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Background: Treatment options for advanced intrahepatic cholangiocarcinoma (ICC) are currently limited. Chemo-containing regimens are the mainstay treatments but associated with notable toxicity, poor tolerance, and reduced compliance, necessitating exploration of alternative therapies. Lenvatinib plus PD-1 inhibitors has shown substantial clinical activity in preliminary studies. This study aimed to assess the effectiveness and safety of lenvatinib plus toripalimab (a novel PD-1 antibody) as chemo-free therapy in advanced ICC. Methods: This retrospective study included consecutive advanced ICC patients receiving lenvatinib plus toripalimab between February 2019 and December 2023. The main outcomes were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and safety. Prognostic factors and exploratory analyses for genetic alternations were also conducted. Results: A total of 78 patients were included, with a median follow-up of 25.9 months. Median OS and PFS were 11.3 (95% CI: 9.5–13.1) and 5.4 (95% CI: 3.8–7.0) months, respectively. ORR was 19.2% and DCR was 75.6%. The incidence of grade 3 or 4 adverse events (AEs) was 50.0%, with no grade 5 AEs reported. Patients with normal baseline CA19-9 levels exhibited a higher ORR (p = 0.011), longer PFS (11.5 versus 4.6 months; HR 0.47; p=0.005), and OS (21.0 versus 9.7 months; HR 0.43; p=0.003). The presence of IDH1 mutations correlated with increased ORR (60.0% versus 8.9%, p=0.016). Conclusion: Lenvatinib plus toripalimab represents an effective and well-tolerated chemo-free therapeutic option for advanced ICC. Baseline CA19-9 levels and IDH1 mutations may serve as predictive treatment-related biomarkers. [ABSTRACT FROM AUTHOR]

Systemic therapies using programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) inhibitors have demonstrated commendable efficacy in some patients with advanced hepatocellular carcinoma (HCC); however, other individuals do not respond favorably. Hence, identifying the biomarkers, the prognostic factors, and their underlying mechanisms is crucial. In this review, we summarized the latest advancements in this field. Within the tumor microenvironment, PD-L1 expression is commonly utilized to predict response. Moreover, the characteristics of tumor-infiltrating lymphocytes are associated with the effectiveness of immunotherapy. Preclinical studies have identified stimulatory dendritic cells, conventional dendritic cells, and macrophages as potential biomarkers. The emergence of single-cell sequencing and spatial transcriptomics has provided invaluable insights into tumor heterogeneity through the lens of single-cell profiling and spatial distribution. With the widespread adoption of next-generation sequencing, certain genomic characteristics, including tumor mutational burden, copy number alterations, specific genes (TP53, CTNNB1, and GZMB), and signaling pathways (WNT/β-catenin) have been found to correlate with prognosis. Furthermore, clinical features such as tumor size, number, and metastasis status have demonstrated prognostic value. Notably, common indicators such as the Child-Pugh score and Eastern Cooperative Oncology Group score, which are used in patients with liver diseases, have shown potential. Similarly, commonly employed laboratory parameters such as baseline transforming growth factor beta, lactate dehydrogenase, dynamic changes in alpha-fetoprotein (AFP) and abnormal prothrombin, CRAFITY score (composed of C-reactive protein and AFP), and immune adverse events have been identified as predictive biomarkers. Novel imaging techniques such as EOB-MRI and PET/CT employing innovative tracers also have potential. Moreover, liquid biopsy has gained widespread use in biomarker studies owing to its non-invasive, convenient, and highly reproducible nature, as well as its dynamic monitoring capabilities. Research on the gut microbiome, including its composition, dynamic changes, and metabolomic analysis, has gained considerable attention. Efficient biomarker discovery relies on continuous updating of treatment strategies. Next, we summarized recent advancements in clinical research on HCC immunotherapy and provided an overview of ongoing clinical trials for contributing to the understanding and improvement of HCC immunotherapy. [ABSTRACT FROM AUTHOR]

Background: A programmed cell death protein-1 (PD-1) inhibitor combined with lenvatinib and Gemox chemotherapy as first-line therapy demonstrated high anti-tumor activity against biliary tract cancer in phase II clinical trials. Herein, we aimed to investigate the efficacy and safety for advanced intrahepatic cholangiocarcinoma (ICC) in a multicenter real-world study. Methods: Patients with advanced ICC who received PD-1 inhibitor combined with lenvatinib and Gemox chemotherapy were retrospectively screened at two medical centers. The primary endpoints were overall survival (OS) and progression-free survival (PFS), whereas the secondary endpoints were objective response rate (ORR), disease control rate (DCR), and safety. Prognostic factors for survival were analyzed. Results: Fifty-three patients with advanced ICC were included in this study. The median follow-up time was 13.7 (95% confidence interval (CI): 12.9–17.2) months. The median OS and PFS were 14.3 (95% CI: 11.3–NR) and 8.63 (95% CI: 7.17–11.6) months, respectively. The ORR, DCR, and clinical benefit rate were 52.8, 94.3, and 75.5%, respectively. In the multivariate analysis, the tumor burden score (TBS), tumor-node metastasis classification (TNM) stage, and PD-L1 expression were independent prognostic factors for OS and PFS. All patients experienced adverse events (AEs), 41.5% (22/53) experienced grade 3 or 4 AEs, including fatigue (8/53, 15.1%) and myelosuppression (7/53, 13.2%). No grade 5 AEs were reported. Conclusion: PD-1 inhibitors combined with lenvatinib and Gemox chemotherapy represent an effective and tolerable regimen for advanced ICC in a multicenter retrospective real-world study. TBS, TNM stage, and PD-L1 expression can be used as potential prognostic factors for OS and PFS. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy. Methods: We constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data. Results: This study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups. Conclusions: The mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups' differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients. [ABSTRACT FROM AUTHOR]

Enhancement in boiler efficiency via controlled operation could lead to energy savings and reduction in pollutant emission. Activities such as scheduled maintenance could be improved by increasing boiler availability and reducing running costs. However, the time interval between recommended maintenance is varied depending on boilers. The application of fault detection, diagnosis and prognosis (FDDP) in industrial boilers plays an important role in optimizing operation, early‐warning of faults, and identification of root causes. This review discusses the application of machine learning (ML)‐based algorithms (knowledge‐driven and data‐driven) for FDDP, thus allowing the identification of fit‐for‐purpose techniques for specific applications leading to improved efficiency, operability, and safety. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint inhibitor (ICI) therapy elicits durable antitumor responses in patients with many types of cancer. Genomic mutations may be used to predict the clinical benefits of ICI therapy. NOTCH homolog-4 (NOTCH4) is frequently mutated in several cancer types, but its role in immunotherapy is still unclear. Our study is the first to study the association between NOTCH4 mutation and the response to ICI therapy.Methods: We tested the predictive value of NOTCH4 mutation in the discovery cohort, which included non-small cell lung cancer, melanoma, head and neck squamous cell carcinoma, esophagogastric cancer, and bladder cancer patients, and validated it in the validation cohort, which included non-small cell lung cancer, melanoma, renal cell carcinoma, colorectal cancer, esophagogastric cancer, glioma, bladder cancer, head and neck cancer, cancer of unknown primary, and breast cancer patients. Then, the relationships between NOTCH4 mutation and intrinsic and extrinsic immune response mechanisms were studied with multiomics data.Results: We collected an ICI-treated cohort (n = 662) and found that patients with NOTCH4 mutation had better clinical benefits in terms of objective response rate (ORR: 42.9% vs 25.9%, P = 0.007), durable clinical benefit (DCB: 54.0% vs 38.1%, P = 0.021), progression-free survival (PFS, hazard ratio [HR] = 0.558, P < 0.001), and overall survival (OS, HR = 0.568, P = 0.006). In addition, we validated the prognostic value of NOTCH4 mutation in an independent ICI-treated cohort (n = 1423). Based on multiomics data, we found that NOTCH4 mutation is significantly associated with enhanced immunogenicity, including a high tumor mutational burden, the expression of costimulatory molecules, and activation of the antigen-processing machinery, and NOTCH4 mutation positively correlates activated antitumor immunity, including infiltration of diverse immune cells and various immune marker sets.Conclusions: Our findings indicated that NOTCH4 mutation serves as a novel biomarker correlated with a better response to ICI therapy. [ABSTRACT FROM AUTHOR]

Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA. [ABSTRACT FROM AUTHOR]

Hypoalbuminemia usually predicts higher mortality, longer hospital stay, and more frequent readmission.1 About 40%-60% of coronavirus disease (COVID-19) patients develop hypoalbuminemia at admission.2,3 This may be due to an increase of capillary permeability caused by a systemic inflammatory response,4 which leads to the leakage of serum albumin (ALB) into interstitial space,5 poor nutritional status, and impaired liver function.1 COVID-19 patients with hypoalbuminemia are more severe and/or critical,2 and have a higher probability of intensive care unit admission and higher risk of death.6,7 However, few studies have evaluated the dynamic change of serum ALB level in COVID-19 patients with hypoalbuminemia on the patients' death. In the human ALB infusion group, ALB is defined as serum ALB level obtained on the first retest after human ALB infusion minus baseline serum ALB level; in the nonhuman ALB infusion group, ALB is defined as serum ALB level obtained on the second test after hospitalization minus baseline serum ALB level. Dynamic change of serum albumin level can predict the prognosis of COVID-19 patients with hypoalbuminemia The inclusion criteria were as follows: (1) COVID-19 patients had a serum ALB level of <35 g/L at admission and/or during hospitalization; and (2) serum ALB level was retested. [Extracted from the article]

Purpose: The immune prognostic index (IPI) has been used as a prognostic biomarker in various cancers. However, the prognostic value of the IPI in gallbladder cancer remains to be determined. Patients and Methods: This study included 139 patients who were diagnosed with gallbladder cancer after surgical resection from 2003 to 2017. We used a Kaplan–Meier curve analysis to evaluate the overall survival (OS). Cox proportional hazards regression methodology was used to identify significant independent prognostic factors. Prognostic nomograms for predicting OS were established to achieve superior discriminatory abilities. The prognostic nomograms were verified according to the concordance index, calibration curves, and decision curve analyses in the training cohort and validation cohort. Results: Of all 139 patients, 87 (62.6%) patients accepted R0 resection, 32% and 68% were stratified into the good and poor IPI group, respectively. The median OS was 55.9 (range, 5.93– 182.7) months in the good IPI group and 15.47 (range, 0.29– 190.37) months in the poor IPI group (P < 0.001). In the multivariate Cox model, the IPI was an independent predictor of OS along with the CA19-9, curative resection, and postoperative chemoradiotherapy. A nomogram based on these factors was efficient in predicting 1-, 3-, and 5-year survival probabilities. The nomogram showed higher sensitivity and specificity than the current cancer TNM staging system in the training cohort and validation cohort. Conclusion: The IPI is an independent prognostic factor in gallbladder cancer. Our IPI-based nomogram can serve as a useful and convenient prognostic tool for gallbladder cancer. [ABSTRACT FROM AUTHOR]

Background: Systemic immune-inflammation index (SII) is considered to be a prognostic marker in several cancers. However, the prognostic value of baseline pre-operative SII in gallbladder carcinoma (GBC) has not been evaluated. This study aimed to determine the prognostic significance of SII and generate a predictive nomogram. Methods: We retrospectively studied 142 GBC patients who underwent surgical resection at the Peking Union Medical College Hospital between 2003 and 2017. SII, neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) were evaluated for their prognostic values. A multivariate Cox proportional hazards model was used for the recognition of significant factors. Then, the cohort was randomly divided into the training and the validation set. A nomogram was constructed using SII and other selected indicators in the training set. C-index, calibration plots, and decision curve analysis were performed to assess the nomogram's clinical utility in both the training and the validation set. Results: The predictive accuracy of SII (Harrell's concordance index [C-index]: 0.624), NLR (C-index: 0.626), and LMR (C-index: 0.622) was evaluated. The multivariate Cox model showed that SII was a superior independent predictor than NLR and LMR. SII level (≥600) (hazard ratio [HR]: 1.694, 95% confidence interval [CI]: 1.069–2.684, p = 0.024), carbohydrate antigen (CA) 19-9 level (≥37 U/ml) (HR: 2.407, 95% CI: 1.472–3.933, p < 0.001), and TNM stage (p = 0.026) were selected to construct a nomogram for predicting overall survival (OS). The predictive ability of this model was assessed by C-index (0.755 in the training set, 0.754 in the validation set). Good performance was demonstrated by the calibration plot. A high net benefit was proven by decision curve analysis (DCA). Conclusion: SII is an independent prognostic indicator in GBC patients after surgical resection, and the nomogram based on it is a useful tool for predicting OS. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide, which can be explained at least in part by its propensity towards metastasis and the limited efficacy of adjuvant therapy. MetastamiRs are miRNAs that promote or suppress migration and metastasis of cancer cells, and their functional status is significantly correlated with HCC prognosis. Unlike targeted therapy, metastamiRs have the potential to target multiple genes and signaling pathways and dramatically suppress cancer metastasis. In this review, we discuss the regulatory role of metastamiRs in the HCC invasion-metastasis cascade. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis has shown that many extensively studied metastamiRs target several critical signaling pathways and these have remarkable therapeutic potential in HCC. The information reviewed here may assist in further anti-HCC miRNA drug screening and development. [ABSTRACT FROM AUTHOR]

Abstract: Background: This study aimed to clarify the risk factors for survival and recurrence of hepatocellular carcinoma (HCC) in a cohort of Chinese HCC patients after hepatectomy and to compare 6 developed staging systems. Methods: A retrospective analysis was performed on 165 consecutive patients. The Kaplan–Meier method was used to calculate survival. Postoperative prognostic factors were evaluated using univariate and multivariate analyses. The overall predictive power of each staging system was evaluated by the area under the receiver operating characteristic curve. Results: The overall survival rates of 1, 3, and 5 years were 81.2%, 58.6%, and 56.7%, respectively, and the corresponding disease-free survival rates were 52.9%, 23.3%, and 15.5%, respectively. α-fetoprotein level and blood transfusion were correlated significantly with patients'' overall survival, and portal vein thrombosis and tumor size (>5 cm) were correlated significantly with poor disease-free survival. Conclusions: The French staging system is better for predicting the prognosis of HCC patients receiving surgical treatment. [Copyright &y& Elsevier]

The tumour mutation burden (TMB) and immune microenvironment (IME) are reportedly associated with immunotherapy responses, but this relationship remains unclear in hepatocellular carcinoma (HCC). We classified HCC patients in the LIHC cohort from The Cancer Genome Atlas into low- and high-TMB groups and evaluated differences in immune infiltrates. Additionally, differentially expressed genes in the low- and high-TMB groups were identified, and functional analyses were conducted. A risk score model was constructed based on three differentially expressed immune genes (DEIGs). The Tumor Immune Estimation Resource database was utilized to analyse how the IME was affected by the three hub DEIGs. Finally, a prognostic nomogram combining risk scores and stages was established and externally validated with the International Cancer Genome Consortium and GSE14520 cohorts. High-TMB (top 20%) patients exhibited a worse prognosis (P = 0.017). Follicular helper cells (P = 0.001) and activated natural killer (NK) cells (P = 0.003) were enriched in high-TMB patients, while resting dendritic cells (P = 0.002) were enriched in low-TMB samples. A risk score model was generated with three hub DEIGs (CCR7 , STC2 and S100A9) to predict overall survival (OS) in HCC cohorts. Moreover, copy number variations (CNVs) mainly reduced infiltration levels. The nomogram performed better than the risk score model in the training and validation datasets. Higher TMB was associated with immune microenvironment diversification and worse prognosis in HCC. Mutations in three hub TMB-associated DEIGs were correlated with lower immune cell infiltration. • Higher tumour mutation burden indicated worse prognosis in hepatocellular carcinoma. • Immune microenvironment varied in patients with different tumour mutation burden. • A prognostic nomogram was built and validated in two external datasets. Tumour mutation burden (TMB) and the immune microenvironment (IME) are reportedly associated with immunotherapy responses, but this relationship remains unclear in hepatocellular carcinoma (HCC). We classified HCC patients in the liver hepatocellular carcinoma cohort from The Cancer Genome Atlas into low- and high-TMB groups and evaluated differences in immune infiltrates. Additionally, differentially expressed genes in the low- and high-TMB groups were identified, and functional analyses were conducted. A risk score model was constructed based on three differentially expressed immune genes (DEIGs). The Tumor Immune Estimation Resource database was utilized to analyse how the IME was affected by the three hub DEIGs. Finally, a prognostic nomogram combining risk scores and stages was established and externally validated with the International Cancer Genome Consortium and GSE14520 cohorts. High-TMB (top 20%) patients exhibited a worse prognosis (P = 0.017). Follicular helper cells (P = 0.001) and activated natural killer cells (P = 0.003) were enriched in high-TMB patients, while resting dendritic cells (P = 0.002) were enriched in low-TMB samples. A risk score model was generated with three hub DEIGs (CCR7 , STC2 and S100A9) to predict overall survival in HCC cohorts. Moreover, copy number variations mainly reduced infiltration levels. The nomogram performed better than the risk score model in the training and validation datasets. Higher TMB was associated with IME diversification and worse prognosis in HCC. Mutations in three hub TMB-associated DEIGs correlated with lower immune cell infiltration. [ABSTRACT FROM AUTHOR]