Prognosis-related Genes Research Articles

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

FAIM2 is correlated with metastasis of medulloblastoma through bioinformatics analysis.

Medulloblastoma (MB) is one of the most frequent malignant brain tumors in children. The metastasis of MB outside the nervous system is associated with a poor prognosis. Our study aimed to explore the genes correlated with metastasis in MB. Using the data downloaded from the gene expression omnibus database, the differentially expressed genes were identified between the metastatic and nonmetastatic samples in MB, which were undergone functional enrichment. Prognosis related genes were identified using univariate Cox regression analysis. The gene set enrichment analysis was conducted to find MB metastasis related pathways. A total of 196 differentially expressed genes were identified between metastatic and nonmetastatic samples in MB patients, and these genes were significantly enriched in 483 gene ontology terms and 29 Kyoto encyclopedia of genes and genomes pathways. In addition, univariate Cox regression analysis screened the top 10 genes (CEMIP, GLCE, ART3, GABRA5, COLEC12, LIN28B, ZNF521, IL17RB, Fas apoptotic inhibitory molecule 2 (FAIM2), RCBTB2) that were significantly associated with survival of MB, among which FAIM2 was prominently expressed in cerebral cortex, cerebellum and hippocampus. The expression of FAIM2 was decreased in metastatic MB samples, and FAIM2 harbored missense mutations, amplifications and deep deletions in metastatic samples of MB. Moreover, a total of 25 pathways were significantly activated and 41 pathways were significantly inhibited in FAIM2 high expression group compared to FAIM2 low expression group in MB patients. FAIM2 was tightly correlated with metastasis in MB patients, and the low expression of FAIM2 was associated with poor prognosis.

Clustering analysis and prognostic model based on PI3K/AKT-related genes in pancreatic cancer.

Pancreatic cancer is one of most aggressive malignancies with a dismal prognosis. Activation of PI3K/AKT signaling is instrumental in pancreatic cancer tumorigenesis. The aims of this study were to identify the molecular clustering, prognostic value, relationship with tumor immunity and targeting of PI3K/AKT-related genes (PARGs) in pancreatic cancer using bioinformatics. The GSEA website was searched for PARGs, and pancreatic cancer-related mRNA data and clinical profiles were obtained through TCGA downloads. Prognosis-related genes were identified by univariate Cox regression analysis, and samples were further clustered by unsupervised methods to identify significant differences in survival, clinical information and immune infiltration between categories. Next, a prognostic model was constructed using Lasso regression analysis. The model was well validated by univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis and ROC curves, and correlations between risk scores and patient pathological characteristics were identified. Finally, GSEA, drug prediction and immune checkpoint protein analyses were performed. Pancreatic cancers were divided into Cluster 1 (C1) and Cluster 2 (C1) according to PARG mRNA expression. C1 exhibited longer overall survival (OS) and higher immune scores and CTLA4 expression, whereas C2 exhibited more abundant PD-L1. A 6-PARG-based prognostic model was constructed to divide pancreatic cancer patients into a high-risk score (HRS) group and a low-risk score (LRS) group, where the HRS group exhibited worse OS. The risk score was defined as an independent predictor of OS. The HRS group was significantly associated with pancreatic cancer metastasis, aggregation and immune score. Furthermore, the HRS group exhibited immunosuppression and was sensitive to radiotherapy and guitarbine chemotherapy. Multidrug sensitivity prediction analysis indicated that the HRS group may be sensitive to PI3K/AKT signaling inhibitors (PIK-93, GSK2126458, CAL-101 and rapamycin) and ATP concentration regulators (Thapsigargin). In addition, we confirmed the oncogenic effect of protein phosphatase 2 regulatory subunit B'' subunit alpha (PPP2R3A) in pancreatic cancer in vitro and in vivo. PARGs predict prognosis, tumor immune profile, radiotherapy and chemotherapy drug sensitivity and are potential predictive markers for pancreatic cancer treatment that can help clinicians make decisions and personalize treatment.

A nine-gene signature as prognostic biomarker in gastric cancer by bioinformatics analysis.

The prognosis of advanced gastric cancer (GC) remains poor. It is urgent and necessary to find suitable prognostic markers. miR-619-5p is highly expressed in GC. However, the value of miR-619-5p and its target genes as prognostic biomarkers of GC is unclear. RT-PCR was performed to verify the expression of miR-619-5p in GC cell lines and their exosomes. Western blotting and transmission electron microscope were used to identify exosomes. The target genes of miR-619-5p were predicted by RNA22 and TargetScan. The differentially expressed genes (DEGs) and prognosis-related genes (PRGs) were obtained using The Cancer Genome Atlas (TCGA) database. The DAVID database was used to analyse pathway enrichment and functional annotation of common target genes. The STRING database and Cytoscape software were used to screen key genes and visualize their functional modules. The survival analysis was conducted using TCGA and Kaplan-Meier Plotter (KMP) databases. Finally, a prognostic model was constructed on the foundation of the key genes to assess the reliability of the screening process. The expression of miR-619-5p in GC cells and their exosomes was proved to be significantly higher than that in normal cell lines. There are 129 common target genes involved in 3 pathways and 28 functional annotations. Finally, nine key target genes of GC (BRCA1, RAD51, KIF11, ERCC6L, BRIP1, TIMELESS, CDC25A, CLSPN and NCAPG2) were identified, and a prognostic model was successfully constructed with a good predictive ability. The model of 9-gene signature could effectively predict the prognosis of GC, and have great potential to be novel prognostic factors and therapeutic targets for patients with GC.

Development and validation of a prognostic model for mitophagy-related genes in colon adenocarcinoma: A study based on TCGA and GEO databases.

Mitophagy is used by eukaryotic cells to eliminate damaged mitochondria. The deregulation of this process can lead to an accumulation of dysfunctional mitochondria and is implicated in carcinogenesis and tumorigenesis. Despite increasing evidence that mitophagy is involved in the development of colon cancer, the role of mitophagy-related genes (MRGs) in colon adenocarcinoma (COAD) prognosis and treatment remains largely unknown. Differential analysis was used to identify differentially expressed mitophagy-related genes associated with COAD and conduct key module screening. Cox regression and least absolute shrinkage selection operator, and other analyses were used to characterize prognosis-related genes and verify the feasibility of the model. The model was tested using GEO data and a nomogram was constructed for future clinical application. The level of immune cell infiltration and immunotherapy were compared between the two groups, and sensitivity to treatment with many commonly used chemotherapeutic agents was assessed in individuals with different risk factors. Finally, qualitative reverse transcription polymerase chain reaction and western blotting were performed to assess the expression of prognosis-related MRGs. A total of 461 differentially expressed genes were mined in COAD. Four prognostic genes, PPARGC1A, SLC6A1, EPHB2, and PPP1R17, were identified to construct a mitophagy-related gene signature. The feasibility of prognostic models was assessed using Kaplan-Meier analysis, time-dependent receiver operating characteristics, risk scores, Cox regression analysis, and principal component analysis. At 1, 3, and 5 years, the area under the receiver operating characteristic curves were 0.628, 0.678, and 0.755, respectively, for TCGA cohort, and 0.609, 0.634, and 0.640, respectively, for the GEO cohort. Drug sensitivity analysis found that camptothecin, paclitaxel, bleomycin, and doxorubicin were significantly different between low- and high-risk patients. The qPCR and western blotting results of clinical samples further confirmed the public database results. This study successfully constructed a mitophagy-related gene signature with significant predictive value for COAD, informing new possibilities for the treatment of this disease.

Omics Analysis of Ferroptosis and Establishment of Prognostic Model for multiple myeloma Patients

To explore the role of ferroptosis-related genes in multiple myeloma(MM) through TCGA database and FerrDb, and build a prognostic model of ferroptosis-related genes for MM patients. Using the TCGA database containing clinical information and gene expression profile data of 764 patients with MM and the FerrDb database including ferroptosis-related genes, the differentially expressed ferroptosis-related genes were screened by wilcox.test function. The prognostic model of ferroptosis-related genes was established by Lasso regression, and the Kaplan-Meier survival curve was drawn. Then COX regression analysis was used to screen independent prognostic factors. Finally, the differential genes between high-risk and low-risk patients were screened, and enrichment analysis was used to explore the mechanism of the relationship between ferroptosis and prognosis in MM. 36 differential genes related to ferroptosis were screened out from bone marrow samples of 764 MM patients and 4 normal people, including 12 up-regulated genes and 24 down-regulated genes. Six prognosis-related genes (GCLM, GLS2, SLC7A11, AIFM2, ACO1, G6PD) were screened out by Lasso regression and the prognostic model with ferroptosis-related genes of MM was established. Kaplan-Meier survival curve analysis showed that the survival rate between high risk group and low risk group was significantly different(P<0.01). Univariate COX regression analysis showed that age, sex, ISS stage and risk score were significantly correlated with overall survival of MM patients(P<0.05), while multivariate COX regression analysis showed that age, ISS stage and risk score were independent prognostic indicators for MM patients (P<0.05). GO and KEGG enrichment analysis showed that the ferroptosis-related genes was mainly related to neutrophil degranulation and migration, cytokine activity and regulation, cell component, antigen processing and presentation, complement and coagulation cascades, haematopoietic cell lineage and so on, which may affect the prognosis of patients. Ferroptosis-related genes change significantly during the pathogenesis of MM. The prognostic model of ferroptosis-related genes can be used to predict the survival of MM patients, but the mechanism of the potential function of ferroptosis-related genes needs to be confirmed by further clinical studies.

Construction of a prognostic model for lung adenocarcinoma based on membrane-tension-related genes.

Lung adenocarcinoma (LUAD), which is the most common type of non-small cell lung cancer (NSCLC), is one of the most aggressive and fatal tumors. Therefore, the identification of key biomarkers affecting prognosis is important to improving the prognosis of patients with LUAD. Cell membranes have long been understood; however, few studies have focused on the role of membrane tension in LUAD. The present study aimed to construct a prognostic model associated with membrane-tension-related genes (MRGs) and explore its prognostic value in LUAD patients. RNA sequencing data and the corresponding clinical characteristics data of LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Five membrane-tension prognosis-related genes (5-MRG) were screened by univariate and multifactorial COX regression and least absolute shrinkage and selection operator (LASSO) regression analyses. The data were then divided into testing, training, and all groups to build a prognostic model, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), copy number variations (CNV), tumor mutation burden (TMB), and tumor microenvironment (TME) analyses were performed to explore the potential mechanisms of MRGs. Finally, single-cell data from the GSE200972 dataset in the Gene Expression Omnibus (GEO) database were obtained to determine the distribution of prognostic MRGs. Construction and validation of the prognostic risk models were conducted using 5-MRG in the trial, test, and all data sets. Patients in the low-risk group had a better prognosis than those in the high-risk group, and the Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) confirmed that the model had a better predictive value for LUAD patients. GO and KEGG analyses of differential genes in the high- and low-risk groups were significantly enriched in immune-related pathways. Immune checkpoint (ICP) differential genes differed significantly in the high- and low-risk groups. By analyzing the single-cell sequencing data, the cells were divided into nine subpopulations and cell subpopulation localization through 5-MRG. The results of this study suggest that a prognostic model based on prognosis-associated MRGs can be used to predict the prognosis of LUAD patients. Therefore, prognosis-related MRGs could be potential prognostic biomarkers and therapeutic targets.

CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer.

It is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC. We obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts. High riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy. Collectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.

A novel scoring system for the quantitative prediction of prognosis in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy. Patient prognosis cannot be accurately assessed in National Comprehensive Cancer Network (NCCN) risk stratification subgroups based on the current criteria. This study aimed to develop a novel prognostic score model for the quantitative prediction of prognosis in AML. We developed a prognostic risk scoring model of AML using differentially expressed genes to predict prognosis in patients with AML. Furthermore, we evaluated the effectiveness and clinical significance of this prognostic model in 4 AML cohorts and 905 patients with AML. A prognostic risk scoring model of AML containing eight prognosis-related genes was constructed using a multivariate Cox regression model. The model had a higher predictive value for the prognosis of AML in the training and validation sets. In addition, patients with lower scores had significantly better overall survival (OS) and even-free survival (EFS) than those with higher scores among patients with intermediate-risk AML according to the NCCN guidelines, indicating that the model could be used to further predict the prognosis of the intermediate-risk AML populations. Similarly, patients with high scores had remarkably poor OS and EFS in the normal-karyotype populations, indicating that the scoring model had an excellent predictive performance for patients with AML having normal karyotype. Our study provided an individualized prognostic risk score model that could predict the prognosis of patients with AML.

BI-D1870 Induces Mitotic Dysfunction and Apoptosis in Neuroblastoma by Regulating the PI3K-Akt-mTORC1 Signal Axis

Introduction: Neuroblastoma (NB) is one of the most common extracranial solid malignant tumors in children. The 5-year survival rate of high-risk or refractory NB is less than 50%. Therefore, developing new effective therapeutics for NB remains an urgent challenge. Materials and Methods: Based on the NB dataset TARGET-NBL in the TCGA database, the prognosis-related genes were analyzed using univariate cox regression (p < 0.01). The protein network interaction of prognostic genes was analyzed using STRING to obtain 150 hub genes with HR > 1 and 150 hub genes with HR < 1. The Connectivity Map database was used to predict a therapeutic drug: BI-D1870, a ribosomal S6 kinase inhibitor. The inhibitory effect of BI-D1870 on NB was investigated through in vivo and in vitro experiments, and its inhibitory mechanism was explored. Results: Both the in vivo and in vitro experiments showed that BI-D1870 could inhibit tumor proliferation and induce tumor apoptosis. Furthermore, we proved that BI-D1870 caused G2/M phase arrest and mitosis damage in cells. RNA-seq of cells showed that BI-D1870 may inhibit the growth of NB by inhibiting the PI3K-Akt-mTOR axis. Western blot and immunofluorescence testing showed that BI-D1870 inhibited the PI3K-Akt-mTORC1 signal pathway to regulate the phosphorylation of RPS6 and 4E BP1 proteins, inhibit protein translation, and inhibit microtubule formation, thus preventing mitotic proliferation and inducing apoptosis. Conclusions: This study provides strong support that BI-D1870 may be a potential adjuvant therapy for NB.

High-Throughput Sequencing Reveals That Rotundine Inhibits Colorectal Cancer by Regulating Prognosis-Related Genes

Rotundine is an herbal medicine with anti-cancer effects. However, little is known about the anti-cancer effect of rotundine on colorectal cancer. Therefore, our study aimed to investigate the specific molecular mechanism of rotundine inhibition of colorectal cancer. MTT and cell scratch assay were performed to investigate the effects of rotundine on the viability, migration, and invasion ability of SW480 cells. Changes in cell apoptosis were analyzed by flow cytometry. DEGs were detected by high-throughput sequencing after the action of rotundine on SW480 cells, and the DEGs were subjected to function enrichment analysis. Bioinformatics analyses were performed to screen out prognosis-related DEGs of COAD. Followed by enrichment analysis of prognosis-related DEGs. Furthermore, prognostic models were constructed, including ROC analysis, risk curve analysis, PCA and t-SNE, Nomo analysis, and Kaplan-Meier prognostic analysis. In this study, we showed that rotundine concentrations of 50 μM, 100 μM, 150 μM, and 200 μM inhibited the proliferation, migration, and invasion of SW480 cells in a time- and concentration-dependent manner. Rotundine does not induce SW480 cell apoptosis. Compared to the control group, high-throughput results showed that there were 385 DEGs in the SW480 group. And DEGs were associated with the Hippo signaling pathway. In addition, 16 of the DEGs were significantly associated with poorer prognosis in COAD, with MEF2B, CCDC187, PSD2, RGS16, PLXDC1, HELB, ASIC3, PLCH2, IGF2BP3, CLHC1, DNHD1, SACS, H1-4, ANKRD36, and ZNF117 being highly expressed in COAD and ARV1 being lowly expressed. Prognosis-related DEGs were mainly enriched in cancer-related pathways and biological functions, such as inositol phosphate metabolism, enterobactin transmembrane transporter activity, and enterobactin transport. Prognostic modeling also showed that these 16 DEGs could be used as predictors of overall survival prognosis in COAD patients. Rotundine inhibits the development and progression of colorectal cancer by regulating the expression of these prognosis-related genes. Our findings could further provide new directions for the treatment of colorectal cancer.

Correlation of tumor mutational burden with prognosis and immune infiltration in lung adenocarcinoma.

Tumor mutational burden (TMB) plays an important role in the evaluation of immunotherapy efficacy in lung adenocarcinoma (LUAD). To improve the clinical management of LUAD by investigating the prognostic value of TMB and the relationship between TMB and immune infiltration. TMB scores were calculated from the mutation data of 587 LUAD samples from The Cancer Genome Atlas (TCGA), and patients were divided into low-TMB and high-TMB groups based on the quartiles of the TMB score. Differentially expressed genes (DEGs), immune cell infiltration and survival analysis were compared between the low-TMB and high-TMB groups. We queried the expression of genes in lung cancer tissues through the GEPIA online database and performed experimental validation of the function of aberrant genes expressed in lung cancer tissues. We obtained sample information from TCGA for 587 LUAD patients, and the results of survival analysis for the high- and low- TMB groups suggested that patients in the high-TMB group had lower survival rates than those in the low-TMB group. A total of 756 DEGs were identified in the study, and gene set enrichment analysis (GSEA) showed that DEGs in the low-TMB group were enriched in immune-related pathways. Among the differentially expressed genes obtained, 15 immune-related key genes were screened with the help of ImmPort database, including 5 prognosis-related genes (CD274, PDCD1, CTLA4, LAG3, TIGIT). No difference in the expression of PDCD1, CTLA4, LAG3, TIGIT in lung cancer tissues and differential expression of CD274 in lung cancer tissues. The survival rate of LUAD patients with low TMB was better than that of LUAD patients with high TMB. CD274 expression was down regulated in human LUAD cell lines H1299, PC-9, A549 and SPC-A1, which inhibited malignant progression of A549 cells.

Identification of a novel eighteen-gene signature of recurrent metastasis neuroblastoma.

Neuroblastoma is the most common malignant tumor in childhood, and metastases occur in more than 30% patients. Recurrent metastasis is the main cause of poor prognosis and high mortality in neuroblastoma. In this regard, there is still a lack of sufficient biomarkers and effective therapies. Therefore, we performed a multi-omics analysis of neuroblastoma patients from Therapeutically Applicable Research To Generate Effective Treatments (TARGET). With clinical relapse site information, tumor samples derived from the primary site were divided into recurrent metastasis and primary tumor groups. The initial gene signature was obtained by comparing RNA-Seq and copy number variation differences. Survival data was used to further filter prognosis-related genes. This 18-gene signature consists of three clusters: tumor suppression, cell proliferation, and immunity. A super enhancer is involved in the enhanced expression of NCAPG in cluster2 together with IRF3. Based on the gene signature expression in primary neuroblastoma, it is possible to predict tumor metastasis before it occurs. According to the anticancer drug dataset of Genomics of Drug Sensitivity in Cancer (GDSC), vinorelbine and docetaxel were predicted to have high sensitivity against recurrent metastatic neuroblastoma. In conclusion, our study offers a novel metastasis biomarker and helps understand the mechanisms of tumor recurrent metastasis. KEY MESSAGES: We identified a novel eighteen-gene signature of recurrent metastasis neuroblastoma and build risk and classification models. We dissected the regulatory role of NCAPG in signatures. We found immune exhaustion and immunosuppression in recurrent metastasis neuroblastoma. Vinorelbine and docetaxel were predicted to have high sensitivity against recurrent metastatic neuroblastoma.

Identification of GNG7 as a novel biomarker and potential therapeutic target for gastric cancer via bioinformatic analysis and in vitro experiments.

Gastric cancer (GC) is one of the most common malignancies with unfavorable prognoses. The present study aimed to identify novel biomarkers or potential therapeutic targets in GC via bioinformatic analysis and in vitro experiments. The Gene Expression Omnibus and The Cancer Genome Atlas databases were used to screen the differentially expressed genes (DEGs). After protein-protein interaction network construction, both module and prognostic analyses were performed to identify prognosis-related genes in GC. The expression patterns and functions of G protein γ subunit 7 (GNG7) in GC were then visualized in multiple databases and further verified using in vitro experiments. A total of 897 overlapping DEGs were detected and 20 hub genes were identified via systematic analysis. After accessing the prognostic value of the hub genes using the online server Kaplan-Meier plotter, a six-gene prognostic signature was identified, which was also significantly correlated with the process of immune infiltration in GC. The results of open-access database analyses suggested that GNG7 is downregulated in GC; this downregulation was associated with tumor progression. Furthermore, the functional enrichment analysis unveiled that the GNG7-coexpressed genes or gene sets were closely correlated with the proliferation and cell cycle processes of GC cells. Finally, in vitro experiments further confirmed that GNG7 overexpression inhibited GC cell proliferation, colony formation, and cell cycle progression and induced apoptosis. As a tumor suppressor gene, GNG7 suppressed the growth of GC cells via cell cycle blockade and apoptosis induction and thus may be used as a potential biomarker and therapeutic target for GC.

Hsa_circ_0046430 promotes the progression of colorectal cancer by targeting miR-6785-5p/SRCIN1 axis as a ceRNA.

The correlation among circular RNAs (circRNAs), microRNAs, and messenger RNAs have gained increasing attention in recent years. However, the mechanism of such discoveries in colorectal cancer (CRC) is not yet elucidated. The present study aimed to clarify whether the novel circRNAs regulate the prognosis-related genes through the competing endogenous RNAs (ceRNA). An analysis of the Weighted Gene Co-Expression Network Analysis was conducted to screen a module-trait circRNAs, and other big data mining technologies were used to predict the related microRNAs and the downstream genes. Prognosis-related gene model was built using the Cox regression analysis for the 138 messenger RNAs associated with hsa circ 0046430. The qRT-PCR was adopted to verify ceRNA network. Immunohistochemistry verified the correlation between SRCIN1 and patient prognosis. In summary, these results demonstrated that hsa_circ_0046430 is a tumor-related circRNA based on the clinical characteristics module of Weighted Gene Co-Expression Network Analysis. The prognostic risk score signature model analysis indicated that CRC risk was independently related to the risk score and SRCIN1 was independently associated with overall survival. Therefore, the hsa_circ_0046430/miR-6785-5p/SRCIN1 axis was constructed. Hsa_circ_0046430/miR-6785-5p/SRCIN1 axis relative expression level was determined by qRT-PCR. Immunohistochemical staining further validated that SCRIN1 was significantly higher in cancer than in adjacent normal tissues. Our study identified and primarily validated the hsa_circ_0046430/miR-6785-5p/SRCIN1 regulatory axis impacted on CRC prognosis, suggesting novel biomarkers and therapeutic targets for CRC patients. Further in-depth studies are essential to confirm the underlying ceRNA mechanism.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma.

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation. The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort. The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy. Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

Comprehensive analysis of cell death genes in hepatocellular carcinoma based on multi-omics data.

Hepatocellular carcinoma (HCC) is a common tumor of the digestive system. Cell death is an essential process in normal tissue that consists of 3 classical pathways: apoptosis, necrosis and autophagy. To perform a comprehensive analysis of the impact of cell death on liver cancer. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Cancer Genome Atlas (TCGA) datasets were used to analyze the relationships between mutations in cell death-related genes and clinical variables of HCC. Then, we applied the DESeq2 package to identify aberrantly expressed genes in HCC and their related biological functions through a Pearson correlation analysis. Finally, a cell death-related signature of HCC was constructed using the single-factor Cox regression. We identified the genes involved in apoptosis, necrosis and autophagy, of which TP53 and SPTA1 had the highest frequency of mutations. The results revealed that cell death-related tumor mutational burden (TMB) was significant for the pathologic stage and had a strong relationship with the prognosis. Moreover, 53 cell death-related genes that are differentially expressed in HCC were screened, and 3 of them were correlated with HCC prognosis. Harvey rat sarcoma viral oncogene homolog (HRAS) affected the infiltration of immune cells and was closely correlated with ferroptosis. Peptidylprolyl isomerase A (PPIA) played a significant role in mitochondrial pathways. At last, we constructed a cell death-related signature of HCC using 10 prognosis-related genes and a nomogram based on 3 variables (expression, group and stage). This study provided a comprehensive analysis of cell death-related genes in HCC based on multi-omics data, identified the contribution of each variable to clinical outcome and predicted the survival probability of HCC patients more directly.

A prognostic model based on necroptosis-related genes for prognosis and therapy in bladder cancer

Bladder cancer, one of the most prevalent malignant cancers, has high rate of recurrence and metastasis. Owing to genomic instability and high-level heterogeneity of bladder cancer, chemotherapy and immunotherapy drugs sensitivity and lack of prognostic markers, the prognosis of bladder cancer is unclear. Necroptosis is a programmed modality of necrotic cell death in a caspase-independent form. Despite the fact that necroptosis plays a critical role in tumor growth, cancer metastasis, and cancer patient prognosis, necroptosis-related gene sets have rarely been studied in bladder cancer. As a result, the development of new necroptosis-related prognostic indicators for bladder cancer patients is critical. Herein, we assessed the necroptosis landscape of bladder cancer patients from The Cancer Genome Atlas database and classified them into two unique necroptosis-related patterns, using the consensus clustering. Then, using five prognosis-related genes, we constructed a prognostic model (risk score), which contained 5 genes (ANXA1, DOK7, FKBP10, MAP1B and SPOCD1). And a nomogram model was also developed to offer the clinic with a more useful prognostic indicator. We found that risk score was significantly associated with clinicopathological characteristics, TIME, and tumor mutation burden in patients with bladder cancer. Moreover, risk score was a valid guide for immunotherapy, chemotherapy, and targeted drugs. In our study, DOK7 was chosen to further verify our prognosis model, and functional assays indicated that knockdown the expression of DOK7 could prompt bladder cancer proliferation and migration. Our work demonstrated the potential role of prognostic model based on necroptosis genes in the prognosis, immune landscape and response efficacy of immunotherapy of bladder cancer.

Identification and validation of a five-gene prognostic signature based on bioinformatics analyses in breast cancer

BackgroundThis study aimed to identify prognostic signatures to predict the prognosis of breast cancer (BRCA) patients based on a series of comprehensive analyses of gene expression data. MethodsThe RNA-sequencing expression data and corresponding BRCA patient clinical data were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Firstly, the differently expressed genes (DEGs) related to prognosis between tumor tissues and normal tissues were ascertained by performing R package “limma”. Secondly, the DEGs were used to construct a polygenic risk scoring model by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator Cox regression (Lasso-cox) analysis method. Thirdly, survival analysis was performed to investigate the risk score values in the TCGA cohort. And the enrichment analysis, immune cell infiltration levels analysis, and protein-protein internet (PPI) analysis were performed. Simultaneously, the GEO cohort was used to validate the model. Lastly, we constructed a nomogram to explore the influence of polygenic risk score and other clinical factors on the survival probability of patients with BRCA. ResultsA total of 1000 DEGs including 396 upregulated genes and 604 downregulated genes were identified from the TCGA-BRCA dataset. We obtained 5 prognosis-related genes, as the key biomarkers by Lasso-cox analysis (FBXL19, HAGHL, PHKG2, PKMYT1, and TXNDC17), all of which were significantly upregulated in breast tumors. The prognostic prediction of the 5 genes model was great in training and validation cohorts. Moreover, the high-risk group had a poorer prognosis. The Cox regression analysis showed that the comprehensive risk score for 5 genes was an independent prognosis factor. ConclusionThe 5 genes risk model constructed in this study had an independent predictive ability to distinguish patients with a high risk of death from those with a low-risk score, and it can be used as a practical and reliable prognostic tool for BRCA.

Identification of microtubule-associated biomarkers in diffuse large B-cell lymphoma and prognosis prediction.

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease with a complicated prognosis. Even though various prognostic evaluations have been applied currently, they usually only use the clinical factors that overlook the molecular underlying DLBCL progression. Therefore, more accurate prognostic assessment needs further exploration. In the present study, we constructed a novel prognostic model based on microtubule associated genes (MAGs). Methods: A total of 33 normal controls and 1360 DLBCL samples containing gene-expression from the Gene Expression Omnibus (GEO) database were included. Subsequently, the univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to select the best prognosis related genes into the MAGs model. To validate the model, Kaplan-Meier curve, and nomogram were analyzed. Results: A risk score model based on fourteen candidate MAGs (CCDC78, CD300LG, CTAG2, DYNLL2, MAPKAPK2, MREG, NME8, PGK2, RALBP1, SIGLEC1, SLC1A1, SLC39A12, TMEM63A, and WRAP73) was established. The K-M curve presented that the high-risk patients had a significantly inferior overall survival (OS) time compared to low-risk patients in training and validation datasets. Furthermore, knocking-out TMEM63A, a key gene belonging to the MAGs model, inhibited cell proliferation noticeably. Conclusion: The novel MAGs prognostic model has a well predictive capability, which may as a supplement for the current assessments. Furthermore, candidate TMEM63A gene has therapeutic target potentially in DLBCL.