International Cancer Genome Consortium Database Research Articles

A survival prediction model and nomogram based on immune-related gene expression in chronic lymphocytic leukemia cells.

There are many different chronic lymphoblastic leukemia (CLL) survival prediction models and scores. But none provide information on expression of immune-related genes in the CLL cells. We interrogated data from the Gene Expression Omnibus database (GEO, GSE22762; Number = 151; training) and International Cancer Genome Consortium database (ICGC, CLLE-ES; Number = 491; validation) to develop an immune risk score (IRS) using Least absolute shrinkage and selection operator (LASSO) Cox regression analyses based on expression of immune-related genes in CLL cells. The accuracy of the predicted nomogram we developed using the IRS, Binet stage, and del(17p) cytogenetic data was subsequently assessed using calibration curves. A survival model based on expression of 5 immune-related genes was constructed. Areas under the curve (AUC) for 1-year survivals were 0.90 (95% confidence interval, 0.78, 0.99) and 0.75 (0.54, 0.87) in the training and validation datasets, respectively. 5-year survivals of low- and high-risk subjects were 89% (83, 95%) vs. 6% (0, 17%; p < 0.001) and 98% (95, 100%) vs. 92% (88, 96%; p < 0.001) in two datasets. The IRS was an independent survival predictor of both datasets. A calibration curve showed good performance of the nomogram. In vitro, the high expression of CDKN2A and SREBF2 in the bone marrow of patients with CLL was verified by immunohistochemistry analysis (IHC), which were associated with poor prognosis and may play an important role in the complex bone marrow immune environment. The IRS is an accurate independent survival predictor with a high C-statistic. A combined nomogram had good survival prediction accuracy in calibration curves. These data demonstrate the potential impact of immune related genes on survival in CLL.

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

There is accumulating evidence that autophagic activity is crucial to the development of hepatocellular carcinoma (HCC). Thus, we sought to develop a predictive model based on autophagy-related genes (ARGs) to forecast the prognosis of HCC patients. Based on expression data from The Cancer Genome Atlas (TCGA) and ARGs from Human Autophagy Database (HADb), the differentially expressed ARGs were screened. The prognosis-related ARGs were identified using a univariate Cox regression analysis. Using multivariate Cox regression analysis, a prognostic model was developed. To assess the predictive value of the model, receiver operating characteristic (ROC) curve, Kaplan-Meier curve, and multivariable Cox regression analyses were conducted. A data cohort gathered independently from the International Cancer Genome Consortium (ICGC) database further verified the model's predictive accuracy. The immune landscape was generated using the TIMER and CIBERSORT algorithms. Finally, the correlation between the prognostic signature and gene mutation status was analyzed by employing "maftools" package. We identified a novel prediction model based on the ARGs of PLD1 and SLC36A1 with significant prognostic values for HCC in both univariate and multivariate Cox regression analysis, and patients were classified into high- or low-risk groups based on their risk scores. High-risk patients had significantly shorter overall survival (OS) times than low-risk patients (P=5e-4). According to the ROC curve analysis, the risk score had a higher predictive value than the other clinical characteristics. Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with HCC. Further, an external independent cohort of ICGC patients provided additional confirmation of the predictive efficacy of the model. We subsequently analyzed the differential immune densities of the two groups and discovered that various immune cells, including naïve B cells, resting memory cluster of differentiation (CD)4 T cells, regulatory T cells, M2 macrophages, and neutrophils, had considerably larger infiltrating densities in the high-risk group than the low-risk group. We established a robust autophagy-related risk model having a certain prediction accuracy for predicting the prognosis of HCC patients. Our findings will contribute to the definition of prognosis and establishment of personalized treatment interventions for HCC patients.

Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

To investigate the prognostic significance of N7-methylguanosine (m7G) regulators and immune infiltration in liver hepatocellular carcinoma (LIHC). The research measured predictive m7G genes in LIHC samples from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Data on the stemness index based on mRNA expression (mRNAsi), gene mutations, and corresponding clinical characteristics were obtained from TCGA and ICGC. Lasso regression was used to construct the prediction model to assess the m7G prognostic signals in LIHC. Based on these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key biological functions and pathways. The correlation between m7G RNA methylation regulators and the prognosis and immune infiltration of LIHC was evaluated. There were 21 m7G-related differentially expressed genes (DEGs) in LIHC and healthy tissues, and LIHC patients could be divided into two categories by consensus clustering of these DEGs. A five-gene predictive approach was employed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Patients in the low-risk group showed a significantly higher survival rate compared with those in the high-risk group (P=0.001). Validations using the ICGC database. Also, univariate and multivariate Cox regression analyses suggested that the risk score produced by the predictive model is an independent predictor for LIHC [hazard ratio (HR): 1.848, 95% confidence interval (CI): 1.286-2.656; HR: 2.597, 95% CI: 1.358-4.965]. The ROC curves of the ICGC cohort revealed that the five-gene prediction model performed well [area under the curve (AUC) =0.642 at 1 year, AUC =0.686 at 2 years, and AUC =0.667 at 3 years]. Immuno-oncology scoring revealed that in the high-risk group, among 16 immune cells, the expressions of neutrophils and natural killer (NK) cells were low and that of regulatory T-cells (Tregs) was high. LIHC occurrence and progression are linked to m7G-related genes. Corresponding prognostic models help forecast the prognosis of LIHC patients. m7G-related genes and associated immune cell infiltration in the TME may serve as potential therapeutic targets in LIHC, which requires further trials. In addition, the m7G-related gene signature offers a viable alternative to predict LIHC, and these m7G-related genes show a prospective research area for LIHC targeted treatment in the future.

A novel refined pyroptosis and inflammasome-related genes signature for predicting prognosis and immune microenvironment in pancreatic ductal adenocarcinoma

Pyroptosis is an inflammatory form of cell death, which plays a key role in the development of auto-inflammation and cancer. This study aimed to construct a pyroptosis and inflammasome-related genes for predicting prognosis of the pancreatic ductal adenocarcinoma (PDAC). This study was based primarily on the one-way analysis of variance, univariate Cox regression analysis, Least absolute shrinkage and selection operator (LASSO) Cox regression, a risk-prognostic signature, gene set variation analysis (GSVA), and immune microenvironment analysis, using PDAC data from The Cancer Genome Atlas and International Cancer Genome Consortium databases for the analysis of the role of 676 pyroptosis and inflammasome-related genes in PDAC retrieved from the Reactome and GeneCards databases. Lastly, we collected six paired PDAC and matched normal adjacent tissue samples to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR). We identified 18 candidate pyroptosis and inflammasome-related genes that differed significantly between pathologic grades (stages) of PDAC patients. The univariate Cox and LASSO analyses pointed to six genes as the best variables for constructing a prognostic signature, including ACTA2, C1QTNF9, DNAH8, GATM, LBP, and NGF. The results of the risk prognostic model indicated that the AUCs at 1, 3, and 5 years were greater than 0.62. GSVA revealed that ‘GLYCOLYSIS’, ‘P53 PATHWAY’, ‘KRAS SIGNALING UP’, and ‘INFLAMMATORY RESPONSE’ hallmark gene sets were associated with the risk score. The high-risk group was associated with poor prognosis and was characterized by a lower infiltration of cells involved in anti-tumor immunity; whereas the low-risk group with higher T cells, NK cells, and macrophages showed relatively better survival and significantly higher upregulation of cytolytic scores and inflammation scores. Additionally, crucial pyroptosis and inflammasome-related genes were further validated by qRT-PCR. Our study revealed the prognostic role of the pyroptosis and inflammasome-related genes in PDAC for the first time. Simultaneously, the biological and prognostic heterogeneity of PDAC had been demonstrated, deepening our molecular understanding of this tumor.

Development and validation of a gene signature for pancreatic cancer: based on inflammatory response-related genes.

Pancreatic cancer (PC) is one of the most common malignant tumors in the world with a poor prognosis. There were limited studies investigating the genetic signatures associated with inflammatory responses, tumor microenvironment (TME), and tumor drug sensitivity prediction. In the Cancer Genome Atlas (TCGA) dataset, we constructed an inflammatory response-related genes prognostic signature for PC, and predictive ability of the model was assessed via the International Cancer Genome Consortium (ICGC) database. Then, we explored the differences of TME, immune checkpoint genes and drug resistance genes, and the cancer cell sensitivity to chemotherapy drugs between different risk score group. Based on the TCGA and ICGC databases, we constructed and validated a prognostic model, which consisted of 5 genes (including AHR, F3, GNA15, IL18, and INHBA). Moreover, the prognostic model was independent prognostic factors affecting overall survival (OS). The low-risk score group had better OS, and lower stromal score, compared with patients in the high-risk score group. The difference of antigen-presenting cells, T cell regulation, and drug resistance genes between different risk score groups was found. In addition, the immune checkpoint genes were positively correlation to risk score. The expression levels of AHR, GNA15, IL18, and INHBA were related to the sensitivity of anti-tumor chemotherapy drugs. Gene set enrichment analysis (GSEA) showed significant pathway such as calcium signaling pathway and p53 signaling pathway. We successfully constructed a 5-inflammatory response-related gene signature to predict survival, TME, and cancer cell sensitivity to chemotherapy drugs in PC patients. Furthermore, substantiation was warranted to verify the role of these genes in tumorigenesis.

基于网络和基因差异表达信息的癌症致病基因预测

<p indent="0mm">In genome research, the discovery of disease-causing pathogenic genes is a major challenge in genome research. Recently, using the available biological data, many researchers have employed computational methods for pathogenic gene prediction. However, most of these computational methods are based on gene interaction networks or other similar networks, and the potential connection between the local network of specific genes and their differential expression information is rarely considered. This paper explores the biological properties of cancerous pathogenic genes and their neighbors based on a local network structure and the differential expression information of genes. Furthermore, machine learning methods are employed for predicting cancerous pathogenic genes based on the newly discovered biological properties. First, the expression data of 21 cancers and their pathogenic genes are obtained from the TCGA (The Cancer Genome Atlas) and OMIM (Online Mendelian Inheritance in Man) databases. Human-protein interaction and tissue-specific interaction networks corresponding to each cancer gene are used as background networks to analyze the potential biological properties between the neighborhood information of different networks and the changed information of the gene expression. Then, based on the discovered biological properties, a vector representation method for gene node features is established, and the support vector machine is employed for pathogenic gene prediction. Experimental results are verified using ICGC (International Cancer Genome Consortium), COSMIC (Catalogue Of Somatic Mutations In Cancer), NCG (Network of Cancer Genes), OncoKB (Oncology Knowledge Base), and other standard databases, as well as using related literature, disease annotations and pathway enrichments. Results show that the definition of gene features can be used to distinguish cancerous pathogenic genes from other genes, thus providing powerful hypotheses for cancerous pathogenic gene prediction. Furthermore, reliable pathogenic gene candidates are obtained for related biological experiments to promote research on the pathogenic mechanism of cancer, a complex disease.

Identification and validation of the necroptosis-related gene signature related to prognosis and tumor immune in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth most common cancer, which is characterized by complicated etiology, excessive heterogeneity, and poor prognosis. Necroptosis is a new kind of programmed cell death, which is intently associated with the occurrence and development of tumors. Although researchers have had a deep understanding of necroptosis in recent years, the expression level of necroptosis-related genes in HCC and its relationship with the survival time of HCC patients are not clear. According to the expression of necroptosis-related genes and the survival of HCC patients, HCC patients in the TCGA database were divided into 2 groups that were relatively independent of each other. The genes related to the survival time of HCC patients were screened from the 2 groups of differentially expressed genes. By using the Least Absolute Shrinkage and Selection Operator Cox regression analysis, the optimal λ value was obtained, and the 10-gene signature model was established. According to the median risk score of the TCGA cohort, HCC patients were averagely divided into high- and low-risk groups. Compared with the low-risk group, the death toll of the high-risk group was relatively higher and the survival time was relatively shorter. Principal component analysis and t-distributed stochastic neighbor embedding analysis showed that there was a significant separation between high- and low-risk groups. Through Kaplan-Meier analysis, it was found that the survival time of HCC patients in the high-risk group was significantly shorter than that in the low-risk group. Through receiver operating characteristic analysis, it was found that the sensitivity and specificity of the model were good. We also make a comprehensive analysis of the international cancer genome consortium database as a verification queue and prove the reliability of the 10-gene signature model. Gene Ontolog, Kyoto Encyclopedia of Genes and Genomes, and single-sample gene set enrichment analysis showed that many biological processes and pathways related to immunity had been enriched, and the antitumor immune function was weakened in the high-risk population. The risk score can be considered as an independent prognostic factor to predict the prognosis of patients with HCC, and necroptosis-related genes are also closely related to tumor immune function.

Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases.

BackgroundKidney cancer (KC) is one of the most challenging cancers due to its delayed diagnosis and high metastasis rate. The 5-year survival rate of KC patients is less than 11.2%. Therefore, identifying suitable biomarkers to accurately predict KC outcomes is important and urgent.MethodsCorresponding data for KC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. Systems biology/bioinformatics/computational approaches were used to identify suitable biomarkers for predicting the outcome and immune landscapes of KC patients.ResultsWe found two ferroptosis- and immune-related differentially expressed genes (FI-DEGs) (Klotho (KL) and Sortilin 1 (SORT1)) independently correlated with the overall survival of KC patients. The area under the curve (AUC) values of the prognosis model using these two FI-DEGs exceeded 0.60 in the training, validation, and entire groups. The AUC value of the 1-year receiver operating characteristic (ROC) curve reached 0.70 in all the groups.ConclusionsOur present study indicated that KL and SORT1 could be prognostic biomarkers for KC patients. Whether this model can be used in clinical settings requires further validation.

Integrated pan-cancer analysis of CSMD2 as a potential prognostic, diagnostic, and immune biomarker.

The protein encoded by CUB and Sushi Multiple Domains 2 (CSMD2) is likely involved in regulating the complement cascade reaction of the immune system. However, current scientific evidence on the comprehensive roles of CSMD2 in pan-cancer is relatively scarce. Therefore, in this study, we explored the transcriptional level of CSMD2 in pan-caner using TCGA, GEO, and International Cancer Genome Consortium databases. Receiver operating characteristic curve analysis was used to investigate the diagnostic efficacy of CSMD2. The Kaplan-Meier Plotter and Oncolnc were used to investigate the correlation between CSMD2 expression and prognosis. Additionally, we analyzed the correlation between epigenetic methylation and CSMD2 expression in various cancers based on UALCAN, as well as, the correlation between CSMD2 and tumor mutational burden (TMB), microsatellite instability (MSI), and tumor neoantigen burden (TNB) in tumors. TIMER2.0 database was employed to investigate the correlation between CSMD2 and immune cells in the tumor microenvironment and immune checkpoints. Based on TISIDB, the correlation between CSMD2 and MHC molecules and immunostimulators was analyzed. Ultimately, we observed with a pan-cancer analysis that CSMD2 was upregulated in most tumors and had moderate to high diagnostic efficiency, and that high expression was closely associated with poor prognosis in patients with tumors. Moreover, hypermethylation of CSMD2 promoter and high levels of m6A methylation regulators were also observed in most cancers. CSMD2 expression was negatively correlated with TMB and MSI in stomach adenocarcinoma (STAD) and stomach and esophageal carcinoma (STES), as well as with tumor mutational burden, microsatellite instability, and TNB in head-neck squamous cell carcinoma (HNSC). In most cancers, CSMD2 might be associated with immune evasion or immunosuppression, as deficient anti-tumor immunity and upregulation of immune checkpoints were also observed in this study. In conclusion, CSMD2 could serve as a promising prognostic, diagnostic and immune biomarker in pan-cancer.

Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Bioinformatics and the Role of NR6A1 in the Progression of HCC.

Background and AimsGenerally acceptable prognostic models for hepatocellular carcinoma (HCC) are not available. This study aimed to establish a prognostic model for HCC by identifying immune-related differentially expressed genes (IR-DEGs) and to investigate the potential role of NR6A1 in the progression of HCC.MethodsBioinformatics analysis using The Cancer Genome Atlas and ImmPort databases was used to identify IR-DEGs. Lasso Cox regression and multivariate Cox regression analysis were used to establish a prognostic model of HCC. Kaplan-Meier analysis and the receiver operating characteristic (ROC) curves were used to evaluate the performance of the prognostic model, which was further verified in the International Cancer Genome Consortium (ICGC) database. Gene set enrichment analysis was used to explore the potential pathways of NR6A1. Cell counting kit 8, colony formation, wound healing, and Transwell migration assays using Huh7 cells, and tumor formation models in nude mice were conducted.ResultsA prognostic model established based on ten identified IR-DEGs including HSPA4, FABP6, MAPT, NDRG1, APLN, IL17D, LHB, SPP1, GLP1R, and NR6A1, effectively predicted the prognosis of HCC patients, was confirmed by the ROC curves and verified in ICGC database. NR6A1 expression was significantly up-regulated in HCC patients, and NR6A1 was significantly associated with a low survival rate. Gene set enrichment analysis showed the enrichment of cell cycle, mTOR, WNT, and ERBB signaling pathways in patients with high NR6A1 expression. NR6A1 promoted cell proliferation, invasiveness, migration, and malignant tumor formation and growth in vitro and in vivo.ConclusionsAn effective prognostic model for HCC, based on a novel signature of 10 immune-related genes, was established. NR6A1 was up-regulated in HCC and was associated with a poor prognosis of HCC. NR6A1 promoted cell proliferation, migration, and growth of HCC, most likely through the cell cycle, mTOR, WNT, and ERBB signaling pathways.

Differentially expressed liver exosome-related genes as candidate prognostic biomarkers for hepatocellular carcinoma.

BackgroundExosomes are involved in cell-to-cell communication, neovascularization, cancer metastasis, and drug resistance, which all play an important role in the occurrence and progression of hepatocellular carcinoma (HCC). Because there are few mechanistic studies about the function of exosomes in HCC, the goals of this study were to identify exosome-related genes in HCC, to establish a reliable prognostic model for HCC, and to explore underlying mechanisms.MethodsThe exoRBase and The Cancer Genome Atlas (TCGA) databases were used to analyze differentially expressed genes (DEGs). Cox regression and least absolute shrinkage and selection operator analyses were used to identify DEGs closely related to the overall survival of patients with HCC. An exosome-related prognostic model was then constructed in TCGA and validated in the International Cancer Genome Consortium database. A nomogram was developed to predict survival. CIBERSORT was used to estimate the abundance of different types of immune cells. Immunotherapy-related DEGs were used to predict the effect of immunotherapy.ResultsForty-eight exosome-related DEGs were obtained; of them, five [exportin 1 (XPO1), lysosomal thiol reductase (IFI30), F-box protein 16 (FBXO16), calmodulin 1 (CALM1), MORC family CW-type zinc finger 3 (MORC3)] were selected to construct a predictive model. Patients with HCC were then divided into low- and high-risk groups using the best cut-off value, as determined by the X-tile software. Prognosis was significantly poorer in the high-risk than in the low-risk group (P=0.009; hazard ratio =2.65). Features related to exosomes were found to positively regulate immune response. Further analysis showed a higher risk score was associated with higher expression of immune checkpoint molecules, including programmed death ligand 1 (PD-L1), programmed death ligand 2 (PD-L2), T cell Ig and ITIM domain (TIGIT), and indoleamine-2,3-dioxygenase 1 (IDO1).ConclusionsThis study has identified a novel signature based on exosome-related genes that has potential as a prognostic biomarker for HCC. Our research provides an immunological perspective for the development of precision treatment for HCC.

Analysis of the OX40/OX40L immunoregulatory axis combined with alternative immune checkpoint molecules in pancreatic ductal adenocarcinoma.

Immune checkpoint modulation has been a vital therapeutic option in many malignancies, and targeting of novel immune checkpoints, including OX40/OX40L costimulatory signaling, is being assessed in clinical trials. However, little is known about the role of OX40 and OX40L in pancreatic ductal adenocarcinoma (PDAC). Thus, we investigated the clinical significance of OX-40 and OX40L and their associations with alternative immune checkpoints, immune infiltrates, clinicopathological features, and clinical outcomes. We performed multiplexed immunofluorescence staining for OX40, OX40L, CD8, and CD68 using tissue microarrays from 255 patients. Immunohistochemistry data for PD-L1, B7-H3, B7-H4, CD3, and Foxp3 were analyzed. And the RNA sequencing data of OX40/OX40L in The Cancer Genome Atlas and International Cancer Genome Consortium databases were also evaluated. The positive rates for OX40 on tumor cells (TCs) and immune cells (ICs) were 8.6% and 10.2%, respectively, and the positive rates for OX40L on TCs, ICs, and macrophages were 20%, 40.4%, and 12.9%, respectively. OX40 was associated with favorable clinicopathological features. OX40+ on ICs, OX40L+ on TCs, or OX40L+ on macrophages, rather than the total gene and protein levels of OX40/OX40L, were associated with improved survival. OX40+ on ICs and OX40L+ on macrophages were independent factors of clinical outcomes. Moreover, we could more accurately stratify patients through the combination of OX40 on ICs and OX40L on TCs, and patients with OX40+ ICs and OX40L+CK+ showed the best outcome. And we demonstrated that patients with OX40-ICs and low CD8+ T cells infiltration had unfavorable survival. Intriguingly, OX40+ ICs or OX40L+ macrophages demonstrated superior survival in patients with PD-L1 negativity than in those with PD-L1 positivity. Furthermore, OX40+ ICs were correlated with negative B7-H4 on TCs, high densities of CD3 T cells, and high densities of Foxp3 T cells; OX40+ TCs and OX40L+ TCs were associated with low densities of Foxp3 T cells. We identified OX40 and OX40L as promising predictors for prognosis in PDAC.

A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

BackgroundRenal cell carcinoma (RCC) is a third most common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state.MethodsIn this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. Then we established a prognostic risk model consisting of the genes most related to prognosis from four signatures to value prognosis of the RCC samples via Kaplan–Meier (KM) survival analysis. An independent data from International Cancer Genome Consortium (ICGC) database were used to test the predictive stability of the model. Furthermore, we performed landscape analysis to assess the difference of gene mutant in the RCC samples from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform.ResultsWe used four genetic signatures to construct prognostic risk models respectively and found that each of the models could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. A comprehensive prognostic risk model was constructed by 8 candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from TCGA database into high-risk and low-risk groups with a significant difference in cancer-specific survival (CSS). The stability of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. Landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC.ConclusionsSum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.

A novel molecular subtype of hepatocellular carcinoma based on the tumor purity and tumor microenvironment-related polygenic risk scores.

e15040 Background: The molecular classification of hepatocellular carcinoma (HCC) plays a vital role in the management of precision therapy. Several molecular classification systems, mainly focusing on the tumor cells, were developed according to distinct omics features of HCC. Heterogeneous tumor microenvironment (TME) in HCC is recognized as a crucial part of inter-tumor heterogeneity. Few molecular classifications of HCC have so far taken into account both the related molecules of tumor cells and TME. Methods: Wefirstly identified the tumor purity (TP)-related and TME-related genes in the differentially expressed genes in HCC using GSE14520 data set, and separately constructed the TP-related and TME-related polygenic risk score (PRS). Secondly, according to the TP-related and TME-related PRSs, we proposed the TP-TME risk classification which was validated in two external data sets from The Cancer Genome Atlas Program and International Cancer Genome Consortium database. We also performed functional enrichment and drug repositioning analysis to reveal the potential biological heterogeneity among different subtypes. Results: The three TP-TME risk subtypes of HCC had significantly different prognosis and biological characteristics. The TP-TME low risk subtype had the best prognosis and was characterized by well-differentiated, the TP-TME high risk subtype had the worst prognosis and was characterized by aberrant activation of TGFβ and WNT pathways, and the TP-TME high risk subtype had the moderate prognosis and was characterized by exhibited activated MYC targets and proliferation-related gene sets. We also found that these three TP-TME risk subtypes may respond differently to immunotherapy (e.g., immune checkpoint inhibitors and chimeric antigen receptor-modified T cells) or other drug therapies. Conclusions: By combining separately constructed TP-related PRS and TME-related PRS, we proposed and validated a novel molecular classification system, the TP-TME risk subtyping system, to divide patients with HCC into three subtypes with distinct biological characteristics and prognosis. These findings highlight the significant clinical implications of the TP-TME risk subtyping system and provide potential personalized immunotherapy strategies for patients with HCC.

A Predictive Model for Prognosis and Therapeutic Response in Hepatocellular Carcinoma Based on a Panel of Three MED8-Related Immunomodulators.

The current tumor-node-metastasis (TNM) system is limited in predicting the survival and guiding the treatment of hepatocellular carcinoma (HCC) patients since the TNM system only focuses on the anatomical factors, regardless of the intratumoral molecule heterogeneity. Besides, the landscape of intratumoral immune genes has emerged as a prognostic indicator. The mediator complex subunit 8 (MED8) is a major polymerase regulator and has been described as an oncogene in renal cell carcinoma, but its pathophysiological significance of HCC and its contribution to the prognosis of HCC remain unclear. Here, we aimed to discuss the expression profile and clinical correlation of MED8 in HCC and construct a predictive model based on MED8-related immunomodulators as a supplement to the TNM system. According to our analyses, MED8 was overexpressed in HCC tissues and increased expression of MED8 was an indicator of poor outcome in HCC. The knockdown of MED8 weakened the proliferation, colony forming, and migration of HepG2 and Huh7 cells. Subsequently, a predictive model was identified based on a panel of three MED8-related immunomodulators using The Cancer Genome Atlas (TCGA) database and further validated in International Cancer Genome Consortium (ICGC) database. The combination of the predictive model and the TNM system could improve the performance in predicting the survival of HCC patients. High-risk patients had poor overall survival in TCGA and ICGC databases, as well as in subgroup analysis with early clinicopathology classification. It was also found that high-risk patients had a higher probability of recurrence in TCGA cohort. Furthermore, low-risk score indicated a better response to immunotherapy and drug therapy. This predictive model can be served as a supplement to the TNM system and may have implications in prognosis stratification and therapeutic guidance for HCC.

Ferroptosis-Related Long Noncoding RNAs as Prognostic Marker for Colon Adenocarcinoma.

Background The incidence of colon adenocarcinoma (COAD) has been increasing over time. Although ferroptosis and long noncoding RNAs (lncRNAs) have been extensively reported to participate in the tumorigenesis and development of COAD, few studies have investigated the role of ferroptosis-related lncRNAs in the prognosis of COAD. Methods Gene-sequencing and clinical data for COAD were obtained from The Cancer Genome Atlas database. The coexpression network was constructed using known ferroptosis-related genes. Cox and least absolute shrinkage and selection operator regression were used to screen ferroptosis-related lncRNAs with prognostic value and to identify a predictive model of COAD. Patients with COAD were divided into low- and high-risk groups according to their risk score. Cases of COAD in the International Cancer Genome Consortium database were included as the testing cohort. Results In total, nine lncRNAs (LINC02381, AC105219.1, AC009283.1, LINC01011, ELFN1-AS1, EIF3J-DT, NKILA, LINC01063, and SNHG16) were considered prognostic factors for COAD. Then, a risk score model was established. The overall survival rate of COAD patients was negatively associated with the risk score. Kaplan–Meier analyses in the original and testing cohorts showed similar results. The expression of the lncRNAs in tissue was consistent with the risk score, and the relationship with tumor mutation burden, immunity, and drug sensitivity presented a marked link between the signature and COAD. A nomogram was established for clinical applications. Conclusions Nine ferroptosis-related lncRNAs and the established signature have a certain predictive value for prognosis of COAD patients and can be used as potential research targets for exploring treatment of COAD.

Knockdown of AKR1C3 Promoted Sorafenib Sensitivity Through Inhibiting the Phosphorylation of AKT in Hepatocellular Carcinoma.

BackgroundSorafenib, which can induce ferroptosis, is a multikinase inhibitor for enhancing survival in advanced hepatocellular carcinoma (HCC). However, a considerable challenge for the treatment of HCC is sorafenib resistance. Therefore, targeting the relationship between sorafenib resistance and ferroptosis genes may provide a novel approach for the treatment of HCC.Materials and MethodsWe analyzed the gene expression and clinicopathological factors from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases (GSE109211/GSE62813). The statistical analysis was conducted in R. Cell proliferation was assayed by MTT, cell colony-forming assay, and wound healing assay. Immunofluorescence assay and Western blot were used to evaluate the expression of AKT.ResultsMany ferroptosis-related genes were upregulated in the sorafenib-resistant group. Aldo-keto reductase 1C3 (AKR1C3) was highly expressed in sorafenib-resistant patients, and the high expression of AKR1C3 was associated with the poor prognosis of patients from the TCGA and ICGC databases. MTT and colony-forming assays showing AKR1C3 overexpression enhanced the proliferation of HCC cells and acute sorafenib resistance. Knockdown of AKR1C3 inhibited the proliferation of HCC cells and increased the drug sensitivity of sorafenib. Immunofluorescence assay and Western blot proved that AKR1C3 promoted the phosphorylation of AKT.ConclusionAKR1C3 can induce sorafenib resistance through promoting the phosphorylation of AKT in HCC. AKR1C3 inhibitors may be used in conjunction with sorafenib to become a better therapeutic target for HCC.

Identification of a Pyroptosis-Related Prognostic Signature Combined With Experiments in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor prognosis. There is a necessary search for improvement in diagnosis and treatment methods to improve the prognosis. Some useful prognostic markers of HCC are still lacking. Pyroptosis is a type of programmed cell death caused by the inflammasome. It is still unknown whether pyroptosis-related genes (PRGs) are involved in the prognosis in HCC. The gene expression and clinical data of LIHC (liver hepatocellular carcinoma) patients were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database (ICGC). In this study, we identified 40 PRGs that were differentially expressed between LIHC and normal liver tissues. Based on the TCGA-LIHC cohort, a 9-gene prediction model was established with the Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression. The risk score was calculated according to the model in the TCGA-LIHC cohort and the ICGC-LIHC cohort. Utilizing the median risk score from the TCGA cohort, LIHC patients from the ICGC-LIHC cohort were divided into two risk subgroups. The Kaplan–Meier (KM) survival curves demonstrated that patients with lower risk scores had significantly favorable overall survival (OS). Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of LIHC patients in both the TCGA-LIHC cohort and the ICGC-LIHC cohort. Functional enrichment and immune function analysis were carried out. Furthermore, a nomogram based on risk score, age, gender, and tumor stage was used to predict mortality of patients with LIHC. Moreover, KM survival analysis was performed for 9 genes in the risk model, among which CHMP4A, SCAF11, and GSDMC had significantly different results and the ceRNA network was constructed. Based on the core role of SCAF11, we performed loss-of-function experiments to explore the function of SCAF11 in vitro. Suppression of SCAF11 expression inhibited the proliferation, attenuated the migration and invasion, and induced apoptosis of liver cancer cell lines. In conclusion, the pyroptosis-related model and nomogram can be utilized for the clinical prognostic prediction in LIHC. This study has demonstrated for the first time that SCAF11 promotes the progression of liver cancer.

Oncolytic Orf virus licenses NK cells via cDC1 to activate innate and adaptive antitumor mechanisms and extends survival in a murine model of late-stage ovarian cancer

BackgroundNovel therapies are needed to improve outcomes for women diagnosed with ovarian cancer. Oncolytic viruses are multifunctional immunotherapeutic biologics that preferentially infect cancer cells and stimulate inflammation with the potential...

Development and validation of an immune-related prognosis signature associated with hypoxia and ferroptosis in hepatocellular carcinoma.

BackgroundHypoxia and ferroptosis are crucial in the occurrence and development of hepatocellular carcinoma (HCC), and they both affect the immune status of the tumor microenvironment. Previous studies have also shown a link between hypoxia and ferroptosis.Patients and methodsIn all, 814 HCC cases from The Cancer Genome Atlas and Gene Expression Omnibus databases were used as the discovery cohort, and 230 HCC cases from the International Cancer Genome Consortium database were used as the validation cohort. Hypoxia subtypes and ferroptosis subtypes were identified by consensus cluster analysis according to 174 hypoxia‐related genes and 193 ferroptosis‐related genes. The prognostic signature was constructed using the Cox and LASSO regression analyses, and two risk groups were identified. A comprehensive analysis of the clinical and immune characteristics between the two risk groups was further performed.ResultsTwo hypoxia subtypes and two ferroptosis subtypes were distinguished and verified; subsequently, a five‐gene prognostic signature was constructed and the risk score could be acquired by the following formula: risk score = 0.0604*Expression (CA9)−0.0714*Expression (ANXA10) + 0.1501*Expression (CDC20)−0.0853*Expression (CYP7A1) + 0.0530*Expression (SPP1). Compared with the low‐risk group, the high‐risk group had a worse prognosis. The high‐risk group also showed a higher level of immune infiltration than the low‐risk group, and immune checkpoints were generally upregulated in the high‐risk group. The antigen presentation ability of the low‐risk group was poor, which may be related to the immune escape mechanism. Drug sensitivity analysis indicated that the high‐ and low‐risk groups were sensitive to tyrosine kinase inhibitors and chemotherapeutic drugs, respectively.ConclusionThe hypoxia‐, ferroptosis‐, and immune‐associated prognostic signature we constructed could stratify patients with HCC and guide precise treatment.