multivariate COX analyses were adopted in order to build a prognostic risk model. The differences of risk score in the different clinical subgroups and the relevance between survival rate and clinical characteristics were explored as well. Finally, chemotherapy drug sensitivity in different risk groups was analyzed. In total, 78 AR DEGs were acquired and a prognostic signature was build based on the 6 characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1), where the patients of low-risk group had longer survival time. The survival rate of BLCA patients was significantly differential in different groups of age, stage, smoking history, pathologic-T, and pathologic-N. The IC50 of 56 drugs showed significant differences between 2 risk groups, such as imatinib, docetaxel, and dasatinib. At last, the results of real time quantitative-polymerase chain reaction (RT-qPCR) demonstrated that the expression trend of CALR, HGF, and INHBB was consistent with the result obtained previously based on public databases. Taken together, this study identified 6 anoikis-related characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1) for the prognosis of BLCA patients, providing a scientific reference for further research on BLCA.

The lncRNA UCA1 Enhances Pancreatic Cancer EMT by Regulating miR-708-5p and miR-135b-5p: A Bioinformatics Approach.

Pancreatic cancer (PC) is an exceedingly malignant ailment that is not only characterized by its insidious onset and rapid progression but also by its poor therapeutic effects. Recently, emerging evidence has shed light on the significant role that non-coding RNAs (ncRNAs), particularly long ncRNAs (lncRNAs) and microRNAs (miRNAs), play in the pathogenesis of PC. This investigation aimed to construct a network of interactions between miRNAs, lncRNAs, and mRNAs, as well as to perform correlation analyses in the context of PC. This study carried out in Kerman City, southeastern Iran in 2023. We utilized the GSE119794 dataset from the Gene Expression Omnibus (GEO) to analyze differentially expressed lncRNAs (DE-lncRNAs), miRNAs (DE-miRNAs), and mRNAs (DE-mRNAs). Following the identification of the DE-lncRNAs, DE-mRNAs, and DE-miRNAs, we proceeded to examine differentially expressed epithelialmesenchymal transition (EMT) genes. Subsequently, we utilized the RNAInter database to predict interactions among lncRNAs, miRNAs, and mRNAs. Finally, we employed Cytoscape to visualize and analyze the constructed network. 14 DE-lncRNAs, 14 DE-miRNAs, 545 DE-mRNAs, and 65 DE-EMT from pancreatic cancer and its adjacent tissue RNA-Seq data were identified. 1184 EMT genes from dbEMT were obtained, among which 65 DE-EMT were assigned as EMT genes and correlated with tumor progression. One functional lncRNA (UCA1) was identified as a key functional lncRNA. The area under the ROC curve (AUC) of UCA1 and miR-708-5p were 0.79 and 0.86, respectively. Thus, it is reasonable to believe that this prognostic risk model helps predict PC metastasis. UCA1 is a new lncRNA linked with EMT in PC and contributes to a better knowledge of the regulatory mechanisms related to lncRNAs in PC.

Development of a prognostic risk model of uveal melanoma based on N7-methylguanosine-related regulators

BackgroundUveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear.MethodsPrimary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan–Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes.ResultA prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model’s predictability for UVM patients’ prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression.ConclusionWe have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients’ survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.

Identification and validation of calcium extrusion-related genes prognostic signature in colon adenocarcinoma.

Disruptions in calcium homeostasis are associated with a wide range of diseases, and play a pivotal role in the development of cancer. However, the construction of prognostic models using calcium extrusion-related genes in colon adenocarcinoma (COAD) has not been well studied. We aimed to identify whether calcium extrusion-related genes serve as a potential prognostic biomarker in the COAD progression. We constructed a prognostic model based on the expression of calcium extrusion-related genes (SLC8A1, SLC8A2, SLC8A3, SLC8B1, SLC24A2, SLC24A3 and SLC24A4) in COAD. Subsequently, we evaluated the associations between the risk score calculated by calcium extrusion-related genes and mutation signature, immune cell infiltration, and immune checkpoint molecules. Then we calculated the immune score, stromal score, tumor purity and estimate score using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. The response to immunotherapy was assessed using tumor immune dysfunction and exclusion (TIDE). Finally, colorectal cancer cells migration, growth and colony formation assays were performed in RKO cells with the overexpression or knockdown SLC8A3, SLC24A2, SLC24A3, or SLC24A4. We found that patients with high risk score of calcium extrusion-related genes tend to have a poorer prognosis than those in the low-risk group. Additionally, patients in high-risk group had higher rates of KRAS mutations and lower MUC16 mutations, implying a strong correlation between KRAS and MUC16 mutations and calcium homeostasis in COAD. Moreover, the high-risk group showed a higher infiltration of regulatory T cells (Tregs) in the tumor microenvironment. Finally, our study identified two previously unreported model genes (SLC8A3 and SLC24A4) that contribute to the growth and migration of colorectal cancer RKO cells. Altogether, we developed a prognostic risk model for predicting the prognosis of COAD patients based on the expression profiles of calcium extrusion-related genes, Furthermore, we validated two previously unreported tumor suppressor genes (SLC8A3 and SLC24A4) involved in colorectal cancer progression.

An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer

Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC). By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted of 66 TRIMs, of which 52 were differentially expressed and 43 were differentially prognostic. Significant survival differences existed between the gene clusters obtained by consensus clustering analysis. Using 4 differentially expressed genes identified by multivariate Cox regression and LASSO regression, a risk scoring system was developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell infiltration, and drug resistance. Transcriptomic data and clinical sample tissue microarrays confirmed that TRIM31 was highly expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to gastric carcinogenesis. This study investigated the whole functional and expression profile and a risk score system based on the TRIM family in GC. Further investigation centered around TRIM31 offers insight into the underlying mechanisms of action exhibited by other members of its family in the context of GC.

Construction and validation of a prognostic model for bladder cancer based on disulfidptosis-related lncRNAs.

Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management. Transcriptomic and clinical datasets for patients with BLCA were obtained from The Cancer Genome Atlas. Using multivariate Cox regression and least absolute shrinkage and selection operator techniques, a risk prognostic signature defined by DRLs was developed. The model's accuracy and prognostic relevance were assessed through Kaplan-Meier survival plots, receiver operating characteristic curves, concordance index, and principal component analysis. Functional and pathway enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, were conducted to elucidate the underlying biological processes. Immune cell infiltration was quantified using the CIBERSORT algorithm. Differences and functions of immune cells in different risk groups were evaluated through single-sample Gene Set Enrichment Analysis. The Tumor Immune Dysfunction and Exclusion predictor and tumor mutational burden (TMB) assessments were utilized to gauge the likelihood of response to immunotherapy. Drug sensitivity predictions were made using the Genomics of Drug Sensitivity in Cancer database. A robust 8-DRL risk prognostic model, comprising LINC00513, SMARCA5-AS1, MIR4435-2HG, MIR4713HG, AL122035.1, AL359762.3, AC006160.1, and AL590428.1, was identified as an independent prognostic indicator. This model demonstrated strong predictive power for overall survival in patients with BLCA, revealing significant disparities between high- and low-risk groups regarding tumor microenvironment, immune infiltration, immune functions, TMB, Tumor Immune Dysfunction and Exclusion scores, and drug susceptibility. This study introduces an innovative prognostic signature of 8 DRLs, offering a valuable prognostic tool and potential therapeutic targets for bladder carcinoma. The findings have significant implications for TMB, the immune landscape, and patient responsiveness to immunotherapy and targeted treatments.

PANoptosis-based molecular subtype and prognostic model predict survival and immune landscape in esophageal cancer

PurposeTo establish a prognostic model to predict the survival of patients with esophageal cancer (EC).MethodsWe extracted the expression profiles of prognostic-related genes and clinicopathological data from TCGA and GEO databases. Subsequently, a comprehensive bioinformatics analysis was conducted to construct a prognostic model utilizing LASSO and multivariate Cox regression. The stability of the risk signature was validated through Kaplan-Meier and ROC curve analyses on the training, internal testing, and external testing sets. Furthermore, we developed a nomogram that incorporates the risk score and clinical features to predict the suvival. Additionally, a nomogram incorporating the risk score and relevant clinical parameters was developed to enhance survivorship prediction. Furthermore, we delved into exploring the correlation between the risk score and immune cell abundance, expression of cancer checkpoints, as well as responses to immunotherapy and chemotherapeutic agents.ResultsIn this study, we successfully identified 19 prognosis-related genes out of a pool of 65 PANoptosis-related genes (PRGs) sourced from existing literature. Through consensus clustering analysis, we classified patients into two distinct groups as PANcluster A and B. Furthermore, the risk score derived from the five PANoptosis-related signatures emerged as an independent prognostic factor among patients with EC. To enhance the prognostic accuracy, we devised a nomogram integrating the risk score with clinical risk characteristics, enabling the prediction of 1-year, 2-year, and 3-year overall survival (OS) rates. Notably, individuals classified in the high-risk group demonstrated poorer prognoses compared to their low-risk counterparts. Furthermore, the risk score displayed substantial correlations with immune cell abundance, expression levels of cancer checkpoints, and responses to immunotherapy and chemotherapeutic agents. These pivotal findings underscore the significance of considering PANoptosis-related patterns in improving prognostic assessment and predicting treatment responses in patients diagnosed with esophageal cancer.ConclusionWe constructed a reliable prognostic risk model for EC utilizing five PRGs. The developed nomogram serves as a valuable tool in predicting patient outcomes, offering crucial insights that can inform and guide treatment decisions for individuals diagnosed with EC.