Survival Of HCC Patients Research Articles

Efficacy and safety of donafenib combined with interventional therapy and with/without immunotherapy in hepatocellular carcinoma: A retrospective study.

e16177 Background: The Phase III clinical trial ZGDH 3 and several real-world studies have demonstrated the potential of Donafenil to improve overall survival (OS) in HCC patients. This study evaluated the efficacy and safety of donafenil combined with intervention ±PD-1 in the treatment of HCC by retrospective analysis of previous patient information. Methods: We retrospectively analyzed patients with hepatocellular carcinoma who received Donafinil combination therapy at Peking University Cancer Hospital between April 2022 and December 2024, all of whom received at least 1 cycle of therapy. The primary endpoint was objective response rate (ORR), and secondary endpoints included progression-free survival (PFS), overall survival (OS), and safety. Results: 32 patients were included, all of whom received donafenil combined with intervention ±PD-1.First-line treatment accounted for 78.1%, and post-line treatment accounted for 21.9%. In the past, 13 patients (40.6%) had undergone interventional therapy, 5 patients (15.6%) had undergone surgery, and 6 patients (18.8%) had undergone radiofrequency ablation. Patients were classified as BCLC stage 0 (3.1%), A (21.9%), B(37.5%) and C(37.5%). The median tumor size was 5.3 cm (range 1.0, 16.7), 23 patients (71.9%) had ≥2 tumors, and 17 patients (53.1%) had ≥5 cm tumors. The median follow-up was 7.6 months, and 9 patients died. The ORR was 56.3% and DCR was 81.3%( CR 9 [28.1%], PR 9 [28.1%]) .The mRFS was 16.1 months, mOS was 23.3 months, 12-month rate was 75.1%, 12-month OS rate 85%. Most of all treated patients had at least one TEAEs (90.6%). 10 patients (31.2%) had grade 3 and above TEAEs, and no grade 4 or 5 TEAE or treatment-related deaths were reported. TEAEs with an incidence of > 20% included elevated total bilirubin, elevated ALT, elevated AST, and decreased platelet count. Conclusions: Donafenib combined interventional therapy with ±PD-1 has shown good efficacy and safety in HCC patients.

WED-100 Prognostic value of a composite biomarker score for predicting overall survival in HCC patients

WED-100 Prognostic value of a composite biomarker score for predicting overall survival in HCC patients

Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1

BackgroundDisulfidptosis is a newly discovered programmed cell death pathway that may be connected to tumorigenesis and development, showing promise as a novel treatment strategy for cancer. This study aims to construct a prognostic model of disulfidptosis-related Long non-coding RNAs (DRLRs) within the Asian HCC population and to investigate the impact of DRLRs on HCC.MethodsUtilising a combination of univariate Cox, Lasso-Cox, and multivariate Cox analyses, five pivotal DRLRs (AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1) were identified, forming a robust prognostic signature. Subsequent validations included Receiver Operating Characteristic (ROC) and Concordance Index analyses, alongside Principal Component Analysis. Comprehensive bioinformatics analysis was performed on the hub DRLRs, followed by experimental validation using quantitative real-time polymerase chain reaction and cellular functional assays.ResultsThe risk score independently predicted prognosis, outperforming traditional clinical-pathological factors across varying ages, tumour stages, and pathological classifications in the cohort. A nomogram integrating these variables demonstrated capability in forecasting survival. Multivariate analysis confirmed that the risk score and AJCC TNM staging are independent prognostic factors for predicting overall survival (OS) in Asian HCC patients (both P < 0.001). The prognostic model’s ROC area under the ROC values for 1-, 3-, and 5-year predictions were 0.837, 0.794, and 0.783, respectively, indicating its strong diagnostic and prognostic value. Pathway and immune landscape analyses elucidated the biological underpinnings and immune modulations associated with the high-risk group. Immune landscape analysis indicated that both immunescore (P < 0.001) and estimatescore (P < 0.05) were significantly decreased in the high-risk group, with both specific and non-specific immune responses being significantly suppressed, while the tumour immune dysfunction and exclusion score was notably increased (P < 0.001). Tumour mutational burden (TMB) analysis revealed a significantly higher TMB in the high-risk group (P = 0.033) and shorter OS for HCC patients in the high TMB subgroup (P = 0.002). Notably, Potential chemotherapeutic agents (PFI3, 5-Fluorouracil, BPD-00008900, GDC0810, and AZ6102) were identified for high-risk group. Experimental validations through quantitative PCR and in vitro assays confirmed the deregulation of these DRLRs in HCC, with functional studies highlighting the potential of ZNF337-AS1 silencing in curtailing tumour invasiveness.ConclusionOur investigations validate a DRLR-based risk scoring model as an effective prognostic tool for Asian HCC. This model not only enhances understanding of disulfidptosis’s role in HCC but also facilitates personalised treatment strategies, potentially improving patient outcomes.

Adjuvant PD-1 inhibitors improve recurrence and survival outcomes in high-risk hepatocellular carcinoma patients after curative hepatectomy

BackgroundHepatocellular carcinoma (HCC) is the most prevalent malignancy in China, with liver resection recognized as the primary curative intervention. However, HCC patients face an elevated risk of recurrence, thereby significantly impacting prognosis.PurposeThis study aimed to assess the impact of adjuvant programmed cell death protein-1 (PD-1) inhibitors on survival outcomes in patients with HCC who are at high risk for postoperative recurrence following curative hepatectomy.Materials and methodsAmong the 199 study participants, 77 received adjuvant PD-1 inhibitors. Propensity score matching (PSM) was used to balance baseline differences between patients who received adjuvant PD-1 inhibitors and those who did not. Assessment of overall survival (OS) and recurrence-free survival (RFS) was conducted using Kaplan–Meier curves, while Cox regression analysis was employed to identify prognostic factors influencing survival.ResultsAfter PSM, the 1-year and 2-year RFS were 87.1% and 74.2% in the PD-1 inhibitors group and 44.6% and 37.8% in non-PD-1 inhibitors group (p < 0.001). The 1-year and 2-year OS were 98.5% and 95.7% in the PD-1 inhibitors group compared with 90.7% and 77.0% in non-PD-1 inhibitors group (p = 0.004). Multivariable analyses demonstrated that the use of adjuvant PD-1 inhibitors was significantly associated with improved RFS and OS. Subgroup analysis indicated that adjuvant PD-1 inhibitors group achieved longer RFS than the non-PD-1 inhibitors group in patients without adjuvant transarterial chemoembolization (TACE).ConclusionThe administration of adjuvant PD-1 inhibitors may effectively reduce the risk of tumor recurrence and improve survival in HCC patients with high risk of recurrence after curative hepatectomy.

Upregulation of miR-3130-5p Enhances Hepatocellular Carcinoma Growth by Suppressing Ferredoxin 1 : miR-3130-5p Enhances HCC Growth via Inhibiting FDX1.

Hepatocellular carcinoma [HCC] is a leading cause of cancer-related mortality worldwide, necessitating the exploration of novel therapeutic targets. Although accumulating studies have identified Ferredoxin 1 [FDX1], a key regulator of cuproptosis, as a candidate tumor suppressor and potential therapeutic target, its role and mechanism remain elusive in HCC. The FDX1 expression was investigated in human HCC tissues and cell lines. Potential microRNAs targeting FDX1 were predicted by bioinformatic analysis and validated using qPCR screening, a dual luciferase reporter assay, MiR-3130-5p and miR-1910-3p mimics and inhibitors, overexpression plasmids, and xenograft nude mouse model. The correlation between miR-3130-5p/FDX1 axis and HCC patient prognosis was analyzed by using Kaplan-Meier survival analysis. We demonstrated that the expression of FDX1 was downregulated in human HCC tissues and cell lines compared to non-cancerous counterparts, and the downregulation of FDX1 was associated with poor overall survival in HCC patients. Subsequent bioinformatic analysis and experimental validations showed that FDX1 expression was reduced by microRNA [miR]-3130-5p mimic while induced by miR-3130-5p inhibitor. Further, miR-3130-5p was upregulated in HCC tissues and cells, correlating with a poor prognosis of HCC patients. Besides, lentivirus-mediated overexpression of miR-3130-5p significantly enhanced HCC growth in xenograft nude mouse models. Mechanistically, it was demonstrated that miR-3130-5p inhibited FDX1 expression via binding to its 3' untranslated region [3' UTR], while overexpression of FDX1 counteracted the promoting effect of miR-3130-5p on HCC cell proliferation. These findings suggest the miR-3130-5p/FDX1 axis as a prognostic biomarker as well as a potential therapeutic target in HCC.

MAZ-mediated N6-methyladenosine modification of ZEB1 promotes hepatocellular carcinoma progression by regulating METTL3

BackgroundHepatocellular carcinoma (HCC) has a hidden onset and high malignancy. Its high metastasis, high recurrence, and short survival time have always been a difficult and hot spot in clinical practice. Our previous study revealed that myc-associated zinc finger protein (MAZ) is highly upregulated in HCC tissues and may promote the proliferation and metastasis of HCC cells by inducing the epithelial-mesenchymal transformation (EMT) process. However, the specific regulatory mechanism by which MAZ functions as an oncogene in HCC has still not been fully elucidated.MethodsImmunohistochemical staining and bioinformatics analyses were conducted to measure the expression of MAZ, key m6A enzymes, and ZEB1 in HCC tissues. RNA sequencing (RNA-seq) of MAZ knockdown HCC cells and human mRNA m6A sequencing (m6A-seq) of HCC tissues were intersected to screen the downstream targets for both MAZ and m6A methylation. The correlations between MAZ and its targets were analyzed by dual-luciferase assays and cell rescue experiments.ResultsHere, we report for the first time that MAZ is involved in m6A methylation of HCC by targeting the transcriptional regulation of key m6A enzymes. MAZ expression was significantly correlated with the expression of key m6A enzymes in HCC tissues and cell lines. Moreover, MAZ could bind to the promoters of key m6A enzymes, and multivariate Cox regression analysis suggested that MAZ and METTL3 expression were independent risk factors for the survival of HCC patients. Through RNA-seq and m6A-seq, we screened out EMT regulators ZEB1 and TRIM50 as the downstream targets for both MAZ and m6A methylation. Mechanistically, m6A sites with high confidence in ZEB1 and TRIM50 mRNA were identified by SRAMP, and there were significant relationships between ZEB1 and METTL3 in HCC tissues and cells. A nomogram model was established to better display the combined effect of MAZ, METTL3, and ZEB1 on HCC prognosis.ConclusionsOur study revealed a promising clinical application of MAZ, METTL3, and ZEB1 in HCC prognosis, further suggesting that MAZ can be used as a potential molecular biomarker for HCC diagnosis and prognosis.

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy

PurposeHepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical data to predict 2-year survival in HCC patients treated with stereotactic body radiation therapy (SBRT).MethodsThis study analyzed a cohort of 186 HCC patients who underwent SBRT. Radiomics features were extracted from CT scans, complemented by collection of clinical data. Training and validation of machine learning models were conducted using nested cross-validation techniques. Deep learning models, leveraging various convolutional neural networks (CNNs), were employed to effectively integrate both image and clinical data. Post-hoc explainability techniques were applied to elucidate the contribution of imaging data to predictive outcomes.ResultsHandcrafted radiomics features demonstrated moderate predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.59 to 0.72. Deep learning models, harnessing the fusion of image and clinical data, exhibited improved predictive accuracy, with AUC values ranging from 0.71 to 0.81. Notably, the ensemble model, amalgamating handcrafted radiomics and deep learning features with clinical data, demonstrated the most robust predictive capability, achieving an AUC of 0.86 (95% CI: 0.80–0.93).ConclusionThe ensemble model represents a significant advancement, providing a comprehensive tool for predicting survival outcomes in HCC patients undergoing SBRT. The inclusion of interpretability methods such as Grad-CAM enhances transparency and understanding of these complex predictive models.

Prognostic value of a composite biomarker score for predicting overall survival in HCC patients

Prognostic value of a composite biomarker score for predicting overall survival in HCC patients

CK19 protein expression: the best cutoff value on the prognosis and the prognosis model of hepatocellular carcinoma

Background and objectiveIn clinical practice, CK19 can be an important predictor for the prognosis of HCC. Due to the high incidence and mortality rates of HCC, more effective and practical prognostic prediction models need to be developed urgently.MethodsA total of 1,168 HCC patients, who underwent radical surgery at the Guangxi Medical University Cancer Hospital, between January 2014 and July 2019, were recruited, and their clinicopathological data were collected. Among the clinicopathological data, the optimal cutoff value of CK19-positive HCC was determined by calculating the area under the curve (AUC) using survival analysis and time-dependent receiver operating characteristic (timeROC) curve analysis. The predictors were screened using univariate and multivariate COX regression and least absolute shrinkage and selection operator (LASSO) regression to construct nomogram prediction models, and their predictive potentials were assessed using calibration curves and AUC values.ResultsThe 0% positive rate of CK19 was considered the optimal cutoff value to predict the poor prognosis of CK19-positive HCC. The survival analysis of 335 CK19-positive HCC showed no significant statistical differences in the overall survival (OS) and disease-free survival (DFS) of CK19-positive HCC patients. A five-factor risk (CK19, CA125, Edmondson, BMI, and tumor number) scoring model and an OS nomograph model were constructed and established, and the OS nomograph model showed a good predictive performance and was subsequently verified.ConclusionA 0% expression level of CK19 protein may be an optimal threshold for predicting the prognosis of CK19-positive HCC. Based on this, CK19 marker a good nomogram model was constructed to predict HCC prognosis.

Identification of pyroptosis-associated miRNAs in the immunoscape and prognosis of hepatocellular carcinoma

BackgroundHepatocellular carcinoma is one of the most prevalent types of liver malignancy and poses a severe threat to global health. Despite recent improvements in therapeutic approaches, treatment options for patients with advanced or recurrent HCC are still limited.Materials and methodsOur study analyzed miRNA differential expression using data from hepatocellular carcinoma patients in the Cancer Genome Atlas. Pyroptosis-related genes were identified from gene cards. Differential expression of miRNAs was analyzed using DESeq2 and visualized using ggplot2 and pheatmap. A prognostic risk model for pyroptosis-associated miRNAs was constructed using LASSO regression and validated by principal component analysis, Kaplan-Meier survival and ROC curve analysis. We also performed gene and pathway enrichment analysis. Immune cell infiltration and function in HCC were assessed using single-sample genomic enrichment analysis, and correlations with immune cells and function were explored. Also, CCK-8 assay as well as migration and invasion assays were performed after knockdown of miR-6844.ResultsWe have established and validated a prognostic risk model based on ten DEmiRNAs, which is important for the survival of HCC patients. Significant changes in immune cell infiltration and immune function were also found in high-risk patients. It also demonstrated that knockdown of miR-6844 inhibited HCC cell proliferation, migration and invasion, highlighting its role in HCC progression.ConclusionOur study reveals the implications of pyroptosis-associated differential miRNAs on the prognosis of patients with hepatocellular carcinoma and provides a foundation for novel HCC therapies, especially immunotherapy.

NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

Comprehensive analysis of POLH-AS1 as a prognostic biomarker in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC), a prevalent primary malignant tumor, is notorious for its high mortality rate. Despite advancements in HCC treatment, patient outcomes remain suboptimal. This study endeavors to assess the potential prognostic significance of POLH-AS1 in HCC.MethodsIn this research, we gathered RNA-Seq information from individuals with HCC in The Cancer Genome Atlas (TCGA). We analyzed the levels of POLH-AS1 expression in both HCC cells and tissues using statistical tests. Additionally, we examined various prognostic factors in HCC using advanced methodologies. Furthermore, we employed Spearman’s rank correlation analysis to examine the association between POLH-AS1 expression and the tumor’s immune microenvironment. Finally, the functional roles of POLH-AS1 in HCC were validated in two HCC cell lines (HEP3B and HEPG2).ResultsOur analysis revealed elevated POLH-AS1 expression across various cancers, including HCC, with heightened expression correlating with HCC progression. Notably, POLH-AS1 expression emerged as a potential biomarker for HCC patient survival and prognosis. Mechanistically, we identified the involvement of POLH-AS1 in tumorigenesis pathways such as herpes simplex virus 1 infection, interactions with neuroactive receptors, and the cAMP signaling pathway. Lastly, inhibition of POLH-AS1 was discovered to hinder the proliferation, invasion and migration of HEP3B and HEPG2 HCC cells.ConclusionsPOLH-AS1 emerges as a promising prognostic biomarker and therapeutic target for HCC, offering potential avenues for enhanced patient management and treatment strategies.

CD69+ Vδ1γδ T cells are anti-tumor subpopulations in hepatocellular carcinoma

CD69+ Vδ1γδ T cells are anti-tumor subpopulations in hepatocellular carcinoma

Sphingosine kinase 2 and p62 regulation are determinants of sexual dimorphism in hepatocellular carcinoma

Sphingosine kinase 2 and p62 regulation are determinants of sexual dimorphism in hepatocellular carcinoma

Long non-coding RNAs in ferroptosis and cuproptosis impact on prognosis and treatment in hepatocellular carcinoma

Ferroptosis and cuproptosis are recently discovered forms of cell death that have gained interest as potential cancer treatments, particularly for hepatocellular carcinoma. Long non-coding RNAs (lncRNAs) influence cancer cell activity by interacting with various nucleic acids and proteins. However, the role of ferroptosis and cuproptosis-related lncRNAs (FCRLs) in cancer remains underexplored. Ferroptosis and cuproptosis scores for each sample were assessed using Gene Set Variation Analysis (GSVA). Weighted correlation network analysis identified the FCRLs most relevant to our study. A risk model based on FCRLs was developed to categorize patients into high-risk and low-risk groups. We then compared overall survival (OS), tumor immune microenvironment, and clinical characteristics between these groups. The IPS score and ImmuCellAI webpage were used to predict the association between FCRL-related signatures and immunotherapy response. Finally, we validated the accuracy of FCRLs in hepatocellular carcinoma cell lines using induction agents (elesclomol and erastin). Patients in different risk subgroups showed significant differences in OS, immune cell infiltration, pathway activity, and clinical characteristics. Cellular assays revealed significant changes in the expression of AC019080.5, AC145207.5, MIR210HG, and LINC01063 in HCC cell lines following the addition of ferroptosis and cuproptosis inducers. We created a signature of four FCRLs that accurately predicted survival in HCC patients, laid the foundation for basic research related to ferroptosis and cuproptosis in hepatocellular carcinoma, and provided therapeutic recommendations for HCC patients.

Repeated Previous Transarterial Treatments Negatively Affect Survival in Patients with Hepatocellular Carcinoma Receiving Sorafenib

Background: Transarterial chemoembolisation (TACE) and radioembolisation (TARE) can lead to the deterioration of liver function, especially in cases of a high tumour burden, potentially lessening the benefits of subsequent systemic treatments. We aimed to verify whether a high number of previous transarterial treatments modified the outcomes of patients who received sorafenib as a frontline systemic treatment. Methods: A retrospective analysis of a large multicenter dataset containing prospectively collected data of sorafenib-treated patients was conducted. Results: Data from 696 patients were analysed, with 139 patients having received &gt;two transarterial procedures before starting sorafenib. A propensity score matched 139 identified pairs of patients. Having received &gt;two locoregional treatments was independently associated with a shorter survival (hazard ratio 1.325, 95% confidence interval 1.018–1.725, p = 0.039). This pattern was confirmed amongst responders to sorafenib, but not in progressors. A trend toward a higher rate of the permanent discontinuation of sorafenib due to liver failure (18.7 vs. 10.8%, p = 0.089) and a lower rate of eligibility for second-line treatments (24.5 vs. 17.3%, p = 0.184) was observed in patients who had received &gt;two transarterial procedures. Conclusions: Repeated endovascular treatments negatively impacted the survival of HCC patients, especially sorafenib-responders. An early switch to systemic therapies should be considered in cases that are unlikely to respond.

Establishment of a circRNA-regulated E3 ubiquitin ligase signature and nomogram to predict immunotherapeutic efficacy and prognosis in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a common type of malignant tumor where the prognosis is dismal. Circular RNA (CircRNA) is a novel RNA that regulates downstream gene transcription and translation to influence the progression of HCC. However, the regulatory relationship that exists between E3 ligases, which is a class of post-translational modifying proteins, and circRNA remains unclear.MethodsBased on the E3 ubiquitin ligase in the competitive endogenous RNA (ceRNA) network, a circRNA-regulated E3 ubiquitin ligase signature (CRE3UL) was developed. A CRE3UL signature was created using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis and merged it with clinicopathologic characteristics to generate a nomogram for prognosis prediction. The pRRophetic algorithm was utilized and immunological checkpoints were analyzed to compare the responses of patients in the high-risk group (HRG) and low-risk group (LRG) to targeted therapy and immunotherapy. Finally, experimental research will further elucidate the relationship between E3 ubiquitin ligase signature and HCC.ResultsHRG patients were found to have a worse prognosis than LRG patients. Furthermore, significant variations in prognosis were observed among different subgroups based on various clinical characteristics. The CRE3UL signature was identified as being an independent prognostic indicator. The nomogram that combined clinical characteristics and the CRE3UL signature was found to accurately predict the prognosis of HCC patients and demonstrated greater clinical utility than the current TNM staging approach. According to anticancer medication sensitivity predictions, the tumors of HRG patients were more responsive to gefitinib and nilotinib. From immune-checkpoint markers analysis, immunotherapy was identified as being more probable to assist those in the HRG.ConclusionsWe found a significant correlation between the CRE3UL signature and the tumor microenvironment, enabling precise prognosis prediction for HCC patients. Additionally, a nomogram was developed that performs well in predicting the overall survival (OS) of HCC patients. This provides valuable guidance for clinicians in devising specific personalized treatment strategies.Graphical

Stereotactic body radiotherapy (SBRT) combined with transcatheter arterial chemoembolization (TACE) and tyrosine kinase inhibitors (TKIs) versus TACE and TKIs alone for unresectable hepatocellular carcinoma (uHCC) with portal vein tumor thrombus (PVTT): A randomized controlled trial.

4102 Background: TKIs-based systemic therapy is a primary treatment option for uHCC. Clinical studies have established the effectiveness and safety of radiotherapy (RT) in patients with PVTT, who typically have a poor prognosis. Previous research has shown that combining TACE and RT can extend survival in HCC patients with PVTT compared to sorafenib treatment. This study aimed to explore the efficacy and safety of combined local therapy (TACE plus SBRT) and TKIs in HCC patients with PVTT. Methods: This single-center, randomized controlled study enrolled patients aged ≥18 years with HCC and PVTT. Additional eligible criteria included ECOG PS 0-1, Child-Pugh score ≤B7, no extrahepatic metastasis on CT scan, and normal liver volume ≥700cc. Patients were randomly assigned to either the SBRT+TACE+TKIs group (Group A) or the TACE+TKIs group (Group B). SBRT was administered at 35-45 Gy in 5 fractions, 3 fractions per week. TKIs (sorafenib 0.4g bid; donafenib 0.2g bid; or lenvatinib 8mg/12mg qd, depending on body weight) were paused during the perioperative period of TACE (up to 4 times). The primary endpoint was the 6-month progression-free survival (PFS) rate, with secondary endpoints including objective response rate (ORR), overall survival (OS), disease control rate (DCR) and treatment-related adverse events (TRAEs). Treatment response was evaluated using mRECIST. Results: A total of 90 patients with uHCC were enrolled from June 2019 to October 2023 in this trial, with baseline characteristics presented in the Table. As of January 5th 2024, the median follow-up time was 14.5 months [IQR, 2.43-55]. The 6-month PFS rate was significantly higher in Group A (78%) compared to Group B (36%, P=0.0245). Group A also showed prolonged median PFS (9.75 vs. 4.89 months, HR=1.703 [95%CI, 1.045-2.777], P=0.0245) and OS (17.93 vs. 9.61 months, HR=1.869 [95%CI, 1.059-3.266], P=0.017). Improved ORR was observed in the SBRT group (74.4% vs. 40.5%, P=0.0015). DCR was 81.4% in Group A and 66.7% in Group B ( P=0.1211). The most common grade 3/4 TRAEs were hypertension (Group A: 17.8%, Group B: 13.3%, P=0.5608), ALT elevation (Group A: 26.7%, Group B: 22.2%, P=0.6237), and AST elevation (Group A: 22.2%, Group B: 15.6%, P=0.4191). There was one treatment-related death in Group B due to liver failure. Conclusions: In uHCC patients with PVTT, the combination of SBRT and TACE-TKIs showed significant survival benefit without the identification of any severe safety concerns. Clinical trial information: ChiCTR1900025300. [Table: see text]

Comprehensively analysis of IL33 in hepatocellular carcinoma prognosis, immune microenvironment and biological role.

IL33 plays an important role in cancer. However, the role of liver cancer remains unclear. Open-accessed data was obtained from the Cancer Genome Atlas, Xena, and TISCH databases. Different algorithms and R packages are used to perform various analyses. Here, in our comprehensive study on IL33 in HCC, we observed its differential expression across cancers, implicating its role in cancer development. The single-cell analysis highlighted its primary expression in endothelial cells, unveiling correlations within the HCC microenvironment. Also, the expression level of IL33 was correlated with patients survival, emphasizing its potential prognostic value. Biological enrichment analyses revealed associations with stem cell division, angiogenesis, and inflammatory response. IL33's impact on the immune microenvironment showcased correlations with diverse immune cells. Genomic features and drug sensitivity analyses provided insights into IL33's broader implications. In a pan-cancer context, IL33 emerged as a potential tumour-inhibitor, influencing immune-related molecules. This study significantly advances our understanding of IL33 in cancer biology. IL33 exhibited differential expression across cancers, particularly in endothelial cells within the HCC microenvironment. IL33 is correlated with the survival of HCC patients, indicating potential prognostic value and highlighting its broader implications in cancer biology.

Precision medicine for papillary thyroid carcinoma (PTC) variants: A machine learning approach to prognosis and treatment guidance.

e18072 Background: PTC is a common endocrine cancer with a good prognosis, but aggressive subtypes, such as Hürthle cell (HCC) and columnar cell variants (CCV), pose challenges due to their higher recurrence and metastasis rates. To provide personalized care, we applied machine learning to evaluate the treatment effectiveness and develop precise prognostic models for PTC variants. Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study’s analysis (2000–2019). Patients who met any of the following criteria were excluded: diagnosis not confirmed by histology, previous history of cancer or with other concurrent malignancies, and unknown data. To identify prognostic variables, we conducted Cox regression analysis and constructed prognostic models using machine learning (ML) algorithms to predict the 5-year survival. Patient records were randomly divided into training (70 %) and validation (30 %) sets. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic curve was used to validate the accuracy and reliability of the ML models. Results: The study population comprised 3690 patients. Among them 3180 patients with CCV and 510 patients with HCC, respectively. Most patients (62.8%) were 45 years or older, with a median age of 52 years. A total of 56.9% of patients had a tumor size greater than 2 cm, with a median tumor size of 3.1 cm. The largest racial group was white, comprising 83.8% of the cases, and 11.8% of the cases were Asian. Most cases were regional (53.4%, n=1969), followed by localized (38.3%, n= 1413). Multivariate Cox regression analysis revealed that N1 negatively affected the survival of HCC patients. CCV has a favorable prognosis after surgery, radiotherapy, or total thyroidectomy. Poor prognosis in CCV is associated with black race, large tumor size, and T4 stage. Improved survival in the localized/regional stage and decreased survival with male sex, older age, distant metastasis, and advanced AJCC stage in both PTC subtypes. ML models revealed that the random forest classifier (RFC) and K-Nearest Neighbors (KNN) accurately predicted outcomes, followed by Logistic Regression (LR) models. The highest contributing factors were AJCC staging, tumor size, and T aspect of TNM staging. Conclusions: Our study offers a method for evaluating and treating patients with PTC variants. The machine learning model that we created serves as a useful and personalized resource to aid in clinical decision-making processes.[Table: see text]