International Cancer Genome Consortium Cohort Research Articles

Subtype recognition and identification of a prognosis model characterized by antibody-dependent cell phagocytosis-related genes in breast cancer.

Breast cancer (BC) is a heterogeneous tumor with a variety of etiology and clinical features. Antibody-dependent cell phagocytosis (ADCP) is the last step of immune checkpoint inhibition (ICI), and macrophages detect and recognize tumor cells, then destroy and engulf tumor cells. Despite the large number, negative regulators that inhibit phagocytic activity are still a key obstacle to the full efficacy of ICI. An ADCP-related risk score prognostic model for risk stratification as well as prognosis prediction was established in the Cancer Genome Atlas (TCGA) cohort. The predictive value of ADCP risk score in prognosis and immunotherapy was also further validated in the TCGA along with International Cancer Genome Consortium cohorts. To promote the clinical application of the risk score, a nomogram was established, with its effectiveness verified by different methods. In this study, the genes collected from previous studies were defined as ADCP-related genes. In BC patients, two ADCP-related subtypes were identified. The immune characteristics and prognostic stratification were significant different between them. We identified two subtypes associated with ADCP gene expression in breast cancer. They have significant differences in immune cells, molecular functions, HLA family genes, immune scores, stromal scores, and inflammatory gene expression, which have important guiding significance for the selection of clinical treatment methods. At the same time, we constructed a risk model based on ADCP, and the risk score can be used as a good indicator of prognosis, providing potential therapeutic advantages for chemotherapy and immunotherapy, thus helping the clinical decision-making of BC patients.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration.

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

The sorafenib resistance-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

ABSTRACT Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Most patients with advanced HCC acquire sorafenib resistance. Drug resistance reflects the heterogeneity of tumors and is the main cause of tumor recurrence and death.We identified and validated sorafenib resistance related-genes (SRGs) as prognostic biomarkers for HCC. We obtained SRGs from the Gene Expression Omnibus and selected four key SRGs using the least absolute shrinkage and selection operator, random forest, and Support Vector Machine-Recursive feature elimination machine learning algorithms. Samples from the The Cancer Genome Atlas (TCGA)-HCC were segregated into two groups by consensus clustering. Following difference analysis, 19 SRGs were obtained through univariate Cox regression analysis, and a sorafenib resistance model was constructed for risk stratification and prognosis prediction. In multivariate Cox regression analysis, the risk score was an independent predictor of overall survival (OS). Patients classified as high-risk were more sensitive to other chemotherapy drugs and showed a higher expression of the common immune checkpoints. Additionally, the expression of drug-resistance genes was verified in the International Cancer Genome Consortium cohort. A nomogram model with a risk score was established, and its prediction performance was verified by calibration chart analysis of the TCGA-HCC cohort. We conclude that there is a significant correlation between sorafenib resistance and the tumor immune microenvironment in HCC. The risk score could be used to identify a reliable prognostic biomarker to optimize the therapeutic benefits of chemotherapy and immunotherapy, which can be helpful in the clinical decision-making for HCC patients.

Cellular senescence throws new insights into patient classification and pharmacological interventions for clinical management of hepatocellular carcinoma.

Cellular senescence, a state of stable growth arrest, is intertwined with human cancers. However, characterization of cellular senescence-associated phenotypes in hepatocellular carcinoma (HCC) remains unexplored. To address this issue, we delineated cellular senescence landscape across HCC. We enrolled two HCC datasets, TCGA-LIHC and International Cancer Genome Consortium (ICGC). Unsupervised clustering was executed to probe tumor heterogeneity based upon cellular senescence genes. Least absolute shrinkage and selection operator algorithm were utilized to define a cellular senescence-relevant scoring system. TRNP1 expression was measured in HCCs and normal tissues through immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. The influence of TMF-regulated nuclear protein (TRNP)1 on HCC senescence and growth was proven via a series of experiments. TCGA-LIHC patients were classified as three cellular senescence subtypes, named C1-3. The robustness and reproducibility of these subtypes were proven in the ICGC cohort. C2 had the worst overall survival, C1 the next, and C3 the best. C2 presented the highest levels of immune checkpoints, abundance of immune cells, and immunogenetic indicators. Thus, C2 might possibly respond to immunotherapy. C2 had the lowest somatic mutation rate, while C1 presented the highest copy number variations. A cellular senescence-relevant gene signature was generated, which can predict patient survival, and chemo- or immunotherapeutic response. Experimentally, it was proven that TRNP1 presented the remarkable upregulation in HCCs. TRNP1 knockdown induced apoptosis and senescence of HCC cells and attenuated tumor growth. These findings provide a systematic framework for assessing cellular senescence in HCC, which decode the tumor heterogeneity and tailor the pharmacological interventions to improve clinical management.

An 8-gene predicting survival model of hepatocellular carcinoma (HCC) related to pyroptosis and cuproptosis

BackgroundThe study aimed to establish a prognostic survival model with 8 pyroptosis-and-cuproptosis-related genes to examine the prognostic effect in patients of hepatocellular carcinoma (HCC).MethodsWe downloaded gene expression data and clinical information of HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The clustering analysis and cox regression with LASSO were used for constructing an 8 PCmRNAs survival model. Using TCGA, ICGC and GEO cohort, the overall survival (OS) between high- and low- risk group was determined. We also evaluated independent prognostic indicators using univariate and multivariate analyses. The relatively bioinformatics analysis, including immune cell infiltration, function enrichment and drug sensitivity analyses, was performed as well. The gene expression of 8 PCmRNAs in vitro were validated in several HCC cell lines by qRT-PCR and Western blot. The relationship between GZMA and Fludarabine were further checked by CCK-8 assay.ResultsThe survival prognostic model was constructed with ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA using data from TCGA cohort. The ICGC and GEO cohort were used for model validation. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Risk scores had the highest predictable value for survival among Stage, Age, Gender and Grade. Most Immune cells and immune functions were decreased in high-risk group. Besides, function enrichment analyses showed that steroid metabolic process, hormone metabolic process, collagen − containing extracellular matrix, oxidoreductase activity and pyruvate metabolism were enriched. Potential drugs targeted different PCDEGs like Nelarabine, Dexamethasone and Fludarabine were found as well. ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NOD1 were upregulated while NLRP6 and GZMA were downregulated in most HCC cell lines. The potential therapy of Fludarabine was demonstrated when GZMA was low expressed in Huh7 cell line.ConclusionWe constructed a novel 8-gene (ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA) prognostic model and explored potential functional information and microenvironment of HCC, which might be worthy of clinical application. In addition, several potential chemotherapy drugs were screened and Fludarabine might be effective for HCC patients whose GZMA was low expressed.

Identification of neoantigens and immunological subtypes in clear cell renal cell carcinoma for mRNA vaccine development and patient selection.

Clear cell renal cell carcinoma (ccRCC) is a common urological malignancy with diverse histological types. This study aimed to detect neoantigens in ccRCC to develop mRNA vaccines and distinguish between ccRCC immunological subtypes for construction of an immune landscape to select patients suitable for vaccination. Using The Cancer Genome Atlas SpliceSeq database, The Cancer Genome Atlas, and the International Cancer Genome Consortium cohorts, we comprehensively analysed potential tumour antigens of ccRCC associated with aberrant alternative splicing, somatic mutation, nonsense-mediated mRNA decay factors, antigen-presenting cells, and overall survival. Immune subtypes (C1/C2) and nine immune gene modules of ccRCC were identified by consistency clustering and weighted correlation network analysis. The immune landscape as well as molecular and cellular characteristics of immunotypes were assessed. Rho-guanine nucleotide exchange factor 3 (ARHGEF3) was identified as a new ccRCC antigen for development of an mRNA vaccine. A higher tumour mutation burden, differential expression of immune checkpoints, and immunogenic cell death were observed in cases with the C2 immunotype. Cellular characteristics increased the complexity of the immune environment, and worse outcomes were observed in ccRCC cases with the C2 immunotype. We constructed the immune landscape for selecting patients with the C2 immunotype suitable for vaccination.

A Novel Platinum Resistance-Related Immune Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer.

Ovarian cancer (OV) is a highly heterogeneous gynecological tumor that makes the prognostic prediction challenging. Resistance to platinum-based chemotherapy is associated with a poor prognosis in OV. There seems to be an overlap between molecular mechanisms responsible for platinum resistance and immunogenicity in OV. However, the predictive role of platinum resistance-related immune genes for OV prognosis needs to be further explored. In our study, the mRNA expression data of OV patients with corresponding clinical information were collected from The Cancer Genome Atlas (TCGA) cohort and International Cancer Genome Consortium (ICGC) cohort. A multigene signature was constructed for OV patients in the TCGA cohort using the least absolute shrinkage and selection operator (LASSO) Cox regression model according to the optimal value of λ and was validated in the ICGC cohort. Furthermore, we performed functional analysis to explore the immune status between low- and high-risk groups based on the median value of the risk score for the multigene signature. Our data showed that there were 41.1% of the platinum resistance-related genes which differentially expressed between immune score low- and high-OV patients in the TCGA cohort. Univariate Cox regression analysis identified 30 differentially expressed genes (DEGs) associated with overall survival (OS) (P < 0.05). 14 genes were identified to construct a novel platinum resistance-related immune model for classifying OV patients into the low- and high- risk groups. Patients in the low-risk group showed significantly higher OS than those in the high-risk group (P < 0.0001 in the both TCGA and ICGC cohort), which was associated with different immune status for the two risk groups. A novel platinum resistance-related immune model can be used for prognostic prediction in OV. Targeting tumor immunity may be a therapeutic alternative for OV with platinum resistance.

Upregulated PPP1R14B is connected to cancer progression and immune infiltration in kidney renal clear cell carcinoma.

Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an oncogenic gene found in a variety of tumors, but its role in the prognosis and development of kidney renal clear cell carcinoma (KIRC) remains unknown. Our study aimed to determine whether PPP1R14B could be a prognostic biomarker for KIRC and its role in the development of KIRC. In this work, we used The Cancer Genome Atlas (TCGA) database to explore the expression of PPP1R14B in tumor tissues, its relationship with the prognosis of tumor patients, and its role in tumor occurrence and development. We validated our findings using the International Cancer Genome Consortium (ICGC) cohort, our clinical samples, and in vitro experiments. PPP1R14B was upregulated in KIRC compared to adjacent normal tissue. Moreover, multivariate analysis revealed that upregulated PPP1R14B expression was an independent risk factor for KIRC progression. High-PPP1R14B groups had shorter overall survival (OS) and disease-free survival (DFS) in TCGA and ICGC cohorts. We used Cell Counting Kit-8 (CCK8) and scratch wound healing assay to explore the proliferation and migration of KIRC cells following PPP1R14B knockdown. Our results indicated that PPP1R14B knockdown significantly reduced the proliferation and migration of KIRC cells in vitro. We also explored the possible cellular mechanisms of PPP1R14B through the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) analysis, and TISIDB analysis. The function enrich analysis revealed that PPP1R14B-related genes were mainly enriched in purine metabolism and the macromolecule catabolic process. PPP1R14B expression was associated with tumor-infiltrating immune cells (TIICs) in the TCGA cohort, and the results of single-cell RNA-seq (scRNA) further demonstrated that PPP1R14B expression was associated with the enhanced infiltration of CD8 + T lymphocytes. PPP1R14B may serve as a prognostic biomarker in KIRC, affect purine metabolism, activate immune infiltration, and promote KIRC cell migration.

Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is a common urinary cancer. Although diagnostic and therapeutic approaches for ccRCC have been improved, the survival outcomes of patients with advanced ccRCC remain unsatisfactory. Fatty acid metabolism (FAM) has been increasingly recognized as a critical modulator of cancer development. However, the significance of the FAM in ccRCC remains unclear. Herein, we explored the function of a FAM-related risk score in the stratification and prediction of treatment responses in patients with ccRCC. MethodsFirst, we applied an unsupervised clustering method to categorize patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets into subtypes and retrieved FAM-related genes from the MSigDB database. We discern differentially expressed genes (DEGs) among different subtypes. Then, we applied univariate Cox regression analysis followed by least absolute shrinkage and selection operator (LASSO) linear regression based on DEGs expression to establish a FAM-related risk score for ccRCC. ResultsWe stratified the three ccRCC subtypes based on FAM-related genes with distinct overall survival (OS), clinical features, immune infiltration patterns, and treatment sensitivities. We screened nine genes from the FAM-related DEGs in the three subtypes to establish a risk prediction model for ccRCC. Nine FAM-related genes were differentially expressed in the ccRCC cell line ACHN compared to the normal kidney cell line HK2. High-risk patients had worse OS, higher genomic heterogeneity, a more complex tumor microenvironment (TME), and elevated expression of immune checkpoints. This phenomenon was validated in the ICGC cohort. ConclusionWe constructed a FAM-related risk score that predicts the prognosis and therapeutic response of ccRCC. The close association between FAM and ccRCC progression lays a foundation for further exploring FAM-related functions in ccRCC.

Integrative Analysis Developing and Validating Potential Candidate Biomarkers for Cancer Stemness Features of Pan-Renal Cell Carcinoma

In our study, 49 key genes significantly associated with renal cell carcinoma (RCC) stemness were obtained. Next, we developed a molecular prognostic signature associated with stemness features of pan-RCC. The difference in overall survival (OS) between the high- and low-risk groups was statistically significant (p < .05). The area under the receiver operating characteristic curve for 1-year OS, 5-year OS, and 10-year OS was 0.759, 0.712, and 0.918, respectively. The results of validation in The Cancer Genome Atlas cohort and International Cancer Genome Consortium cohort revealed the predictive capability of this signature. Furthermore, we selected three genes and further validation showed that these three hub genes were potential hub biomarkers for pan-RCC stemness features.

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma

BackgroundAn immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).MethodsBased on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.ResultsThe IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.ConclusionsThis study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients. The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR. 11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong. TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma

BackgroundCuproptosis is recently emerging as a hot spot in cancer research. However, its role in pancreatic adenocarcinoma (PAAD) has not yet been clarified. This study aimed to explore the prognostic and therapeutic implications of cuproptosis-related genes in PAAD.MethodsTwo hundred thirteen PAAD samples from the International Cancer Genome Consortium (ICGC) were split into training and validation sets in the ratio of 7:3. The Cox regression analyses generated a prognostic model using the ICGC cohort for training (n = 152) and validation (n = 61). The model was externally tested on the Gene Expression Omnibus (GEO) (n = 80) and The Cancer Genome Atlas (TCGA) datasets (n = 176). The clinical characteristics, molecular mechanisms, immune landscape, and treatment responses in model-defined subgroups were explored. The expression of an independent prognostic gene TSC22D2 was confirmed by public databases, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC).ResultsA prognostic model was established based on three cuproptosis-related genes (TSC22D2, C6orf136, PRKDC). Patients were stratified into high- and low-risk groups using the risk score based on this model. PAAD patients in the high-risk group had a worse prognosis. The risk score was statistically significantly correlated with most clinicopathological characteristics. The risk score based on this model was an independent predictor of overall survival (OS) (HR = 10.7, p < 0.001), and was utilized to create a scoring nomogram with excellent prognostic value. High-risk patients had a higher TP53 mutation rate and a superior response to multiple targeted therapies and chemotherapeutic drugs, but might obtain fewer benefits from immunotherapy. Moreover, elevated TSC22D2 expression was discovered to be an independent prognostic predictor for OS (p < 0.001). Data from public databases and our own experiments showed that TSC22D2 expression was significantly higher in pancreatic cancer tissues/cells compared to normal tissues/cells.ConclusionThis novel model based on cuproptosis-related genes provided a robust biomarker for predicting the prognosis and treatment responses of PAAD. The potential roles and underlying mechanisms of TSC22D2 in PAAD need further explored.

Gene-based cancer-testis antigens as prognostic indicators in hepatocellular carcinoma

Cancer/testis antigens (CTAs) are reproductive tissue-restricted genes, frequently ectopic expressed in tumors. CTA genes associate with a poor prognosis in some solid tumors, due to their potential roles in the tumorigenesis and progression. However, whether CTAs relate with hepatocellular carcinoma (HCC) remains unclear. In this study, the prognostic signatures based on CTA genes were investigated and validated in three cohorts including Chinese HCC patients with hepatitis B virus infection (CHCC-HBV), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cohorts. Univariate, LASSO, and multivariate Cox regression analyses were used to screen prognostic genes and develop the prognostic gene signature. A prognosis model was established with six CTA genes (SSX1, CTCFL, OIP5, CEP55, NOL4, and TPPP2) in CHCC-HBV cohort, and further validated in the ICGC and TCGA cohorts. The CTA signature was an essential prognostic predictor independent of other clinical pathological factors. High-risk group exhibited up-regulated cell cycle-related and tumor-related pathways and more M0 macrophage, activated mast cell, activated memory CD4+ T cell, and memory B cell infiltration. Furthermore, CTA signature correlated with the sensitivity to multiple chemotherapy drugs. Our results highlighted that the CTA gene profiling was a prognostic assessment tool for HCC patients.

Identification and validation of a novel cuproptosis-related genes signature associated with prognosis, clinical implications and immunotherapy of hepatocellular carcinoma.

Background: Cuproptosis is a novel type of regulated cell death and is reported to promote tumor occurrence and progression. However, whether a cuproptosis-related signature has an impact on hepatocellular carcinoma (HCC) is still unclear. Materials and methods: We analyzed the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and searched for tumor types with different cuproptosis patterns through consistent clustering of cuproptosis genes. We then constructed a Cuproptosis-Related Genes (CRGs)-based risk signature through LASSO COX regression, and further analyzed its impact on the prognosis, clinical characteristics, immune cell infiltration, and drug sensitivity of HCC. Results: We identified the expression changes of 10 cuproptosis-related genes in HCC, and all the patients can be divided into two subtypes with different prognosis by applying the consensus clustering algorithm. We then constructed a cuproptosis-related risk signature and identified five CRGs, which were highly correlated with prognosis and representative of this gene set, namely G6PD, PRR11, KIF20A, EZH2, and CDCA8. Patients in the low CRGs signature group had a favorable prognosis. We further validated the CRGs signature in ICGC cohorts and got consistent results. Besides, we also discovered that the CRGs signature was significantly associated with a variety of clinical characteristics, different immune landscapes and drug sensitivity. Moreover, we explored that the high CRGs signature group was more sensitive to immunotherapy. Conclusion: Our integrative analysis demonstrated the potential molecular signature and clinical applications of CRGs in HCC. The model based on CRGs can precisely predict the survival outcomes of HCC, and help better guide risk stratification and treatment strategy for HCC patients.

Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases.

Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations. TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients. We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.

A bile acid-related prognostic signature in hepatocellular carcinoma

Due to the high mortality of hepatocellular carcinoma (HCC), its prognostic models are urgently needed. Bile acid (BA) metabolic disturbance participates in hepatocarcinogenesis. We aim to develop a BA-related gene signature for HCC patients. Research data of HCC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) online databases. After least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a BA-related prognostic signature in TCGA cohort based on differentially expressed prognostic BA-related genes. Then, the predictive performance of the signature was evaluated and verified in TCGA and ICGC cohort respectively. We obtained the risk score of each HCC patient according to the model. The differences of immune status and drug sensitivity were compared in patients that were stratified based on risk score. The protein and mRNA levels of the modeling genes were validated in the Human Protein Atlas database and our cell lines, respectively. In TCGA cohort, we selected 4 BA-related genes to construct the first BA-related prognostic signature. The risk signature exhibited good discrimination and predictive ability, which was verified in ICGC cohort. Patients were classified into high- and low-risk groups according to their median scores. The occurrence of death increased with increasing risk score. Low-risk patients owned favorable overall survival. High-risk patients possessed high immune checkpoint expression and low IC50 values for sorafenib, cisplatin and doxorubicin. Real-time quantitative PCR and immunohistochemical results validate expression of modeling genes in the signature. We constructed the first BA-related gene signature, which might help to identify HCC patients with poor prognosis and guide individualized treatment.

Prognostic prediction and immune infiltration analysis based on ferroptosis and EMT state in hepatocellular carcinoma.

Ferroptosis is one of the main mechanisms of sorafenib against hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) plays an important role in the heterogeneity, tumor metastasis, immunosuppressive microenvironment, and drug resistance of HCC. However, there are few studies looking into the relationship between ferroptosis and EMT and how they may affect the prognosis of HCC collectively. We downloaded gene expression and clinical data of HCC patients from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases for prognostic model construction and validation respectively. The Least absolute shrinkage and selection operator (LASSO) Cox regression was used for model construction. The predictive ability of the model was assessed by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve. We performed the expression profiles analysis to evaluate the ferroptosis and EMT state. CIBERSORT and single-sample Gene Set Enrichment Analysis (ssGSEA) methods were used for immune infiltration analysis. A total of thirteen crucial genes were identified for ferroptosis-related and EMT-related prognostic model (FEPM) stratifying patients into two risk groups. The high-FEPM group had shorter overall survivals than the low-FEPM group (p<0.0001 in the TCGA cohort and p<0.05 in the ICGC cohort). The FEPM score was proved to be an independent prognostic risk factor (HR>1, p<0.01). Furthermore, the expression profiles analysis suggested that the high-FEPM group appeared to have a more suppressive ferroptosis status and a more active EMT status than the low- FEPM group. Immune infiltration analysis showed that the myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs) were highly enriched in the high-FEPM group. Finally, a nomogram enrolling FEPM score and TNM stage was constructed showing outstanding predictive capacity for the prognosis of patients in the two cohorts. In conclusion, we developed a ferroptosis-related and EMT-related prognostic model, which could help predict overall survival for HCC patients. It might provide a new idea for predicting the response to targeted therapies and immunotherapies in 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.

DDX1 is a prognostic biomarker and correlates with immune infiltrations in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading lethal malignant tumors worldwide. DEAD-box (DDX) family helicases are implicated in numerous human cancers. However, the role of DDX1 in HCC has not yet been fully elucidated. We downloaded gene expression data and clinical information data of HCC from The Cancer Genome Atlas and International Cancer Genome Consortium (ICGC) database and conducted subsequent analyses using the R package and online portal. The results revealed that HCC tissues had higher DDX1 expression compared with either paired or unpaired normal tissues. The increased DDX1 expression was closely related to the advanced pathological grade and histologic grade of HCC. Further analysis suggested that patients with high DDX1 expression contributed to poor prognosis The Cox regression analysis revealed that the expression level of DDX1 was an independent prognostic factor for HCC. In addition, an ICGC cohort was used for external validation. The cBio-Portal, MethSurv, and UALCAN database were used for evaluating the genomic mechanism. Moreover, the Tumor Immune Estimation Resource dataset and QUANTISEQ algorithm revealed that DDX1 expression positively correlates with immune infiltrating cells. We also identified the DDX1-related differentially expressed genes (DEGs) and explored their biological functions by GO, KEGG, and GSEA analyses, which indicated that DDX1 may regulate the progression of HCC. In general, increased DDX1 expression predicts a poor prognosis and drives the progression of HCC.