Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Frontiers in Immunology, Год журнала: 2023, Номер 14

Опубликована: Авг. 9, 2023

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed reveal the connection between stemness characteristics of HCC, established new gene signature related utilized it assess HCC prognosis immunotherapy response. The clinical information expression profiles (GEPs) 478 patients came from Gene Expression Omnibus (GEO) Cancer Genome Atlas (TCGA). one-class logistic regression (OCLR) algorithm was employed calculate messenger ribonucleic acid expression-based index (mRNAsi), quantifying features. Differentially expressed analyses were done high- low-mRNAsi groups 74 differentially metabolism-related genes (DEMRGs) identified with help sets Molecular Signatures Database (MSigDB). After integrated analysis, risk score model based on three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) UDP-glucuronosyltransferase 1A5 (UGT1A5) constructed divided into high-risk low-risk groups. Significant differences found pathway enrichment, immune cell infiltration patterns, alterations two High-risk group tended worse outcomes more likely respond immunotherapy. A stemness-metabolism-related composed gender, age, tumor-node-metastasis (TNM) staging generated showed great discrimination strong ability predicting

Язык: Английский

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Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Critical Reviews in Oncology/Hematology, Год журнала: 2024, Номер 196, С. 104293 - 104293

Опубликована: Фев. 10, 2024

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms cancer patients, we aimed review and critically appraise prognostic models DLBCL patients. A literature search PubMed/Embase identified 59 articles that proposed by combining parameters of (e.g., clinical, laboratory, immunohistochemical, genetic) between January 2000 2024. Of them, 40 studies different gene expression signatures incorporated them into nomogram-based models. Although most assessed discrimination calibration when developing the model, many lacked external validation. Current mainly developed from publicly available databases, lack validation, have no applicability clinical practice. However, they may be helpful individual patient counseling, although careful considerations should made regarding model development due possible limitations choosing prognostication.

Язык: Английский

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DLD is a potential therapeutic target for COVID-19 infection in diffuse large B-cell lymphoma patients

APOPTOSIS, Год журнала: 2024, Номер 29(9-10), С. 1696 - 1708

Опубликована: Апрель 6, 2024

Since the discovery of copper induces cell death(cuprotosis) in 2022, it has been one biggest research hotspots. cuprotosis related genes (CRGs) demonstrated to be a potential therapeutic target for cancer, however, molecular mechanism CRGs coronavirus disease 2019 (COVID-19) infected DLBCL patients not reported yet. Therefore, our objective is first elucidate and role COVID-19. Secondly, we conducted univariate multivariate analysis machine learning screen with common expression differences COVID-19 DLBCL. Finally, functional immune were confirmed through experiments analysis. The results show that play an important occurrence development Univariate confirm dihydrolipoamide dehydrogenase (DLD) key gene Inhibiting DLD can significantly inhibit cycle progression promote apoptosis cells positive regulation Lysine-specific demethylase 1 (LSD1, also known as KDM1A) proliferation apoptosis. high-expression may reduce T cell-mediated anti-tumor immunity by regulating infiltration CD8 + positively checkpoints LAG3 CD276. Reducing effectively enhance immunity, thereby clearing cancer preventing growth. In conclusion, infection patients. Our provides theoretical basis improving clinical treatment

Язык: Английский

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Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Aging, Год журнала: 2024, Номер 16(10), С. 8772 - 8809

Опубликована: Май 20, 2024

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and outcomes. However, comprehensively analyzing their relationship still lacking lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways TCGA-LUAD by ssGSEA. A matrix of pathway pairs was generated pathway-pair score (MPPS) model established univariable, LASSO, multivariable Cox regression analyses. The differences reprogramming, tumor microenvironment (TME), mutation burden drug sensitivity different MPPS groups were further explored. WGCNA 117 machine learning algorithms performed to identify MPPS-related genes. Single-cell RNA sequencing vitro experiments used explore the role C1QTNF6 on TME. results showed accurately predicted prognosis LUAD regardless platforms. High-MPPS group had worse prognosis, lower immune cells infiltration, immune-related genes expression cancer-immunity cycle scores than low-MPPS group. Seven identified, which mainly expressed fibroblasts. High fibroblasts more infiltration M2 macrophage, Treg less NK cells, memory CD8+ T cells. In validated silencing could inhibit macrophage polarization migration. study depicted landscape constructed predict efficacy. promising target regulate

Язык: Английский

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Artificial intelligence-based prognostic model accurately predicts the survival of patients with diffuse large B-cell lymphomas: analysis of a large cohort in China

BMC Cancer, Год журнала: 2024, Номер 24(1)

Опубликована: Май 22, 2024

Abstract Background Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include data. Therefore, we developed a widely applicable novel scoring system with screened by artificial intelligence (AI) that achieves accurate prognostic stratification promotes individualized treatments. Methods We retrospectively enrolled cohort of 401 patients DLBCL from our hospital, covering period January 2015 2019. included 22 variables in analysis assigned them weights using random survival forest method establish new predictive model combining bidirectional long-short term memory (Bi-LSTM) logistic hazard techniques. compared performance “molecular-contained model” (McPM) IPI. In addition, simplified version McPM (sMcPM) enhance its practical applicability settings. also demonstrated improved risk capabilities sMcPM. Results Our showed superior accuracy, as indicated C-index low integrated Brier score (IBS), for both overall (OS) progression-free (PFS). The was better than IPI based on receiver operating characteristic (ROC) curve fitting. selected five key indicators, including extranodal involvement sites, lactate dehydrogenase (LDH), MYC gene status, absolute monocyte count (AMC), platelet (PLT) sMcPM, which is more suitable applications. sMcPM similar OS results ( P < 0.0001 both) significantly PFS vs. = 0.44 IPI). Conclusions McPM, variables, IPI, rendering it era. Moreover, may become used effective tool guide individual precision treatments drive drug development.

Язык: Английский

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