International Cancer Genome Consortium Research Articles

PSMD9 promotes the malignant progression of hepatocellular carcinoma by interacting with c-Cbl to activate EGFR signaling and recycling

BackgroundMounting evidences shows that the ubiquitin‒proteasome pathway plays a pivotal role in tumor progression. The expression of 26S proteasome non-ATPase regulatory subunit 9 (PSMD9) is correlated with recurrence and radiotherapy resistance in several tumor types. However, the role and mechanism of PSMD9 in hepatocellular carcinoma (HCC) progression remain largely unclear.MethodsPSMD9 was identified as a prognosis-related biomarker for HCC based on analysis of clinical characteristics and RNA-seq data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and the JP Project of the International Cancer Genome Consortium (ICGC-LIRI-JP). PSMD9 expression was analyzed in cancer tissues and adjacent noncancerous tissues via immunohistochemistry and Western blotting. Multiple in vivo and in vitro experimental techniques (such as CCK-8, colony formation, EdU, and Transwell assays; flow cytometry; Western blotting; quantitative RT-PCR; Coimmunoprecipitation assay and immunofluorescence confocal imaging) were used to assess the functions of PSMD9 in the pathogenesis of HCC.ResultsWe found that the expression of PSMD9 was upregulated and associated with a poor prognosis in HCC patients. PSMD9 promoted HCC cell proliferation, migration, invasion and metastasis. Knockdown of PSMD9 significantly inhibited HCC cell proliferation by inducing G1/S cell cycle arrest and apoptosis. Mechanistically, we demonstrated that PSMD9 promoted HCC cell proliferation and metastasis via direct interaction with the E3 ubiquitin ligase c-Cbl, suppresses EGFR ubiquitination, influenced EGFR endosomal trafficking and degradation and subsequently activated ERK1/2 and Akt signaling. In addition, we showed that PSMD9 knockdown sensitized HCC cells to the tyrosine kinase inhibitor erlotinib in vitro and in vivo.ConclusionsCollectively, our results indicate that PSMD9 drives HCC progression and erlotinib resistance by suppressing c-Cbl mediated EGFR ubiquitination and therefore can be a potential therapeutic target for HCC.

CCL16 inhibits tumor proliferation and metastasis in HCC by impacting CK19 phenotype

Background and aimsCytokeratin 19–positive (CK19+) hepatocellular carcinoma (HCC) is an aggressive subtype with poor outcomes. The initiation and development of CK19+ HCC in the background of liver cirrhosis remains unclear. This study investigated the role of the cirrhosis-related gene CC motif chemokine ligand 16 (CCL16) in the development of CK19+ HCC. MethodsDatasets from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were analyzed to screen and validate the genes associated with CK19+ HCC. A total of 102 HCC patients were included for tissue microarray analysis. Gain-of-function experiments were conducted to investigate the biological functions of CCL16. CIBERSORT was used to investigate the correlation of CCL16 and immune infiltration. ResultsGEO dataset analysis showed that CK19+ HCC had lower expression of CCL16. In both TCGA dataset and our HCC cohort, CCL16 expression was negatively correlated with CK19 expression (P < 0.05) and its expression was higher in para-tumor than tumor tissues (P < 0.001). Moreover, low CCL16 expression was related to advanced stage and poor overall survival (P < 0.05). CCL16 overexpression downregulated CK19 expression and impacted the sphere formation ability of HCC cells. Overexpression of CCL16 inhibited the cell proliferation, migration, and invasion of HCC cell lines. Immune analysis showed HCC with high CCL16 expression had more infiltration of mast cells. HCC patients with both low CCL16 expression and low mast cells had the worst prognosis (P < 0.001). ConclusionOur data indicated that CCL16 downregulated the expression of CK19 and inhibited the malignant phenotype of HCC.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BackgroundHepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.MethodsThe GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.ResultsOur findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.ConclusionOur research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

An exosome mRNA-related gene risk model to evaluate the tumor microenvironment and predict prognosis in hepatocellular carcinoma

BackgroundThe interplay between exosomes and the tumor microenvironment (TME) remains unclear. We investigated the influence of exosomes on the TME in hepatocellular carcinoma (HCC), focusing on their mRNA expression profile.MethodsmRNA expression profiles of exosomes were obtained from exoRBase. RNA sequencing data from HCC patients’ tumors were acquired from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An exosome mRNA-related risk score model of prognostic value was established. The patients in the two databases were divided into high- and low-risk groups based on the median risk score value, and used to validate one another. Functional enrichment analysis was performed based on a differential gene prognosis model (DGPM). CIBERSORT was used to assess the abundance of immune cells in the TME. The correlation between the expression levels of immune checkpoint-related genes and DGPM was analyzed alongside the prediction value to drug sensitivity.ResultsA prognostic exosome mRNA-related 4-gene signature (DYNC1H1, PRKDC, CCDC88A, and ADAMTS5) was constructed and validated. A prognostic nomogram had prognostic ability for HCC. The genes for this model are involved in extracellular matrix, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Expression of genes here had a positive correlation with immune cell infiltration in the TME.ConclusionsOur study results demonstrate that an exosome mRNA-related risk model can be established in HCC, highlighting the functional significance of the molecules in prognosis and risk stratification.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Cellular senescence and metabolic reprogramming model based on bulk/single-cell RNA sequencing reveals PTGER4 as a therapeutic target for ccRCC

Clear cell renal cell carcinoma (ccRCC) is the prevailing histological subtype of renal cell carcinoma and has unique metabolic reprogramming during its occurrence and development. Cell senescence is one of the newly identified tumor characteristics. However, there is a dearth of methodical and all-encompassing investigations regarding the correlation between the broad-ranging alterations in metabolic processes associated with aging and ccRCC. We utilized a range of analytical methodologies, such as protein‒protein interaction network analysis and least absolute shrinkage and selection operator (LASSO) regression analysis, to form and validate a risk score model known as the senescence-metabolism-related risk model (SeMRM). Our study demonstrated that SeMRM could more precisely predict the OS of ccRCC patients than the clinical prognostic markers in use. By utilizing two distinct datasets of ccRCC, ICGC-KIRC (the International Cancer Genome Consortium) and GSE29609, as well as a single-cell dataset (GSE156632) and real patient clinical information, and further confirmed the relationship between the senescence-metabolism-related risk score (SeMRS) and ccRCC patient progression. It is worth noting that patients who were classified into different subgroups based on the SeMRS exhibited notable variations in metabolic activity, immune microenvironment, immune cell type transformation, mutant landscape, and drug responsiveness. We also demonstrated that PTGER4, a key gene in SeMRM, regulated ccRCC cell proliferation, lipid levels and the cell cycle in vivo and in vitro. Together, the utilization of SeMRM has the potential to function as a dependable clinical characteristic to increase the accuracy of prognostic assessment for patients diagnosed with ccRCC, thereby facilitating the selection of suitable treatment strategies.

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma.

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

Deciphering a Prognostic Signature Based on Soluble Mediators Defines the Immune Landscape and Predicts Prognosis in HNSCC.

The study on Head and Neck Squamous Cell Carcinoma (HNSCC), a prevalent and aggressive form of head and neck cancer, focuses on the often-overlooked role of soluble mediators. The objective is to leverage a transcriptome-based risk analysis utilizing soluble mediator-related genes (SMRGs) to provide novel insights into prognosis and immunotherapy efficacy in HNSCC patients. We analyzed the expression and prognostic significance of 10,859 SMRGs using 502 HNSCC and 44 normal samples from the TCGA-HNSC cohort in The Cancer Genome Atlas (TCGA). The samples were divided into training and test sets in a 7:3 ratio, with an additional external validation using 40 tumor samples from the International Cancer Genome Consortium (ICGC). Key differentially expressed genes (DEGs) with prognostic significance were identified through univariate and Lasso-Cox regression analyses. A prognostic model based on 20 SMRGs was developed using Lasso and multivariate Cox regression. We assessed the clinical outcomes and immune status in high-risk (HR) and low-risk (LR) HNSCC patients utilizing the BEST databases and single-sample Gene Set Enrichment Analysis (ssGSEA). The 20 SMRGs were crucial in predicting the prognosis of HNSCC, with the SMRG signature emerging as an independent prognostic indicator. Patients classified in the HR group exhibited poorer outcomes compared to those in the LR group. A nomogram, integrating clinical characteristics and risk scores, demonstrated substantial prognostic value. Immunotherapy appeared to be more effective in the LR group, possibly attributed to enhanced immune infiltration and expression of immune checkpoints. The model based on soluble mediator-associated genes offers a fresh perspective for assessing the pre-immune efficacy and showcases robust predictive capabilities. This innovative approach holds significant promise in advancing the field of precision immuno-oncology research, providing valuable insights for personalized treatment strategies in HNSCC.

Abstract 7035: Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics

Abstract As the fifth leading cause of cancer-related mortality, liver cancer imposes a global burden. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with a five-year overall survival rate of less than 20% for advanced-stage cases. Even the most advanced immunotherapy for HCC has disparate response rates in each patient with HCC, which has led to efforts to unravel the underlying factors in the progression of HCC using bioinformatic approaches. In recent decades, numerous studies have explored T-cell subtypes with tissue-specific residency, suggesting their early protective functions and therapeutic potential within tumor microenvironment (TME). This study aimed to understand inter-individual and intercellular TME through single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq) in five recurrent/non-recurrent HCC patients. We identified tumor-associated tissue-resident memory T (TA-TRM) cells and investigated the heterogeneous characteristics of two TRM clusters (CD69+ and CD103+). Whereas CD69+ TA-TRM showed high expression of genes associated with immunological activation, CD103+ TA-TRM exhibited upregulation of genes related to proliferation capacity. To validate the unique molecular patterns, we analyzed an independent HCC dataset (GSE140228) and observed high concordance of significant core marker genes for TA-TRM clusters between the two datasets. Moreover, we assessed the association of gene expression in TA-TRM cells with overall and recurrence-free survival by conducting survival analysis in two independent large-scale cohorts. The cross-validation approach was applied to estimate risk scores for patients with primary HCC tumors in The Cancer Genome Atlas (TCGA), and the trained model was evaluated using the International Cancer Genome Consortium (ICGC) data. The result of calculating the risk scores clearly indicates that TA-TRM cells are associated with survival in HCC. Therefore, our study characterized two distinct subtypes of TA-TRM cells in HCC, delineated their unique molecular signatures, and established their relevance to survival outcomes. The findings can potentially enhance the understanding of the roles of TA-TRM cells in the context of HCC. Citation Format: Mi-So Park, Hyunbin Cho, Hyeree Kim, Woong-Yang Park, Yong-Han Paik, Yeup Yoon, Wonseok Kang, Hong-Hee Won. Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7035.

Abstract 1743: Pan-cancer review of TP53 somatic alterations as a function of tumor type: Sometimes lost, sometimes gained, frequently altered

Abstract Background: TP53 is the gene most frequently disrupted by somatic alterations in cancer. Mutations include single nucleotide variants resulting in functional alterations, as well as premature stop codons, splice variants, and cryptic alterations. TP53 gene dosage is disrupted by heterozygous and homozygous deletions, copy-neutral loss of heterozygosity, and other structural changes such as intragenic deletions and fusions. TP53 is altered by epigenetic events and viral oncogenes, such as E6 expressed by the human papillomavirus. The readout of altered TP53 includes decreased gene expression of either variant or wild-type alleles. Epistatic alteration of TP53 pathway components have also been described, with the E3 ubiquitin ligase MDM2 being the canonical example. Depending on the type of TP53 alteration, there may be differential signaling consequences, ranging from loss of function and dominant negative to oncogenic signaling. Hypothesis: Patterns of TP53 mutations will vary based on tumor type. Differential alterations of TP53 will suggest differential pathway dependence and downstream pathophysiology that may be relevant to signaling and therapeutic potential. Methods: We analyzed extensive cancer genomics repositories, such as the Pan-Cancer Atlas (10,000+ tumors, &amp;gt;30 cancer types) from The Cancer Genome Atlas project and the International Cancer Genome Consortium. Assessing genes in the TP53 pathway, we examined somatic mutations, copy number alterations, structural changes, and gene expression. Epigenetic data was considered when available. Results: A significant pattern amongst sarcomas, prostate cancers, and urothelial carcinomas emerged, revealing that certain tumors exhibited a predominant classic tumor suppressor gene genotype. This genotype featured frequent homozygous deletions, inactivating mutations, and relatively low TP53 gene expression, often accompanied by epistatic MDM2 amplification. By contrast, other tumors demonstrate an atypical pattern in which TP53 mutations were rarely if ever associated with loss of TP53 gene expression with low levels of homozygous TP53 gene deletion. Likewise, atypical TP53 mutant tumor types rarely demonstrated MDM2 amplifications, and in opposition to typical TP53 cases, MDM2 amplification was positively associated with TP53 mutation rather than anti-correlated. Head and neck squamous cell carcinoma, uterine carcinoma, and pancreatic adenocarcinoma followed this pattern. Investigation within selected tumor types such as lung squamous cell carcinoma suggested that both typical and atypical TP53 mutation patterns could be observed. Conclusions: TP53 mutation patterns were strongly associated with tumor type and may suggest differential pathway activation according to the somatic alteration status. Citation Format: Ubaid A. Tanveer, David N. Hayes, Julie George, Jeremiah R. Holt, Martin Peifer, Roman Thomas. Pan-cancer review of TP53 somatic alterations as a function of tumor type: Sometimes lost, sometimes gained, frequently altered [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1743.

Combining a glycolysis‑related prognostic model based on scRNA‑Seq with experimental verification identifies ZFP41 as a potential prognostic biomarker for HCC.

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis, and its heterogeneity affects the response to clinical treatments. Glycolysis is highly associated with HCC therapy and prognosis. The present study aimed to identify a novel biomarker for HCC by exploring the heterogeneity of glycolysis in HCC. The intersection of both marker genes of glycolysis‑related cell clusters from single‑cell RNA sequencing analysis and mRNA data of liver HCC from The Cancer Genome Atlas were used to construct a prognostic model through Cox proportional hazard regression and the least absolute shrinkage and selection operator Cox regression. Data from the International Cancer Genome Consortium were used to validate the results of the analysis. Immune status analysis was then conducted. A significant gene in the prognostic model was identified as a potential biomarker and was verified through in vitro experiments. The results revealed that the glycolysis‑related prognostic model divided patients with HCC into high‑ and low‑risk groups. A nomogram combining the model and clinical features exhibited accurate predictive ability, with an area under the curve of 0.763 at 3 years. The high‑risk group exhibited a higher expression of checkpoint genes and lower tumor immune dysfunction and exclusion scores, suggesting that this group may be more likely to benefit from immunotherapy. The tumor tissues had a higher zinc finger protein (ZFP)41 mRNA and protein expression compared with the adjacent tissues. In vitro analyses revealed that ZFP41 played a crucial role in cell viability, proliferation, migration, invasion and glycolysis. On the whole, the present study demonstrates that the glycolysis‑related prognostic gene, ZFP41, is a potential prognostic biomarker and therapeutic target, and may play a crucial role in glycolysis and malignancy in HCC.

A novel CD8+ T cell-related gene signature as a prognostic biomarker in hepatocellular carcinoma.

CD8+ T cells have great roles in tumor suppression and elimination of various tumors including hepatocellular carcinoma (HCC). Nonetheless, potential prognostic roles of CD8+ T cell-related genes (CD8Gs) in HCC remains unknown. In our study, 416 CD8Gs were identified in HCC, which were enriched in inflammatory and immune signaling pathways. Using The Cancer Genome Atlas dataset, a 5-CD8Gs risk model (KLRB1, FYN, IL2RG, FCER1G, and DGKZ) was constructed, which was verified in International Cancer Genome Consortium and gene expression omnibus datasets. Furthermore, we found that overall survival was independently correlated with the CD8Gs signature, and it was associated with immune- and cancer-related signaling pathways and immune cells infiltration. Finally, drug sensitivity data indicated that 10 chemotherapeutic drugs held promise as therapeutics for HCC patients with high-risk. In conclusion, multi-databases analysis showed that 5-CD8Gs and their signature could be an indicator to predict candidate drugs for HCC therapy.

A 15-Inflammation-Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma

Chronic inflammation promotes the development of pancreatic ductal adenocarcinoma (PDAC) and PDAC-related inflammatory tumor microenvironment facilitates tumor growth and metastasis. Thus, we aimed to study the association between inflammatory response and prognosis in patients with PDAC. We conducted the whole transcriptomic sequencing using tissue samples collected from patients diagnosed with PDAC (n = 106) recruited from Shandong Cancer Hospital. We first constructed a prognostic signature using 15 inflammation-related genes in The Cancer Genome Atlas (TCGA) cohort (n = 177) and further validated it in an independent International Cancer Genome Consortium (ICGC) cohort (n = 90) and our in-house cohort. PDAC patients with a higher risk score had poorer overall survival (OS) (P < 0.001; HR, 3.02; 95% CI, 1.94-4.70). The association between the prognostic signature and OS remained significant in the multivariable Cox regression adjusting for age, sex, alcohol exposure, diabetes, and stage (P < 0.001; HR, 2.91; 95% CI, 1.73-4.89). This gene signature also robustly predicted prognosis in the ICGC cohort (P = 0.01; HR, 1.94; 95% CI, 1.14-3.30) and our cohort (P < 0.001; HR, 2.40; 95% CI, 1.45-3.97). Immune subtype C3 (inflammatory) was enriched and CD8+ T cells were higher in patients with a lower risk score (P < 0.05). Furthermore, PDAC patients with higher risk scores were more sensitive to chemotherapy, immunotherapy, and PARP inhibitors (P < 0.05). In sum, we identified a novel gene signature that was associated with inflammatory response for risk stratification, prognosis prediction, and therapy guidance in PDAC patients. Future studies are warranted to validate the clinical utility of the signature.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer.

Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes. The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database. In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration. The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

Exploring the Interplay of m6A Regulators in Renal Cell Carcinoma: Molecular Insights and clinical implications

BackgroundThe m6A modification of N6-methyladenosine (m6A) modification regulates gene expression and functioning by affecting RNA splicing, stability, translocation, and translation. ObjectiveThis study used comprehensive bioinformatics analysis to elucidate the mechanisms by which m6A regulators function in kidney renal clear cell carcinoma (KIRC) and their interrelationships. MethodsThe KIRC data extracted from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases was used in this study. LASSO Cox regression, protein–protein interaction analysis, univariate Cox regression, and a predictive risk model. Cox regression, Kaplan–Meier analysis, consensus clustering, CIBERSORT, gene set enrichment analysis (GSEA), ESTIMATE, immune cell infiltration, and prognostic factors were investigated in KIRC utilizing computational methods. ResultsThe results of integrated analysis showed that KIRC has unique and complex gene patterns associated with m6A and is linked to different gene patterns. These patterns included an 11.68-fold change in insulin-like growth factor-2 mRNA-binding protein 3 (IGF2BP3), a 3.35-fold change in ELAV-like RNA binding protein 2 (ELAVL2), a 2.34-fold change in fat mass- and obesity-associated protein (FTO), a 2.52-fold change in ELAVL4, and a 2.15-fold change in ELAVL3. By contrast, there was downregulation of IGF2BP2, zinc finger CCCH-type containing 14 (ZC3H14), and alpha-ketoglutarate-dependent dioxygenase (ALKBH6), with fold changes of 0.44, 0.66, and 0.68, respectively. Minor modifications ranging from 1 to 1.28 were seen in other m6A regulators. ConclusionThis study showed that m6A alteration affects KIRC tumor immunity. Moreover, m6A regulators such as IGF2BP3 have therapeutic and diagnostic potential, which could improve patient outcomes by personalized immunotherapy.

PANoptosis-related molecule CASP2 affects the immune microenvironment and immunotherapy response of hepatocellular carcinoma

BackgroundThe involvement of molecules associated with PANoptosis in hepatocellular carcinoma (HCC) is still not well understood. MethodsVarious R packages were utilized to analyze within the R software. Data that was freely accessible was obtained from the databases of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). ResultsHere, we comprehensively explored the role of PANoptosis-related genes in HCC. The caspase 2 (CASP2) was identified as the interest gene for further analysis. We found that CASP2 is related to the poor prognosis and worse clinical features of HCC patients. Moreover, we explored the biological pathway CASP2 is involved in and found that CASP2 is associated with multiple carcinogenic pathways. Also, we noticed that CASP2 can significantly reshape the HCC immune microenvironment and affect the response rate of immunotherapy. Analysis of drug sensitivity suggested that individuals exhibiting elevated CASP2 levels may display increased susceptibility to doxorubicin and vorinostat while demonstrating resistance towards erlotinib, lapatinib, sunitinib, and temsirolimus. Meanwhile, we explored the single-cell distribution of CASP2 in the HCC microenvironment. To enhance the clinical application of CASP2 in HCC, we constructed a prognosis model using the molecules derived from CASP2, which demonstrated good efficiency in predicting patients prognosis. Moreover, in vitro experiments indicated that CASP2 can significantly inhibits cell proliferation, invasion and migration ability of HCC cells. ConclusionsOur study comprehensively explored the role of PANoptosis-related molecule CASP2 in HCC, which can provide directions for future studies.

Cuproptosis-related molecular classification and gene signature of hepatocellular carcinoma and experimental verification.

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor overall prognosis. Cuproptosis, a recently proposed mode of copper-dependent cell death, plays a critical role in the malignant progression of various tumors; however, the expression and prognostic value of cuproptosis-related regulatory genes in HCC remain unclear. Genomic, genetic, and expression profiles of ten key cuproptosis-related regulatory genes were analyzed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset and protein expression data from the Human Protein Atlas (HPA) database. Unsupervised clustering of HCC patients based on these ten key cuproptosis-related regulatory genes was used to identify different HCC subtypes and analyze the differences in clinical and immune characteristics among subtypes. Subsequently, univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox analyses were used to establish a cuproptosis-related prognostic signature, and the accuracy of prognostic signature prediction was internally validated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curve in TCGA training and testing cohorts. The prognostic signature was externally validated using TCGA-LIHC entire cohort and International Cancer Genome Consortium Liver Cancer (ICGC-LIRI) cohorts. Finally, the expression landscape of cuproptosis-related regulatory genes in prognostic signature was explored by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting and immunohistochemistry (IHC) experiments. Ten cuproptosis-related genes were differentially expressed in normal and HCC tissues. Unsupervised clustering identified two subtypes and HCC patients with these two subtypes had different clinical prognoses and immune characteristics, as well as different degrees of response to immunotherapy. Lipoyltransferase 1 (LIPT1), dihydrolipoamide s-acetyltransferase (DLAT), and cyclin dependent kinase inhibitor 2A (CDKN2A) were selected to construct a prognostic signature, which significantly distinguished HCC patients with different survival periods in the TCGA training and testing cohorts and was well validated in both the TCGA-LIHC entire cohort and ICGC-LIRI cohort. The risk score of the prognostic signature was confirmed to be an independent prognostic factor, and nomograms were generated to effectively predict the probability of HCC patient survival. The qRT-PCR, western blotting and IHC results also revealed a significant imbalance in the expression of these cuproptosis-related genes in HCC. The classification and prognostic signature based on cuproptosis-related regulatory genes helps to explain the heterogeneity of HCC, which may contribute to the individualized treatment of patients with the disease.

Discovering ferroptosis-associated tumor antigens and ferroptosis subtypes in pancreatic adenocarcinoma to facilitate mRNA vaccine development

Pancreatic adenocarcinoma (PAAD) is an aggressive, heterogeneous malignancy. We studied the potential of ferroptosis-related tumor vaccines for PAAD treatment. Ferroptosis-related genes, gene expression profiles, and clinical information were extracted from the FerrDB, UCSC Xena, and International Cancer Genome Consortium databases. Differential expression levels and prognostic indices were calculated, genetic alterations and correlations with immune-infiltrating cells were explored, and consensus clustering analysis was performed to identify ferroptosis subtypes and gene modules. Immune enrichment scores were calculated using gene set enrichment analysis, and gene modules were screened using weighted gene co-expression network analysis. The ferroptosis subtype distribution was visualized using graph learning-based dimensionality reduction analysis of the Monocle package with a Gaussian distribution. We identified four ferroptosis-related tumor antigens, AGPS, KDM5A, NRAS, and OSBPL9, which were associated with pancreatic cancer prognosis and antigen-presenting cell infiltration. We determined three minor ferroptosis subtypes, with different clinical prognosis and tumor immune status. Of the subtypes, FS3 may be more suitable for mRNA therapy. We constructed a PAAD ferroptosis landscape to identify the ferroptosis status of patients and predict their prognosis. Finally, we found that the eigengene of the green module was an independent prognostic factor, with a significantly better prognosis in the high-score group than in the low-score group. In conclusion, we identified four ferroptosis-related genes as targets for mRNA vaccines and three ferroptosis subtypes, providing a theoretical basis for the anti-PAAD mRNA vaccine and defining suitable patients for vaccination.

Multilayered insights: a machine learning approach for personalized prognostic assessment in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a complex malignancy, and precise prognosis assessment is vital for personalized treatment decisions. This study aimed to develop a multi-level prognostic risk model for HCC, offering individualized prognosis assessment and treatment guidance. By utilizing data from The Cancer Genome Atlas (TCGA) and the Surveillance, Epidemiology, and End Results (SEER) database, we performed differential gene expression analysis to identify genes associated with survival in HCC patients. The HCC Differential Gene Prognostic Model (HCC-DGPM) was developed through multivariate Cox regression. Clinical indicators were incorporated into the HCC-DGPM using Cox regression, leading to the creation of the HCC Multilevel Prognostic Model (HCC-MLPM). Immune function was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and immune cell infiltration was assessed. Patient responsiveness to immunotherapy was evaluated using the Immunophenoscore (IPS). Clinical drug responsiveness was investigated using drug-related information from the TCGA database. Cox regression, Kaplan-Meier analysis, and trend association tests were conducted. Seven differentially expressed genes from the TCGA database were used to construct the HCC-DGPM. Additionally, four clinical indicators associated with survival were identified from the SEER database for model adjustment. The adjusted HCC-MLPM showed significantly improved discriminative capacity (AUC=0.819 vs. 0.724). External validation involving 153 HCC patients from the International Cancer Genome Consortium (ICGC) database verified the performance of the HCC-MLPM (AUC=0.776). Significantly, the HCC-MLPM exhibited predictive capacity for patient response to immunotherapy and clinical drug efficacy (P < 0.05). This study offers comprehensive insights into HCC prognosis and develops predictive models to enhance patient outcomes. The evaluation of immune function, immune cell infiltration, and clinical drug responsiveness enhances our comprehension and management of HCC.

Identification of immune cell-related prognostic genes characterized by a distinct microenvironment in hepatocellular carcinoma.

The development and progression of hepatocellular carcinoma (HCC) have been reported to be associated with immune-related genes and the tumor microenvironment. Nevertheless, there are not enough prognostic biomarkers and models available for clinical use. Based on seven prognostic genes, this study calculated overall survival in patients with HCC using a prognostic survival model and revealed the immune status of the tumor microenvironment (TME). To develop a novel immune cell-related prognostic model of HCC and depict the basic profile of the immune response in HCC. We obtained clinical information and gene expression data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. TCGA and ICGC datasets were used for screening prognostic genes along with developing and validating a seven-gene prognostic survival model by weighted gene coexpression network analysis and least absolute shrinkage and selection operator regression with Cox regression. The relative analysis of tumor mutation burden (TMB), TME cell infiltration, immune checkpoints, immune therapy, and functional pathways was also performed based on prognostic genes. Seven prognostic genes were identified for signature construction. Survival receiver operating characteristic curve analysis showed the good performance of survival prediction. TMB could be regarded as an independent factor in HCC survival prediction. There was a significant difference in stromal score, immune score, and estimate score between the high-risk and low-risk groups stratified based on the risk score derived from the seven-gene prognostic model. Several immune checkpoints, including VTCN1 and TNFSF9, were found to be associated with the seven prognostic genes and risk score. Different combinations of checkpoint blockade targeting inhibitory CTLA4 and PD1 receptors and potential chemotherapy drugs hold great promise for specific HCC therapies. Potential pathways, such as cell cycle regulation and metabolism of some amino acids, were also identified and analyzed. The novel seven-gene (CYTH3, ENG, HTRA3, PDZD4, SAMD14, PGF, and PLN) prognostic model showed high predictive efficiency. The TMB analysis based on the seven genes could depict the basic profile of the immune response in HCC, which might be worthy of clinical application.