The m6A/m5C/m1A regulator genes signature reveals the prognosis and is related with immune microenvironment for hepatocellular carcinoma

BackgroundWe developed a prognostic model to evaluate the overall survival (OS) and progression-free survival (PFS) of patients with unresectable hepatocellular carcinoma (u-HCC) treated with Hepatic arterial infusion chemotherapy of infusion oxaliplatin, fluorouracil and leucovorin (FOLFOX-HAIC).MethodsThis model was based on a retrospective study of u-HCC patients treated with the FOLFOX-HAIC (oxaliplatin 130 mg/m2, leucovorin 400 mg/m2, fluorouracil bolus 400 mg/m2 on day 1, and fluorouracil infusion 2,400 mg/m2 for 23–46 h, once every 3–4 weeks). We divided the patients into a training cohort and a validation cohort, used LASSO regression construct prognostic models, predict patient’s OS and PFS based on nomograms of models. Patients were divided into high-risk, medium-risk, and low-risk groups according to their respective model risk scores. Kaplan-Meier survival analysis was used to assess the survival time between the three patient cohorts.ResultsA total of 333 patients were enrolled in the study and divided into a training cohort and a verification cohort at a ratio of 7:3 (233 in the training cohort and 100 in the validation cohort). The prognostic model we established contained nine prognostic variables. The results of concordance index (C-index) of the OS and PFS prognostic model was 0.75 and 0.71, respectively, higher than that of the TNM staging (0.57 and 0.55, p < 0.001), time-dependent ROC (td-ROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) also showed that the model was better than the TNM staging for u-HCC predicting OS and PFS. Subsequently, the model was used to develop a nomogram to predict the individualized prognosis of patients with u-HCC treated with the FOLFOX-HAIC, with a higher net benefit than the TMN staging. According to the risk score, patients were divided into a low-risk group (risk score ≤ 0.458), the medium-risk group (risk score: 0.458–0.799) and the high-risk group (risk score > 0.799). There were significant differences in the OS and PFS between the three groups.ConclusionsThe model developed by our team enables risk stratification and personalized prognosis assessment for u-HCC patients undergoing FOLFOX-HAIC treatment, exhibiting superior predictive accuracy and discriminative capability compared to TNM staging.

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.

This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C‐index), time‐dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high‐risk and low‐risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan–Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C‐index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low‐risk (risk score ≤ −1.423) and high‐risk (risk score > −1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients.

Glioblastoma multiforme (GBM), the most prevalent and aggressive primary brain tumor, poses substantial challenges in both treatment and prognosis. Post-translational modifications, like palmitoylation, are known to have critical roles in the development and progression of glioma. Yet, the molecular mechanisms involved in palmitoylation and its prognostic significance in GBM are still not fully understood. This study aimed to explore prognostic biomarkers for GBM based on palmitoylation-related genes and to construct a prognostic risk model. The messenger ribonucleic acid (mRNA) expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore palmitoylation-related mechanisms in GBM. The Cox regression analysis was performed to identify prognostic palmitoylation-related genes and the consensus clustering was used for molecular classification. The package "limma" was used for differential gene expression analysis and the least absolute shrinkage and selection operator (LASSO) regression was applied to construct a risk signature. A nomogram model was established using the risk score and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. The difference in immune cell infiltration was compared between different risk groups. The drug susceptibility analysis and immunotherapy response prediction were conducted to access the ability of the risk signature in predicting the therapeutic effect. Based on datasets from TCGA, five palmitoylation-related genes were identified as prognostic markers, allowing for the categorization of GBM patients into two subtypes with differing survival rates. Through differential expression analysis, 570 specific genes linked to GBM advancement were uncovered. A total of seven signature genes (COL22A1, IGFBP6, SOD3, UPP1, CA14, TIMP4 and FERMT1) were applied to establish a prognostic risk model, which was demonstrated to be an independent prognostic indicator for patients with GBM. Kaplan-Meier analysis indicted that the GBM patients in low-risk group exhibited a better survival outcome compared the patients in high-risk group. The ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Two external cohort of patients from the GSE74187 and GSE83300 in the GEO database confirmed the model's strong predictive performance. The immune infiltration, drug sensitivity and immunotherapy responses were significantly different between the low- and high-risk groups. Our study offers insights into the molecular classification and prognostic assessment of GBM, focusing on palmitoylation-related mechanisms. The prognostic model we constructed provides valuable guidance for tailoring personalized treatment strategies for GBM patients.

BackgroundBladder cancer (BC) is a commonly occurring malignant tumor of the urinary system, demonstrating high global morbidity and mortality rates. BC currently lacks widely accepted biomarkers and its predictive, preventive, and personalized medicine (PPPM) is still unsatisfactory. N6-methyladenosine (m6A) modification and non-coding RNAs (ncRNAs) have been shown to be effective prognostic and immunotherapeutic responsiveness biomarkers and contribute to PPPM for various tumors. However, their role in BC remains unclear.Methodsm6A-related ncRNAs (lncRNAs and miRNAs) were identified through a comprehensive analysis of TCGA, starBase, and m6A2Target databases. Using TCGA dataset (training set), univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop an m6A-related ncRNA–based prognostic risk model. Kaplan-Meier analysis of overall survival (OS) and receiver operating characteristic (ROC) curves were used to verify the prognostic evaluation power of the risk model in the GSE154261 dataset (testing set) from Gene Expression Omnibus (GEO). A nomogram containing independent prognostic factors was developed. Differences in BC clinical characteristics, m6A regulators, m6A-related ncRNAs, gene expression patterns, and differentially expressed genes (DEGs)–associated molecular networks between the high- and low-risk groups in TCGA dataset were also analyzed. Additionally, the potential applicability of the risk model in the prediction of immunotherapeutic responsiveness was evaluated based on the “IMvigor210CoreBiologies” data set.ResultsWe identified 183 m6A-related ncRNAs, of which 14 were related to OS. LASSO regression analysis was further used to develop a prognostic risk model that included 10 m6A-related ncRNAs (BAALC-AS1, MIR324, MIR191, MIR25, AC023509.1, AL021707.1, AC026362.1, GATA2-AS1, AC012065.2, and HCP5). The risk model showed an excellent prognostic evaluation performance in both TCGA and GSE154261 datasets, with ROC curve areas under the curve (AUC) of 0.62 and 0.83, respectively. A nomogram containing 3 independent prognostic factors (risk score, age, and clinical stage) was developed and was found to demonstrate high prognostic prediction accuracy (AUC = 0.83). Moreover, the risk model could also predict BC progression. A higher risk score indicated a higher pathological grade and clinical stage. We identified 1058 DEGs between the high- and low-risk groups in TCGA dataset; these DEGs were involved in 3 molecular network systems, i.e., cellular immune response, cell adhesion, and cellular biological metabolism. Furthermore, the expression levels of 8 m6A regulators and 12 m6A-related ncRNAs were significantly different between the two groups. Finally, this risk model could be used to predict immunotherapeutic responses.ConclusionOur study is the first to explore the potential application value of m6A-related ncRNAs in BC. The m6A-related ncRNA–based risk model demonstrated excellent performance in predicting prognosis and immunotherapeutic responsiveness. Based on this model, in addition to identifying high-risk patients early to provide them with focused attention and targeted prevention, we can also select beneficiaries of immunotherapy to deliver personalized medical services. Furthermore, the m6A-related ncRNAs could elucidate the molecular mechanisms of BC and lead to a new direction for the improvement of PPPM for BC.

The development of a prognostic model for patients with colorectal cancer (CRC) can facilitate the assessment of patient survival and the effectiveness of clinical treatments. A reasonable prognostic model can provide a basis for individualized treatment, prognostic risk stratification, and subsequent therapy for CRC patients. The aim of our study was to construct a prognostic model for patients with CRC using sequencing data derived from The Cancer Genome Atlas (TCGA) database. Sequencing data of paracancerous tissues (n=51) and CRC samples (n=647) were downloaded from the TCGA database. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify prognostic factors. A restricted cubic spline (RCS) model was used to assess the nonlinear relationship between risk score and poor overall survival (OS). The Genomics of Drug Sensitivity in Cancer (GDSC) database was accessed to evaluate the correlation between the prognostic model's risk score and drug sensitivity. The single-sample gene set enrichment analysis (ssGSEA), estimate, and CIBERSORT algorithms were applied to quantify the association between prognostic genes and immune cell infiltration in CRC. Our findings revealed that six genes, including Niemann-Pick C1-like 1 (NPC1L1) [hazard ratio (HR) =1.53; 95% confidence interval (CI): 1.08-2.17; P=0.02], glucagon-like peptide 2 receptor (GLP2R) (HR =0.68; 95% CI: 0.48-0.97; P=0.04), solute carrier family 8 member A3 (SLC8A3) (HR =0.67; 95% CI: 0.47-0.96; P=0.03), alpha-1-microglobulin/bikunin precursor (AMBP) (HR =0.64; 95% CI: 0.45-0.91; P=0.01), single-pass membrane protein with coiled-coil domains 2 (SMCO2) (HR =0.68; 95% CI: 0.48-0.97; P=0.03), and tetratricopeptide repeat domain 16 (TTC16) (HR =1.55; 95% CI: 1.09-2.20; P=0.02) function as independent prognostic factors for CRC. Based on these six genes, the developed prognostic assessment model identified a strong association between high risk score and poor OS (HR =2.43; 95% CI: 1.67-3.53; P<0.001) in patients with CRC. Furthermore, the analysis revealed a nonlinear relationship (P<0.001) between continuous variation in risk score and the risk of poor OS. Additionally, specific genes included in the prognostic model were found to be strongly associated with cancer stem cell and immune cell infiltration in CRC. We developed a prognostic risk model incorporating a six-gene panel for patients with CRC. Our analysis revealed a nonlinear relationship between this prognostic model and OS in patients with CRC. A high risk score was associated with poor prognosis, indicating that the adverse outcomes observed in patients with CRC may be influenced by cancer stem cell and immune cell infiltration. Our model provides a promising predictive method for the prognosis of CRC patients, but it still needs to be validated in a larger sample size.

BackgroundThere has been little investigation carried out into the activity of immune-related genes in the prognosis of non-small cell lung cancer (NSCLC). Our study set out to analyze the correlation between the differential expression of immune genes and NSCLC prognosis by screening the differential expression of immune genes. Based on the immune genes identified, we aimed to construct a prognostic risk model and explore some novel molecules which have predictive potential for therapeutic effect and prognosis in lung cancer.MethodsImmune gene transcriptome data and clinical data of NSCLC samples were extracted from TCGA database, and transcription factors in the ImmPort dataset were obtained. The data were divided into two groups: normal tissues and tumor tissues. The expression levels of immune genes were compared using the edgeR algorithm, and then differential expression analysis was performed. The survival analysis was carried out by combining differential immune genes with clinical survival time, so that the immune genes influencing the prognosis of NSCLC could be determined. A risk score was calculated based on the expression levels of the immune genes related to the prognosis of NSCLC and their corresponding coefficients to construct a prognostic risk model. This model was used to calculate patient risk scores and perform clinical correlation analysis. The selected molecules were further verified by clinical samples.ResultsBy comparing NSCLC tissues with normal tissues, a total of 6,778 differentially expressed genes were found (P<0.05), of which 490 were differential immune-related genes. Survival analysis determined 28 differential immune genes to be associated with prognosis (P<0.05). We calculated the patient risk value based on the immune gene prognosis model. The survival curve was drawn according to the patient risk score and showed that the survival prognosis was significantly different for the high-risk and the low-risk groups (P<0.05). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.723, which represented a relatively high true-positive rate. All of the results proved the reliability of our immune gene risk prognostic model. After drawing the risk curve, S100A16, IGKV4, S100P, ANGPTL4, SEMA4B, and LGR4 were found to be the high-risk immune genes in NSCLC. Clinical correlation analysis of survival-related differential immune genes revealed that in patients with lymph node metastasis, ANGPTL4 was positively correlated with T stage, S100a16 and SEMA4B were upregulated, and VIPR1 was downregulated. Further analysis revealed that VIPR1 was decreased in metastatic lung cancer compared to non-metastatic lung cancer. Furthermore, the real-time PCR detection of the clinical samples showed that S100A16 expression in lung cancer was increased, while VIPR1 expression in lung cancer was downregulated, which was consistent with the results of our bioinformatics analysis.ConclusionsBased on big data from the TCGA and ImmPort databases, our study analyzed the relationship between differential expression of immune-related genes and clinical data, and constructed a prognostic model based on the immune genes identified. Two novel molecules, S100A16 and VIPR1, were verified to possibly have significant biological function in NSCLC. Our research may provide us with new insight into the immune genes by which the malignant biological behavior of NSCLC is mediated.

Objectives: It is reported that inflammation- and nutrition-related indicators have a prognostic impact on multiple cancers. Here we aimed to identify a prognostic nomogram model for prediction of overall survival (OS) in surgical patients with tongue squamous cell carcinoma (TSCC). Methods: The retrospective data of 172 TSCC patients were charted from the Cancer Hospital of Shantou University Medical College between 2008 and 2019. A Cox regression analysis was performed to determine prognostic factors to establish a nomogram and predict OS. The predictive accuracy of the model was analyzed by the calibration curves and the concordance index (C-index). The difference of OS was analyzed by Kaplan–Meier survival analysis. Results: Multivariate analysis showed age, tumor node metastasis (TNM) stage, red blood cell, platelets, and platelet-to-lymphocyte ratio were independent prognostic factors for OS, which were used to build the prognostic nomogram model. The C-index of the model for OS was 0.794 (95% CI = 0.729-0.860), which was higher than that of TNM stage 0.685 (95% CI = 0.605-0.765). In addition, decision curve analysis also showed the nomogram model had improved predictive accuracy and discriminatory performance for OS, compared to the TNM stage. According to the prognostic model risk score, patients in the high-risk subgroup had a lower 5-year OS rate than that in a low-risk subgroup (23% vs 49%, P < .0001). Conclusions: The nomogram model based on clinicopathological features inflammation- and nutrition-related indicators represents a promising tool that might complement the TNM stage in the prognosis of TSCC.

BackgroundAcute leukemia (AL) is one of the most prevalent pediatric malignancies with highly heterogeneous clinical outcomes. Coagulation-related genes (CRGs) play a crucial role in tumours, but their value in combination with clinical factors for prognostic prediction in AL is unclear. This study aims to develop a prognostic model based on the CRGs signature, with the goal of improving prognostic monitoring and identifying potential therapeutic targets for pediatric AL.MethodsWe collected transcriptomic and clinical data of pediatric AL patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) databases, including age, gender, and white blood cell count (WBC). Molecular subtypes related to CRGs were identified via non-negative matrix factorization (NMF). A CRGs-based gene signature was developed using the least absolute shrinkage and selection operator (LASSO) and regression analyses. The model was built on a training set and validated independently. Time-dependent receiver operating characteristic (ROC) was used to assess the predictive accuracy of the model for 1-, 3-, and 5-year overall survival (OS). Nomograms were constructed combining CRGs characteristics and clinical factors, and their clinical utility was assessed using calibration curves and decision curve analysis (DCA). Immune infiltration was quantified using the single-sample gene set enrichment analysis (ssGSEA) and the microenvironment cell populations-counter (MCPcounter) algorithm. Kaplan-Meier (K-M) survival analysis was performed to assess the correlation between signature gene expression and OS. Moreover, molecular docking was utilized to investigate the potential interactions between signature genes and small-molecule drugs. Expression of key genes was confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsA total of 103 AL patients were included as a training set. Risk stratification based on the median risk score of CRGs showed a significant difference in OS between the two groups (P<0.001), with the low-risk group having a better prognosis. The area under the curves (AUCs) of the model for 1-, 3-, and 5-year survival prediction in the training set were 0.711, 0.762, and 0.718, respectively, and the AUC values in the independent validation set also showed good agreement. Analysis integrating risk scores with clinical data indicated that the CRGs signature could serve as an independent prognostic factor. The nomogram constructed based on CRGs features and key clinical variables showed good fit and potential clinical net effect. Molecular docking analysis revealed stable binding interactions between PROS1 and the small-molecule drugs, avatrombopag and lusutrombopag.ConclusionsIn this study, a robust prognostic model incorporating CRGs was constructed to effectively predict survival outcomes in paediatric AL patients. The model helps to enable individualised risk stratification and guide targeted therapy. In addition, avatrombopag and lusutrombopag as potential therapeutic agents provide new ideas for precision medicine in paediatric AL.

The median survival time of lower grade glioma (LGG) tumors spans a wide range of 2-10 years and is highly dependent on the molecular characteristics and tumor location. Currently, there is no prognostic predictor for these tumors based on autophagy-related (ATG) genes. A prognostic risk score model based on the most significant seven ATG genes was established for LGG. These seven genes, including GRID2, FOXO1, MYC, PTK6, IKBKE, BIRC5, and TP73, have been screened as potentially therapeutic targets. The Kaplan-Meier survival curve analyses validated that patients with high or low risk scores had significantly different overall survival. Following the multivariate Cox regression and area under the ROC curve (AUC) analysis, a final prognostic model based on age, World Health Organization grade, 1p19q-codeletion status, and ATG risk score was performed as an independent prognostic indicator (training set: p = 4.09E-05, AUC = 0.901; validation set-1: p = .00069, AUC = 0.808; validation set-2: p = .0376, AUC = 0.830). Subsequently, a prognostic nomogram was constructed for individualized survival prediction. The calibration plots showed excellent predict efficiency between probability and actual overall survival. In this study, we provided several potential biomarkers for further developing potentially therapeutic targets of LGG. We also established a prognostic model and nomogram to improve the clinical glioma management and assist individualized survival prediction.

BackgroundWith the swift advancements in immunotherapy for solid tumors, exploring immune characteristics of tumors has become increasingly important. The tumor microenvironment (TME) is closely related to the prognosis and treatment of tumor patients. This study aims to explore the expression characteristics and model construction of TME-related genes in lung adenocarcinoma (LUAD) patients, and provide help for clinical diagnosis and treatment.MethodsThrough the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, we analyzed the transcriptomic data of 559 samples from The Cancer Genome Atlas (TCGA) data set to estimate the stromal cells and immune cells, and screened the immune-related differentially expressed genes (DEGs), namely, the TME-DEGs. Essential TME genes were then selected from the TME-DEGs by multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression, and a prediction model of prognostic risk score (RS) was established.ResultsWe identified 5 crucial TME genes: ABCC2, ECT2L, CD200R1, ACSM5, and CLEC17A. Analysis of the genes’ associations with prognosis and clinical features showed that ABCC2 was significantly associated with poorer prognosis and decreased immune signatures, whereas the other 4 associated with improved prognosis and immune signatures. Further, a prognostic RS prediction model was constructed based on these 5 genes, and the results showed that patients with low RS had significantly higher overall survival (OS; P<0.001), relapse-free survival (RFS; P=0.009) and disease-free survival (DFS; P=0.005) than the high RS group, and it had a certain predictive accuracy [area under the curve (AUC)] of 5 years OS =0.70). Those were consistent in the GSE50081 cohort.ConclusionsFive crucial TME genes, ABCC2, ECT2L, CD200R1, ACSM5, and CLEC17A, are significantly correlated with the prognosis and tumor immune microenvironment (TIME) characteristic of LUAD patients, and the prognostic model has good prediction efficiency, which may improve clinical prognostic models and therapy selection.

BackgroundMultiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM.MethodsWe acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan–Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC).ResultsFifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group.ConclusionOur research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM.

Objective This study aimed to construct a prognostic model for rectal adenocarcinomas based on immune-related long noncoding RNAs (lncRNAs) and verify its prediction efficiency. Methods Transcript data and clinical data of rectal adenocarcinomas were downloaded from The Cancer Genome Atlas (TCGA) database. Perl software (strawberry version) and R language (version 3.6.1) were used to analyze the immune-related genes and immune-related lncRNAs of rectal adenocarcinomas, and the differentially expressed immune-related lncRNAs were screened according to the criteria |log2FC| &gt; 1 and P &lt; 0.05. The key immune-related lncRNAs were screened using single-factor Cox regression analysis and lasso regression analysis. Multivariate Cox regression analysis was performed to construct an immune-related lncRNA prognostic model using the risk scores. Next, we evaluated the effectiveness of the model through Kaplan-Meier (K-M) survival analysis, ROC curve analysis, and independent prognostic analysis of clinical features. In addition, prognostic biomarkers of immune-related lncRNAs in the model were analyzed by K-M survival analysis. Results In this study, we obtained gene expression profile matrices of 89 rectal adenocarcinomas and 2 paracancerous specimens from TCGA database and applied immunologic signatures to these transcripts. Through R and Perl software analysis, we obtained 847 immune-related lncRNAs and 331 protein-encoded immune-related genes in rectal adenocarcinomas. Eight important immune-related lncRNAs related to the prognosis of rectal adenocarcinomas were identified using univariate Cox regression and lasso regression analysis. Furthermore, four immune-related lncRNAs were identified as prognostic markers of rectal adenocarcinomas via multivariate Cox regression analysis. The prognostic risk model was as follows: risk score = (-4.084) * expression LINC01871 + (3.112) * expression AL158152.2 + (7.616) * expression PXN-AS1 + (-0.867) * expression HCP5. The independent prognostic effect of the rectal adenocarcinoma risk score model was revealed through K-M analysis, ROC curve analysis, and univariate, and multivariate Cox regression analysis (P = 0.035). LINC01871 (P = 0.006), PXN-AS1 (P = 0.008), and AL158152.2 (P = 0.0386) were closely correlated with the prognosis of rectal adenocarcinomas through the K-M survival analysis. Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immune-related lncRNAs by analyzing the data based on TCGA database, with high prediction accuracy. We also identified two biomarkers with poor prognosis (PXN-AS1 and AL158152.2) and one biomarker with good prognosis (LINC01871).

BackgroundColorectal cancer (CRC) is a common malignant solid tumor with an extremely low survival rate after relapse. Previous investigations have shown that autophagy possesses a crucial function in tumors. However, there is no consensus on the value of autophagy-associated genes in predicting the prognosis of CRC patients. This work screens autophagy-related markers and signaling pathways that may participate in the development of CRC, and establishes a prognostic model of CRC based on autophagy-associated genes.MethodsGene transcripts from the TCGA database and autophagy-associated gene data from the GeneCards database were used to obtain expression levels of autophagy-associated genes, followed by Wilcox tests to screen for autophagy-related differentially expressed genes. Then, 11 key autophagy-associated genes were identified through univariate and multivariate Cox proportional hazard regression analysis and used to establish prognostic models. Additionally, immunohistochemical and CRC cell line data were used to evaluate the results of our three autophagy-associated genes EPHB2, NOL3, and SNAI1 in TCGA. Based on the multivariate Cox analysis, risk scores were calculated and used to classify samples into high-risk and low-risk groups. Kaplan-Meier survival analysis, risk profiling, and independent prognosis analysis were carried out. Receiver operating characteristic analysis was performed to estimate the specificity and sensitivity of the prognostic model. Finally, GSEA, GO, and KEGG analysis were performed to identify the relevant signaling pathways.ResultsA total of 301 autophagy-related genes were differentially expressed in CRC. The areas under the 1-year, 3-year, and 5-year receiver operating characteristic curves of the autophagy-based prognostic model for CRC were 0.764, 0.751, and 0.729, respectively. GSEA analysis of the model showed significant enrichment in several tumor-relevant pathways and cellular protective biological processes. The expression of EPHB2, IL-13, MAP2, RPN2, and TRAF5 was correlated with microsatellite instability (MSI), while the expression of IL-13, RPN2, and TRAF5 was related to tumor mutation burden (TMB). GO analysis showed that the 11 target autophagy genes were chiefly enriched in mRNA processing, RNA splicing, and regulation of the mRNA metabolic process. KEGG analysis showed enrichment mainly in spliceosomes. We constructed a prognostic risk assessment model based on 11 autophagy-related genes in CRC.ConclusionA prognostic risk assessment model based on 11 autophagy-associated genes was constructed in CRC. The new model suggests directions and ideas for evaluating prognosis and provides guidance to choose better treatment strategies for CRC.

BackgroundLiver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported.MethodsFirstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan–Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC.ResultsUsing lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells.ConclusionIn this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.