The role of liver cancer stem cells in hepatocellular carcinoma metastasis

Metastasis accounts for the vast majority of cancer deaths; however, this complex process has yet to be fully explained. To form metastases, cancer cells must undergo a series of steps, known as the "Metastatic cascade", each of which requires a specific functional transformation. Cancer stem cells (CSCs) play a vital role in tumor metastasis, but their dynamic behavior and regulatory mechanisms have not been fully elucidated. Based on the "Metastatic cascade" theory, this review summarizes the effect of liver CSCs on the metastatic biological programs that underlie the dissemination and metastatic growth of cancer cells. Liver CSCs have the capacity to initiate distant organ metastasis via EMT, and the microenvironment transformation that supports the ability of these cells to disseminate, evade immune surveillance, dormancy, and regenerate metastasis. Understanding the heterogeneity and traits of liver CSCs in these processes is critical for developing strategies to prevent and treat metastasis of advanced hepatocellular carcinoma (HCC).

TCR-like bispecific antibodies toward eliminating infected hepatocytes in HBV mouse models

Therapeutics for eradicating hepatitis B virus (HBV) infection are still limited and current nucleos(t)ide analogs (NAs) and interferon are effective in controlling viral replication and improving liver health, but they cannot completely eradicate the hepatitis B virus and only a very small number of patients are cured of it. The TCR-like antibodies recognizing viral peptides presented on human leukocyte antigens (HLA) provide possible tools for targeting and eliminating HBV-infected hepatocytes. Here, we generated three TCR-like antibodies targeting three different HLA-A2.1-presented peptides derived from HBV core and surface proteins. Bispecific antibodies (BsAbs) were developed by fuzing variable fragments of these TCR-like mAbs with an anti-CD3ϵ antibody. Our data demonstrate that the BsAbs could act as T cell engagers, effectively redirecting and activating T cells to target HBV-infected hepatocytes in vitro and in vivo. In HBV-persistent mice expressing human HLA-A2.1, two infusions of BsAbs induced marked and sustained suppression in serum HBsAg levels and also reduced the numbers of HBV-positive hepatocytes. These findings highlighted the therapeutic potential of TCR-like BsAbs as a new strategy to cure hepatitis B.

Multiomics and single-cell sequencings reveal the specific biological characteristics of low Ki-67 triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) displays high heterogeneity. The majority of TNBC cases are characterized by high Ki-67 expression. TNBC with low Ki-67 expression accounts for only a small fraction of cases and has been relatively less studied.

Methods

This study analyzed a large single-center multiomics TNBC data set, combined with a single-cell data set. The clinical, genomic, and metabolic characteristics of patients with low Ki-67 TNBC were analyzed.

Results

The clinical and pathological characteristics were analyzed in 2217 TNBC patients. Low Ki-67 TNBC was associated with a higher patient age at diagnosis, a lower proportion of invasive ductal carcinoma, increased alterations in the PI3K-AKT-mTOR pathway, upregulated lipid metabolism pathways, and enhanced infiltration of M2 macrophages. High Ki-67 TNBC exhibited a higher prevalence of TP53 gene mutations, elevated nucleotide metabolism, and increased infiltration of M1 macrophages.

Conclusions

We identified specific genomic and metabolic characteristics unique to low Ki-67 TNBC, which have implications for the development of precision therapies and patient stratification strategies.

Increases in 4-Acetaminobutyric Acid Generated by Phosphomevalonate Kinase Suppress CD8+ T Cell Activation and Allow Tumor Immune Escape

Certain metabolites in the tumor microenvironment (TME) can alter innate immunity. Here, it is shown how phosphomevalonate kinase (PMVK) allows hepatocellular carcinoma (HCC) cells to overcome the anti-tumor immunity mediated by CD8+ T cells. In HCCs, depletion of PMVK is required to facilitate CD8+ T cell activation and their subsequent suppression of tumor growth. Mechanistically, PMVK phosphorylates and stabilizes glutamate decarboxylase 1 (GAD1), thus increasing the synthesis of γ-aminobutyric acid (GABA), which normally functions as a neurotransmitter. However, PMVK also recruits acetyl-CoA acetyltransferase 1 (ACAT1) and allows it to convert GABA, to 4-acetaminobutyric acid (4-Ac-GABA), which is released into the TME. There, 4-Ac-GABA activates the GABAA receptor (GABAAR) on CD8+ T cells, which inhibits AKT1 signaling. This in turn suppresses CD8+ T cell activation, intratumoral infiltration, and the anti-tumor response. Inhibiting PMVK or GABAAR in HCC mouse models overcomes resistance to anti-PD-1 immune checkpoint therapy. These findings reveal non-canonical and cooperative functions among the key metabolic enzymes PMVK, GAD1, and ACAT1 that reprogram glutamine metabolism to synthesize a potent CD8+ T cell inhibitor 4-Ac-GABA. Blocking 4-Ac-GABA signaling in CD8+ T cells, particularly when combined with immune checkpoint inhibition, potentially represents a new and potent form of immunotherapy.

SGDBNet: A scene-class guided dual branch network for port UAV images oil spill detection

The unmanned aerial vehicle (UAV) is usually flexible and frequently low-altitude flying without the influence of clouds and severe weather, and it is widely used for port oil spill detection (OSD). However, the background of the port is usually complex, the oil spills in UAV images are usually small and irregular, as well as the oil boundary is fuzzy, which has led to the failure of existing methods in accurately detecting the port oil spill. Here, we propose a scene-class guided dual branch network for port OSD based on UAV images, which can locate the oil spill areas of different sizes and suppress the influence caused by complex backgrounds. Specifically, the dual-branch network consists of semantic segmentation and image classification branches. The image classification branch utilizes the scene-class as the label and further can extract the feature attention, which can guide the semantic segmentation branch to learn the key area features. Second, we propose a multi-scale arbitrary shape convolution module, which can address the challenges caused by fuzzy oil boundaries and irregular small objects. Finally, due to the imbalance between oil spill pixels and other pixels, we design a joint loss to optimize the network. We evaluate our proposed method on a public UAV OSD dataset. The results show that our method is superior to the state-of-the-art method, achieving mIoU of 90.22 %, A of 96.03 %, P of 91.99 %, R of 92.56 %, and F1 of 92.28 %, which represents the feasibility of our method in port OSD and its potential to save a lot of manpower and material resources. The ablation experiment further demonstrates the effectiveness of each designed part.

Proteogenomic Characterization of Early Intrahepatic Recurrence after Curative-Intent Treatment of Colorectal Liver Metastases

Clinical and pathological factors are insufficient to accurately identify patients at risk of early recurrence after curative-intent treatment of colorectal liver metastases (CRLM). This study aimed to identify candidate prognostic proteogenomic biomarkers for early intrahepatic recurrence after curative-intent resection of CRLM. Patients diagnosed with intrahepatic recurrence within 6 months of liver resection were categorized as the "early recurrence" group, while those who achieved a recurrence-free status for 10 years were designated as "durable remission". Comprehensive genomic and proteomic profiling of fresh frozen samples from these prognostically distinct groups was performed using the TruSight Oncology 500 assay and label-free data-dependent acquisition liquid chromatography-mass spectrometry. Genetic alterations were identified in 117 of the 523 profiled genes in patients with early recurrence. The most common somatic mutations linked to early recurrence were TP53 (88%), APC (71%), KRAS (38%), and SMAD4 (21%). SMAD4 alterations were absent in samples from patients with a durable remission. Calponin-2, versican core protein, glutathione peroxidase 3, fibulin-5, and amyloid-β precursor protein were upregulated more than 2-fold in early recurrence. Exploratory analysis of these proteogenomic biomarkers suggests that SMAD4, calponin-2, and glutathione peroxidase 3 may have the potential to predict early recurrence, enabling improved prognostication and precision oncology in CRLM.

Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Predictive Biomarkers and Resistance Mechanisms of Checkpoint Inhibitors in Malignant Solid Tumors

Predictive biomarkers for immune checkpoint inhibitors (ICIs) in solid tumors such as melanoma, hepatocellular carcinoma (HCC), colorectal cancer (CRC), non-small cell lung cancer (NSCLC), endometrial carcinoma, renal cell carcinoma (RCC), or urothelial carcinoma (UC) include programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), defective deoxyribonucleic acid (DNA) mismatch repair (dMMR), microsatellite instability (MSI), and the tumor microenvironment (TME). Over the past decade, several types of ICIs, including cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, anti-programmed cell death 1 (PD-1) antibodies, anti-programmed cell death ligand 1 (PD-L1) antibodies, and anti-lymphocyte activation gene-3 (LAG-3) antibodies have been studied and approved by the Food and Drug Administration (FDA), with ongoing research on others. Recent studies highlight the critical role of the gut microbiome in influencing a positive therapeutic response to ICIs, emphasizing the importance of modeling factors that can maintain a healthy microbiome. However, resistance mechanisms can emerge, such as increased expression of alternative immune checkpoints, T-cell immunoglobulin (Ig), mucin domain-containing protein 3 (TIM-3), LAG-3, impaired antigen presentation, and alterations in the TME. This review aims to synthesize the data regarding the interactions between microbiota and immunotherapy (IT). Understanding these mechanisms is essential for optimizing ICI therapy and developing effective combination strategies.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

The gut microbiome modulate response to immunotherapy in cancer

Gut microbiota have been reported to play an important role in the occurrence and development of malignant tumors. Currently, clinical studies have identified specific gut microbiota and its metabolites associated with efficacy of immunotherapy in multiple types of cancers. Preclinical investigations have elucidated that gut microbiota modulate the antitumor immunity and affect the efficacy of cancer immunotherapy. Certain microbiota and its metabolites may favorably remodel the tumor microenvironment by engaging innate and/or adaptive immune cells. Understanding how the gut microbiome interacts with cancer immunotherapy opens new avenues for improving treatment strategies. Fecal microbial transplants, probiotics, dietary interventions, and other strategies targeting the microbiota have shown promise in preclinical studies to enhance the immunotherapy. Ongoing clinical trials are evaluating these approaches. This review presents the recent advancements in understanding the dynamic interplay among the host immunity, the microbiome, and cancer immunotherapy, as well as strategies for modulating the microbiome, with a view to translating into clinical applications.

A Novel Monoclonal Antibody against PD-1 for the Treatment of Viral Oncogene-Induced Tumors or Other Cancer

Programmed cell death 1 (PD-1) and programmed death-ligand 1 (PD-L1) interact to form an immune checkpoint fostering viral infection and viral oncogene-induced tumorigenesis. We generated a novel anti-human PD-1, humanized monoclonal antibody P1801 and investigated its pharmacologic, pharmacokinetic (PK), and pharmacodynamic properties. In vitro binding assays revealed that P1801 uniquely binds to human PD-1 and inhibits its interaction with PD-L1/2. It showed a minor effect on the induction of antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). P1801 significantly induced the release of IL-2 from activated T-cells but not from nonactivated T-cells. A dose-dependent linear PK profile was observed for the cynomolgus monkeys treated with repeated doses of P1801 at 5 mg/kg to 200 mg/kg once weekly. A four-week repeat-dose toxicity study revealed that P1801 given weekly was safe and well tolerated at doses ranging from 5 to 200 mg/kg/dose. No pathological abnormalities were noted. In humanized PD-1 mice harboring human PD-L1-expressing colon tumor cells, P1801 administered intraperitoneally twice per week at 12 mg/kg significantly inhibited tumor growth and prolonged mouse survival. P1801 displayed unique binding properties different from pembrolizumab and nivolumab. Therefore, it showed distinctive immunological reactions and significant antitumor activities. We are initiating a Phase 1 clinical study to test its combination use with ropeginterferon alfa-2b, which also has antiviral and antitumor activities, for the treatment of cancer.

Serum sPD-1 and sPD-L1 as predictive biomarkers for HBsAg clearance in HBeAg-negative CHB patients undergoing IFN-based therapy

BACKGROUND AND AIMS

For chronic hepatitis B (CHB) patients, there is still a need to improve hepatitis B surface antigen (HBsAg) clearance rates. This study aimed to assess the predictive effectiveness of soluble programmed cell death-1 (sPD-1) and soluble programmed cell death ligand-1 (sPD-L1) for HBsAg clearance in HBeAg-negative CHB patients undergoing peginterferon (Peg-IFN)-based antiviral treatment.

METHODS

This study encompassed 280 patients undergoing treatment with Peg-IFNα. Serum levels of sPD-1 and sPD-L1 were measured using ELISA kits at baseline, as well as at 12, 24 and 48 weeks. The primary endpoint of the study was the determination of HBsAg clearance at 48 weeks. Logistic regression analysis was employed to identify predictors of HBsAg clearance.

RESULTS

The clearance group demonstrated significantly lower serum sPD-L1 levels compared to the non-clearance group. While both groups exhibited an increase in sPD-1 levels, only the clearance group showed a rise in sPD-L1 levels. Multivariate analysis identified sPD-L1 increase at 24 weeks, and HBsAg decline at 24 weeks as predictors for HBsAg clearance at 48 weeks. The combined use of these indicators showed a predictive performance for HBsAg clearance with an AUROC of 0.907 (95% CI: 0.861-0.953, p < 0.001).

CONCLUSIONS

The study revealed an inverse relationship between the trends of sPD-1/sPD-L1 and HBsAg clearance during combined IFN and NAs treatment. Moreover, the magnitude of HBsAg reduction and sPD-L1 increase emerged as significant predictors for HBsAg clearance.

HOXA9 versus HOXB9; particular focus on their controversial role in tumor pathogenesis

The Homeobox (HOX) gene family is essential to regulating cellular processes because it maintains the exact coordination required for tissue homeostasis, cellular differentiation, and embryonic development. The most distinctive feature of this class of genes is the presence of the highly conserved DNA region known as the homeobox, which is essential for controlling their regulatory activities. Important players in the intricate process of genetic regulation are the HOX genes. Many diseases, especially in the area of cancer, are linked to their aberrant functioning. Due to their distinctive functions in biomedical research-particularly in the complex process of tumor advancement-HOXA9 and HOXB9 have drawn particular attention. HOXA9 and HOXB9 are more significant than what is usually connected with HOX genes since they have roles in the intricate field of cancer and beyond embryonic processes. The framework for a focused study of the different effects of HOXA9 and HOXB9 in the context of tumor biology is established in this study.

Host-Gut Microbiota Metabolic Interactions and Their Role in Precision Diagnosis and Treatment of Gastrointestinal Cancers

The critical role of the gut microbiome in gastrointestinal cancers is becoming increasingly clear. Imbalances in the gut microbial community, referred to as dysbiosis, are linked to increased risks for various forms of gastrointestinal cancers. Pathogens like Fusobacterium and Helicobacter pylori relate to the onset of esophageal and gastric cancers, respectively, while microbes such as Porphyromonas gingivalis and Clostridium species have been associated with a higher risk of pancreatic cancer. In colorectal cancer, bacteria such as Fusobacterium nucleatum are known to stimulate the growth of tumor cells and trigger cancer-promoting pathways. On the other hand, beneficial microbes like Bifidobacteria offer a protective effect, potentially inhibiting the development of gastrointestinal cancers. The potential for therapeutic interventions that manipulate the gut microbiome is substantial, including strategies to engineer anti-tumor metabolites and employ microbiota-based treatments. Despite the progress in understanding the influence of the microbiome on gastrointestinal cancers, significant challenges remain in identifying and understanding the precise contributions of specific microbial species and their metabolic products. This knowledge is essential for leveraging the role of the gut microbiome in the development of precise diagnostics and targeted therapies for gastrointestinal cancers.

Immunotherapy efficacy and toxicity: Reviewing the evidence behind patient implementable strategies

The use of immune checkpoint inhibitors (ICI) in cancer treatment is expanding, offering promising outcomes but with an important risk of immune-related adverse events (irAEs). These events, stemming from an overstimulated immune system attacking healthy cells, can necessitate immunosuppressant treatment, disrupt treatment courses, and impact patients' quality of life. The analysis of ICI efficacy data has led to a better understanding of the characteristics of responders. Similarly, we are gaining clearer insights into the characteristics of patients who develop irAEs, prompting an increasing emphasis on modifiable factors associated with irAE risk. These factors include lifestyle choices and the composition of the gut microbiome. Despite comprehensive reviews exploring the microbiome's role in therapy efficacy, understanding its connection with immune-related toxicity remains incomplete. While endeavours to identify predictive biomarkers continue, lifestyle modifications emerge as a promising avenue for enhancing treatment outcomes. This review consolidates the current evidence regarding the impact of the gut microbiome on irAE occurrence. Furthermore, it focuses on actionable strategies for mitigating these adverse events, elucidating the evidence supporting dietary adjustments, supplementation, medication management, and physical activity. With the expanding range of indications for ICI therapy, a significant proportion of oncology patients, including those in early disease stages, are now exposed to these treatments. Acknowledging the importance of averting irAEs in this context, our review offers timely insights crucial for addressing the evolving challenges associated with immunotherapy across diverse oncological settings.

Exploration of the role of EMC3‑AS1 as a potential diagnostic and prognostic indicator in liver cancer

The aim of the present study was to evaluate the diagnostic and prognostic significance of the long non-coding RNA (lncRNA) endoplasmic reticulum membrane protein complex subunit 3 antisense RNA 1 (EMC3-AS1) in liver cancer, and its impact on the proliferative and invasive capabilities of liver cancer cells. EMC3-AS1 expression in liver cancer was assessed using data from The Cancer Genome Atlas and three Gene Expression Omnibus datasets, and validated in clinical liver cancer samples using reverse transcription-quantitative PCR. The prognostic and diagnostic potentials of this lncRNA were evaluated using Kaplan-Meier and receiver operating characteristic analyses, respectively. The infiltration of immune cells and differential expression of immune checkpoints (ICs) between high- and low-EMC3-AS1 expression groups were investigated. Therapeutic correlation analyses were also undertaken to assess the impact of EMC3-AS1 in the treatment of liver cancer. In addition, in vitro experiments were conducted using small interfering RNA to knock down the expression of EMC3-AS1 in HepG2, Sk-Hep-1 and Huh-7 cells, and evaluate the effect on cell proliferation, colony formation and migration. The results revealed a significant upregulation of EMC3-AS1 expression in liver cancer tissues compared with that in adjacent normal tissues, which was associated with an unfavorable prognosis and demonstrated diagnostic effectiveness for patients with liver cancer. Furthermore, patients with high EMC3-AS1 expression exhibited increased levels of IC markers in comparison with those with low EMC3-AS1 expression. In addition, EMC3-AS1 was indicated to have clinical significance in the prediction of the response to immunotherapy and chemotherapy. Notably, the in vitro experiments demonstrated that the knockdown of EMC3-AS1 significantly hindered cell proliferation, colony formation and migration. Consequently, it was concluded that EMC3-AS1 is upregulated in liver cancer and serves as a prognostic indicator for unfavorable outcomes in patients with liver cancer. Additionally, targeting EMC3-AS1 through knockdown interventions showed potential in mitigating the ability of liver cancer cells to proliferate and migrate, which highlights its dual role as a biomarker and therapeutic target for liver cancer.

Phenotypes of Metabolic Dysfunction-Associated Steatotic Liver Disease-Associated Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) typically develops as a consequence of liver cirrhosis, but HCC epidemiology has evolved drastically in recent years. Metabolic dysfunction-associated steatotic liver disease (MASLD), including metabolic dysfunction-associated steatohepatitis, has emerged as the most common chronic liver disease worldwide and a leading cause of HCC. A substantial proportion of MASLD-associated HCC (MASLD-HCC) also can develop in patients without cirrhosis. The specific pathways that trigger carcinogenesis in this context are not elucidated completely, and recommendations for HCC surveillance in MASLD patients are challenging. In the era of precision medicine, it is critical to understand the processes that define the profiles of patients at increased risk of HCC in the MASLD setting, including cardiometabolic risk factors and the molecular targets that could be tackled effectively. Ideally, defining categories that encompass key pathophysiological features, associated with tailored diagnostic and treatment strategies, should facilitate the identification of specific MASLD-HCC phenotypes. In this review, we discuss MASLD-HCC, including its epidemiology and health care burden, the mechanistic data promoting MASLD, metabolic dysfunction-associated steatohepatitis, and MASLD-HCC. Its natural history, prognosis, and treatment are addressed specifically, as the role of metabolic phenotypes of MASLD-HCC as a potential strategy for risk stratification. The challenges in identifying high-risk patients and screening strategies also are discussed, as well as the potential approaches for MASLD-HCC prevention and treatment.

PPP1R14B-mediated phosphorylation enhances protein stability of RPS6KA1 to promote hepatocellular carcinoma tumorigenesis

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide with a poor clinical prognosis. Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an unidentified protein phosphatase 1 regulatory subunit that is associated with the occurrence and development of various cancers. Recently, PPP1R14B was found to contribute to paclitaxel resistance and cell progression in triple-negative breast cancer; however, the role of PPP1R14B in HCC is unknown. Here, we found that PPP1R14B was highly expressed in HCC tissues, which suggested a poor prognosis. Knockdown of PPP1R14B significantly inhibited the survival and tumorigenic ability of HCC cells, while overexpression of PPP1R14B had the opposite effects. Mechanistically, Ribosomal Protein S6 Kinase type 1(RPS6KA1) was identified as the target gene of PPP1R14B. PPP1R14B maintained the stability and phosphorylation of RPS6KA1, and positively regulated activation of the AKT/NF-κB pathway. Importantly, PPP1R14B-deficient tumor suppression could be partially restored by wild-type but not phosphorylated mutant RPS6KA1. Taken together, these findings shed light on the function and mechanism of PPP1R14B in HCC progression, indicating PPP1R14B is a promising molecular target for the treatment of HCC.

Mechanochemistry: Fundamental Principles and Applications

Mechanochemistry is an emerging research field at the interface of physics, mechanics, materials science, and chemistry. Complementary to traditional activation methods in chemistry, such as heat, electricity, and light, mechanochemistry focuses on the activation of chemical reactions by directly or indirectly applying mechanical forces. It has evolved as a powerful tool for controlling chemical reactions in solid state systems, sensing and responding to stresses in polymer materials, regulating interfacial adhesions, and stimulating biological processes. By combining theoretical approaches, simulations and experimental techniques, researchers have gained intricate insights into the mechanisms underlying mechanochemistry. In this review, the physical chemistry principles underpinning mechanochemistry are elucidated and a comprehensive overview of recent significant achievements in the discovery of mechanically responsive chemical processes is provided, with a particular emphasis on their applications in materials science. Additionally, The perspectives and insights into potential future directions for this exciting research field are offered.

Insights into the specific feature of the electrostatic recognition binding mechanism between BM2 and BM1: a molecular dynamics simulation study

Matrix protein 2 (M2) and matrix protein 1 (M1) of the influenza B virus are two important proteins, and the interactions between BM2 and BM1 play an important role in the process of virus assembly and replication. However, the interaction details between BM2 and BM1 are still unclear at the atomic level. Here, we constructed the BM2-BM1 complex system using homology modelling and molecular docking methods. Molecular dynamics (MD) simulations were used to illustrate the binding mechanism between BM2 and BM1. The results identify that the eight polar residues (E88B, E89B, H119BM1, E94B, R101BM1, K102BM1, R105BM1, and E104B) play an important role in stabilizing the binding through the formation of hydrogen bond networks and salt-bridge interactions at the binding interface. Furthermore, based on the simulation results and the experimental facts, the mutation experiments were designed to verify the influence of the mutation of residues both within and outside the effector domain. The mutations directly or indirectly disrupt interactions between polar residues, thus affecting viral assembly and replication. The results could help us understand the details of the interactions between BM2 and BM1 and provide useful information for the anti-influenza drug design.

The gut microbiota improves the efficacy of immune-checkpoint inhibitor immunotherapy against tumors: From association to cause and effect

Immune-checkpoint inhibitors (ICIs), including anti-PD-1/PD-L1 therapeutic antibodies, have markedly enhanced survival across numerous cancer types. However, the limited number of patients with durable benefits creates an urgent need to identify response biomarkers and to develop novel strategies so as to improve response. It is widely recognized that the gut microbiome is a key mediator in shaping immunity. Additionally, the gut microbiome shows significant potential in predicting the response to and enhancing the efficacy of ICI immunotherapy against cancer. Recent studies encompassing mechanistic analyses and clinical trials of microbiome-based therapy have shown a cause-and-effect relationship between the gut microbiome and the modulation of the ICI immunotherapeutic response, greatly contributing to the establishment of novel strategies that will improve response and overcome resistance to ICI treatment. In this review, we outline the current state of research advances and discuss the future directions of utilizing the gut microbiome to enhance the efficacy of ICI immunotherapy against tumors.

Editorial for the Special Issue on Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments

As the editor of the Special Issue on "Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments", I am delighted to present this collection of groundbreaking research that addresses the emerging needs and challenges in the field of IoT sensors and smart environments [...].

A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastases

PURPOSE

This study aimed to develop a tumor radiomics quality and quantity model (RQQM) based on preoperative enhanced CT to predict early recurrence after radical surgery for colorectal liver metastases (CRLM).

METHODS

A retrospective analysis was conducted on 282 cases from 3 centers. Clinical risk factors were examined using univariate and multivariate logistic regression (LR) to construct the clinical model. Radiomics features were extracted using the least absolute shrinkage and selection operator (LASSO) for dimensionality reduction. The LR learning algorithm was employed to construct the radiomics model, RQQM (radiomics-TBS), combined model (radiomics-clinical), clinical risk score (CRS) model and tumor burden score (TBS) model. Inter-model comparisons were made using area under the curve (AUC), decision curve analysis (DCA) and calibration curve. Log-rank tests assessed differences in disease-free survival (DFS) and overall survival (OS).

RESULTS

Clinical features screening identified CRS, KRAS/NRAS/BRAF and liver lobe distribution as risk factors. Radiomics model, RQQM, combined model demonstrated higher AUC values compared to CRS and TBS model in training, internal and external validation cohorts (Delong-test P < 0.05). RQQM outperformed the radiomics model, but was slightly inferior to the combined model. Survival curves revealed statistically significant differences in 1-year DFS and 3-year OS for the RQQM (P < 0.001).

CONCLUSIONS

RQQM integrates both "quality" (radiomics) and "quantity" (TBS). The radiomics model is superior to the TBS model and has a greater impact on patient prognosis. In the absence of clinical data, RQQM, relying solely on imaging data, shows an advantage in predicting early recurrence after radical surgery for CRLM.

Microbiota-induced plastic T cells enhance immune control of antigen-sharing tumors

Therapies that harness the immune system to target and eliminate tumor cells have revolutionized cancer care. Immune checkpoint blockade (ICB), which boosts the anti-tumor immune response by inhibiting negative regulators of T cell activation 1-3 , is remarkably successful in a subset of cancer patients, yet a significant proportion do not respond to treatment, emphasizing the need to understand factors influencing the therapeutic efficacy of ICB 4-9 . The gut microbiota, consisting of trillions of microorganisms residing in the gastrointestinal tract, has emerged as a critical determinant of immune function and response to cancer immunotherapy, with multiple studies demonstrating association of microbiota composition with clinical response 10-16 . However, a mechanistic understanding of how gut commensal bacteria influence the efficacy of ICB remains elusive. Here we utilized a gut commensal microorganism, segmented filamentous bacteria (SFB), which induces an antigen-specific Th17 cell effector program 17 , to investigate how colonization with it affects the efficacy of ICB in restraining distal growth of tumors sharing antigen with SFB. We find that anti-PD-1 treatment effectively inhibits the growth of implanted SFB antigen-expressing melanoma only if mice are colonized with SFB. Through T cell receptor clonal lineage tracing, fate mapping, and peptide-MHC tetramer staining, we identify tumor-associated SFB-specific Th1-like cells derived from the homeostatic Th17 cells induced by SFB colonization in the small intestine lamina propria. These gut-educated ex-Th17 cells produce high levels of the pro-inflammatory cytokines IFN-γ and TNF-α, and promote expansion and effector functions of CD8 + tumor-infiltrating cytotoxic lymphocytes, thereby controlling tumor growth. A better understanding of how distinct intestinal commensal microbes can promote T cell plasticity-dependent responses against antigen-sharing tumors may allow for the design of novel cancer immunotherapeutic strategies.

The potential role of Akkermansia muciniphila in liver health

Akkermansia muciniphila (A. muciniphila) is a 'star strain' that has attracted much attention in recent years. A. muciniphila can effectively regulate host metabolism, significantly affect host immune function, and play an important role in balancing host health and disease. As one of the organs most closely related to the gut (the two can communicate through the hepatic portal vein and bile duct system), liver is widely affected by intestinal microorganisms. A growing body of evidence suggests that A. muciniphila may alleviate liver-related diseases by improving the intestinal barrier, energy metabolism and regulating inflammation through its protein components and metabolites. This paper systematically reviews the key roles of A. muciniphila and its derivatives in maintaining liver health and improving liver disease.

Identification and experimental verification of a biomarker by combining the unfolded protein response with the immune cells in colon cancer

BACKGROUND

The unfolded protein response (UPR) is associated with immune cells that regulate the biological behavior of tumors. This article aims to combine UPR-associated genes with immune cells to find a prognostic marker and to verify its connection to the UPR.

METHODS

Univariate cox analysis was used to screen prognostically relevant UPRs and further screened for key UPRs among them by machine learning. ssGSEA was used to calculate immune cell abundance. Univariate cox analysis was used to screen for prognostically relevant immune cells. Multivariate cox analysis was used to calculate UPR_score and Tumor Immune Microenvironment score (TIME_score). WGCNA was used to screen UPR-Immune-related (UI-related) genes. Consensus clustering analysis was used to classify patients into molecular subtype. Based on the UI-related genes, we classified colon adenocarcinoma (COAD) samples by cluster analysis. Single-cell analysis was used to analyze the role of UI-related genes. We detected the function of TIMP1 by cell counting and transwell. Immunoblotting was used to detect whether TIMP1 was regulated by key UPR genes.

RESULTS

Combined UPR-related genes and immune cells can determine the prognosis of COAD patients. Cluster analysis showed that UI-related genes were associated with clinical features of COAD. Single-cell analysis revealed that UI-related genes may act through stromal cells. We defined three key UI-related genes by machine learning algorithms. Finally, we found that TIMP1, regulated by key genes of UPR, promoted colon cancer proliferation and metastasis.

CONCLUSIONS

We found that TIMP1 was a prognostic marker and experimentally confirmed that TIMP1 was regulated by key genes of UPR.

Gut Microbiota, Deranged Immunity, and Hepatocellular Carcinoma

BACKGROUND

Liver cancer, particularly hepatocellular carcinoma (HCC), is a significant gastrointestinal disease with a mortality rate as high as nearly 80% within five years. The disease's pathophysiology involves deranged immune responses and bile acid metabolism, with the gut microbiota (GM) playing a crucial role. Recent research highlights the potential of GM in influencing HCC treatment outcomes, especially regarding immune checkpoint inhibitors (ICIs). However, few patients currently benefit from ICIs due to a lack of effective response biomarkers.

AIMS AND METHODS

This review aimed to explore the literature on HCC treatment issues, focusing on immune response, bile acid metabolism, and GM dysbiosis. This review included studies from PubMed, Medline, and major gastroenterology and hepatology meetings, using keywords like gut microbiota, immune system, liver cancer, and checkpoint inhibitors.

RESULTS

GM dysbiosis significantly impacts immune response and bile acid metabolism, making it a promising biomarker for ICI response. Modulating GM can enhance ICI treatment efficacy, although more research is needed to confirm its direct therapeutic benefits for HCC.

CONCLUSIONS

GM dysbiosis is integral to liver cancer pathogenesis and treatment response. Its modulation offers promising therapeutic avenues for improving HCC prognosis and response to immunotherapy.

Monocyte/Macrophage-Mediated Transport of Dual-Drug ZIF Nanoplatforms Synergized with Programmed Cell Death Protein-1 Inhibitor Against Microsatellite-Stable Colorectal Cancer

Microsatellite-stable colorectal cancer (MSS-CRC) exhibits resistance to programmed cell death protein-1 (PD-1) therapy. Improving the infiltration and tumor recognition of cytotoxic T-lymphocytes (CTLs) is a promising strategy, but it encounters huge challenges from drug delivery and mechanisms aspects. Here, a zeolitic imidazolate framework (ZIF) coated with apoptotic body membranes derived from MSS-CRC cells is engineered for the co-delivery of ginsenoside Rg1 (Rg1) and atractylenolide-I (Att) to MSS-CRC, named as Ab@Rg1/Att-ZIF. This system is selectively engulfed by Ly-6C+ monocytes during blood circulation and utilizes a "hitchhiking" mechanism to migrate toward the core of MSS-CRC. Ab@Rg1/Att-ZIF undergoes rapid disassembly in the tumor, released Rg1 promotes the processing and transportation of tumor antigens in dendritic cells (DCs), enhancing their maturation. Meanwhile, Att enhances the activity of the 26S proteasome complex in tumor cells, leading to increased expression of major histocompatibility complex class-I (MHC-I). These coordinated actions enhance the infiltration and recognition of CTLs in the center of MSS-CRC, significantly improving the tumor inhibition of PD-1 treatment from ≈5% to ≈69%. This innovative design, involving inflammation-guided precise drug co-delivery and a rational combination, achieves synergistic engineering of the tumor microenvironment, providing a novel strategy for successful PD-1 treatment of MSS-CRC.

Genetically engineered nanomodulators elicit potent immunity against cancer stem cells by checkpoint blockade and hypoxia relief

Rapid development of checkpoint inhibitors has provided significant breakthroughs for cancer stem cell (CSC) therapy, while the therapeutic efficacy is restricted by hypoxia-mediated tumor immune evasion, especially hypoxia-induced CD47 overexpression in CSCs. Herein, we developed a genetically engineered CSC membrane-coated hollow manganese dioxide (hMnO2@gCMs) to elicit robust antitumor immunity by blocking CD47 and alleviating hypoxia to ultimately achieve the eradication of CSCs. The hMnO2 core effectively alleviated tumor hypoxia by inducing decomposition of tumor endogenous H2O2, thus suppressing the CSCs and reducing the expression of CD47. Cooperating with hypoxia relief-induced downregulation of CD47, the overexpressed SIRPα on gCM shell efficiently blocked the CD47-SIRPα "don't eat me" pathway, synergistically eliciting robust antitumor-mediated immune responses. In a B16F10-CSC bearing melanoma mouse model, the hMnO2@gCMs showed an enhanced therapeutic effect in eradicating CSCs and inhibiting tumor growth. Our work presents a simple, safe, and robust platform for CSC eradication and cancer immunotherapy.

Microecological regulation in HCC therapy: Gut microbiome enhances ICI treatment

The exploration of the complex mechanisms of cancer immunotherapy is rapidly evolving worldwide, and our focus is on the interaction of hepatocellular carcinoma (HCC) with immune checkpoint inhibitors (ICIs), particularly as it relates to the regulatory role of the gut microbiome. An important basis for the induction of immune responses in HCC is the presence of specific anti-tumor cells that can be activated and reinforced by ICIs, which is why the application of ICIs results in sustained tumor response rates in the majority of HCC patients. However, mechanisms of acquired resistance to immunotherapy in unresectable HCC result in no long-term benefit for some patients. The significant heterogeneity of inter-individual differences in the gut microbiome in response to treatment with ICIs makes it possible to target modulation of specific gut microbes to assist in augmenting checkpoint blockade therapies in HCC. This review focuses on the complex relationship between the gut microbiome, host immunity, and HCC, and emphasizes that manipulating the gut microbiome to improve response rates to cancer ICI therapy is a clinical strategy with unlimited potential.

Leveraging oncovirus-derived antigen against the viral malignancies in adoptive cell therapies

Adoptive cell therapies (ACTs) have revolutionized cancer immunotherapy, prompting exploration into their application against oncoviruses. Oncoviruses such as human papillomavirus (HPV), hepatitis B virus (HBV), hepatitis C virus (HCV), and Epstein-Barr virus (EBV) contribute significantly (12-25%) to human malignancies through direct or indirect oncogenic mechanisms. These viruses persistently or latently infect cells, disrupt cellular homeostasis and pathways, challenging current antiviral treatment paradigms. Moreover, viral infections pose additional risks in the setting of long-term cancer therapy and lead to morbidity and mortality. Virally encoded oncoproteins, which are tumor-restricted, immunologically foreign, and even uniformly expressed, represent promising targets for patient-tailored ACTs. This review elucidates the rationale for leveraging viral antigen-specific ACTs in combating viral-associated malignancies. On this basis, ongoing preclinical studies consolidate our understanding of harnessing ACTs against viral malignancies, underscoring their potential to eradicate viruses implicated in cancer progression. Furthermore, we scrutinize the current landscape of clinical trials focusing on virus-specific ACTs and discuss their implications for therapeutic advancement.

Novel molecular subtypes of METex14 non-small cell lung cancer with distinct biological and clinical significance

Not all MET exon 14 skipping (METex14) NSCLC patients benefited from MET inhibitors. We hypothesized an inter-tumoral heterogeneity in METex14 NSCLC. Investigations at genomic and transcriptomic level were conducted in METex14 NSCLC samples from stage I-III and recurrent/metastatic patients as discovery and validation cohort. Four molecular subtypes were discovered. MET-Driven subtype, with the worst prognosis, displayed MET overexpression, enrichment of MET-related pathways, and higher infiltration of fibroblast and regulatory T cells. Immune-Activated subtype having the most idea long-term survival, had higher tertiary lymphoid structures, spatial co-option of PD-L1+ cancer cells, and GZMK+ CD8+ T cell. FGFR- and Bypass-Activated subtypes displayed FGFR2 overexpression and enrichments of multiple oncogenic pathways respectively. In the validation cohort, patients with MET-Driven subtype had better response to MET inhibitors than those with MET overexpression. Thus, molecular subtypes of METex14 NSCLC with distinct biological and clinical significance may indicate more precise therapeutic strategies for METex14 NSCLC patients.

Microbiota composition effect on immunotherapy outcomes in colorectal cancer patients: A systematic review

BACKGROUND

Immune checkpoint inhibitors (ICIs) have emerged as an effective treatment for colorectal cancer (CRC). Studies indicate that the composition of gut microbiota could potentially serve as a biomarker for predicting the clinical effectiveness of immune checkpoint inhibitors.

METHODS

Following PRISMA guidelines, the review was conducted after registering the protocol with PROSPERO. A comprehensive literature search was carried out across five databases: PubMed, Scopus, Web of Science, Embase, and Cochrane Library. Assessment tools from the National Institutes of Health (NIH) were used to gauge the quality of the studies.

RESULTS

A total of 5,132 papers were identified, and three studies and one conference abstract published between 2017-2022 met the inclusion criteria and were summarized in a descriptive synthesis table. These four studies were in accord with the following findings, four main phyla, Firmicutes, Bacteroidata, Actinobacteria, and Verrucomicrobiota were associated with CRC patients' clinical response toward ICIs treatment. Ruminococcaceae was predominantly related to CRC patients responding to therapy, while the Micrococcaceae family was more common among the non-responders. Bacterial taxa such as Faecalibacterium and Prevotellaceae were associated with better responses to ICIs and could be predictive biomarkers. The signature of fecal microbiota with Akkermansia muciniphila and Eubacterium rectale enrichment, and Rothia mucilaginosa depletion could independently predict better response to ICIs in patients with CRC.

CONCLUSION

The findings have brought attention to the notable differences in terms of richness and composition of microbiota between patients who responded positively to the treatment and those who did not. Bacterial species and families, such as Faecalibacterium, Bifidobacterium, Lachnospiraceae, Akkermansia sp., Ruminococcaceae, and Prevotellaceae, have consistently surfaced as potential indicators of immunotherapeutic responses. Furthermore, this review also emphasizes the need for additional comprehensive, multi-center studies with larger sample sizes to validate reported microbiota and expand our understanding of the role of gut microbiota in CRC ICIs therapy. PROSPERO ID: CRD42021277691.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceaebacterium and Blautiawexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

Role of Gut Microbial Metabolites in the Pathogenesis of Primary Liver Cancers

Hepatobiliary malignancies, which include hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are the sixth most common cancers and the third leading cause of cancer-related death worldwide. Hepatic carcinogenesis is highly stimulated by chronic inflammation, defined as fibrosis deposition, and an aberrant imbalance between liver necrosis and nodular regeneration. In this context, the gut-liver axis and gut microbiota have demonstrated a critical role in the pathogenesis of HCC, as dysbiosis and altered intestinal permeability promote bacterial translocation, leading to chronic liver inflammation and tumorigenesis through several pathways. A few data exist on the role of the gut microbiota or bacteria resident in the biliary tract in the pathogenesis of CCA, and some microbial metabolites, such as choline and bile acids, seem to show an association. In this review, we analyze the impact of the gut microbiota and its metabolites on HCC and CCA development and the role of gut dysbiosis as a biomarker of hepatobiliary cancer risk and of response during anti-tumor therapy. We also discuss the future application of gut microbiota in hepatobiliary cancer management.

Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters

Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8-12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.

PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives

The advent of PD1/PD-L1 inhibitors has significantly transformed the therapeutic landscape for clear cell renal cell carcinoma (ccRCC). This review provides an in-depth analysis of the biological functions and regulatory mechanisms of PD1 and PD-L1 in ccRCC, emphasizing their role in tumor immune evasion. We comprehensively evaluate the clinical efficacy and safety profiles of PD1/PD-L1 inhibitors, such as Nivolumab and Pembrolizumab, through a critical examination of recent clinical trial data. Furthermore, we discuss the challenges posed by resistance mechanisms to these therapies and potential strategies to overcome them. We also explores the synergistic potential of combination therapies, integrating PD1/PD-L1 inhibitors with other immunotherapies, targeted therapies, and conventional modalities such as chemotherapy and radiotherapy. In addition, we examine emerging predictive biomarkers for response to PD1/PD-L1 blockade and biomarkers indicative of resistance, providing a foundation for personalized therapeutic approaches. Finally, we outline future research directions, highlighting the need for novel therapeutic strategies, deeper mechanistic insights, and the development of individualized treatment regimens. Our work summarizes the latest knowledge and progress in this field, aiming to provide a valuable reference for improving clinical efficacy and guiding future research on the application of PD1/PD-L1 inhibitors in ccRCC.

An update to experimental and clinical aspects of tumor-associated macrophages in cancer development: hopes and pitfalls

Tumor-associated macrophages (TAMs) represent one of the most abundant tumor-infiltrating stromal cells, and their normal function in tumor microenvironment (TME) is to suppress tumor cells by producing cytokines which trigger both direct cell cytotoxicity and antibody-mediated immune response. However, upon prolonged exposure to TME, the classical function of these so-called M1-type TAMs can be converted to another type, "M2-type," which are recruited by tumor cells so that they promote tumor growth and metastasis. This is the reason why the accumulation of TAMs in TME is correlated with poor prognosis in cancer patients. Both M1- and M2-types have high degree of plasticity, and M2-type cells can be reprogrammed to M1-type for therapeutic purposes. This characteristic introduces TAMs as promising target for developing novel cancer treatments. In addition, inhibition of M2-type cells and blocking their recruitment in TME, as well as their depletion by inducing apoptosis, are other approaches for effective immunotherapy of cancer. In this review, we summarize the potential of TAMs to be targeted for cancer immunotherapy and provide an up-to-date about novel strategies for targeting TAMs.

Optimizing storage on fog computing edge servers: A recent algorithm design with minimal interference

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field.

Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.

M2 macrophages promote PD-L1 expression in triple-negative breast cancer via secreting CXCL1

BACKGROUND

M2 macrophages are known to play a significant role in the progression of triple-negative breast cancer (TNBC) by creating an immunosuppressive microenvironment. The aim of this study is to investigate the impact of M2 macrophages on TNBC and their correlation with programmed death-ligand 1 (PD-L1) expression.

METHODS

We employed a co-culture system to analyze the role of the mutual regulation of M2 macrophages and TNBC cells. Employing a multifaceted approach, including bioinformatics analysis, Western blotting, flow cytometry analysis, ELISA, qRT-PCR, lentivirus infection, mouse models, and IHC, we aimed to elucidate the influence and mechanism of M2 macrophages on PD-L1 expression.

RESULTS

The results showed a substantial infiltration of M2 macrophages in TNBC tissue, which demonstrated a positive correlation with PD-L1 expression. CXCL1 exhibited abnormally high expression in M2 macrophages and enhanced the expression of PD-L1 in TNBC cells. Notably, silencing CXCL1 or its receptor CXCR2 inhibited M2 macrophages-induced expression of PD-L1. Mechanistically, CXCL1 derived from M2 macrophages binding to CXCR2 activated the PI3K/AKT/NF-κB signaling pathway, resulting in increased PD-L1 expression in TNBC.

CONCLUSION

Broadly speaking, these results provide evidence for the immunosuppressive role of M2 macrophages and CXCL1 in TNBC cells, indicating their potential as therapeutic biomarkers.

Understanding gut dysbiosis for hepatocellular carcinoma diagnosis and treatment

The gut microbiome can play a crucial role in hepatocellular carcinoma (HCC) progression through the enterohepatic circulation, primarily acting via metabolic reprogramming and alterations in the hepatic immune microenvironment triggered by microbe-associated molecular patterns (MAMPs), metabolites, and fungi. In addition, the gut microbiome shows potential as a biomarker for early HCC diagnosis and for assessing the efficacy of immunotherapy in unresectable HCC. This review examines how gut microbiota dysbiosis, with varied functional profiles, contributes to HCCs of different etiologies. We discuss therapeutic strategies to modulate the gut microbiome including diets, antibiotics, probiotics, fecal microbiota transplantation, and nano-delivery systems, and underscore their potential as an adjunctive treatment modality for HCC.

Hepatitis B virus X protein promotes tumor glycolysis by downregulating lncRNA OIP5-AS1/HKDC1 in HCC

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide, with Hepatitis B virus (HBV) infection being the leading cause. This study aims to investigate the role of HBV in HCC pathogenesis involving glucose metabolism. Long non-coding RNA (lncRNA) OIP5-AS1 was significantly downregulated in HBV-positive HCC patients, and its low expression indicated a poor prognosis. This lncRNA was primarily localized in the cytoplasm, acting as a tumor suppressor. HBV protein X (HBx) repressed OIP5-AS1 expression by inhibiting a ligand-activated transcriptional factor peroxisome proliferator-activated receptor α (PPARα). Furthermore, mechanistic studies revealed that OIP5-AS1 inhibited tumor growth by suppressing Hexokinase domain component 1 (HKDC1)-mediated glycolysis. The expression of HKDC1 could be enhanced by transcriptional factor sterol regulatory element-binding protein 1 (SREBP1). OIP5-AS1 facilitated the ubiquitination and degradation of SREBP1 to suppress HKDC1 transcription, which inhibited glycolysis. The results suggest that lncRNA OIP5-AS1 plays an anti-oncogenic role in HBV-positive HCC via the HBx/OIP5-AS1/HKDC1 axis, providing a promising diagnostic marker and therapeutic target for HBV-positive HCC patients.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

A new strategy for overcoming drug resistance in liver cancer: Epigenetic regulation

Drug resistance in hepatocellular carcinoma has posed significant obstacles to effective treatment. Recent evidence indicates that, in addition to traditional gene mutations, epigenetic recoding plays a crucial role in HCC drug resistance. Unlike irreversible gene mutations, epigenetic changes are reversible, offering a promising avenue for preventing and overcoming drug resistance in liver cancer. This review focuses on various epigenetic modifications relevant to drug resistance in HCC and their underlying mechanisms. Additionally, we introduce current clinical epigenetic drugs and clinical trials of these drugs as regulators of drug resistance in other solid tumors. Although there is no clinical study to prevent the occurrence of drug resistance in liver cancer, the development of liquid biopsy and other technologies has provided a bridge to achieve this goal.

Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database

OBJECTIVE

This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC).

METHODS

Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients.

RESULTS

51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611.

CONCLUSION

In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.

Harnessing CD8+ T cell dynamics in hepatitis B virus-associated liver diseases: Insights, therapies and future directions

Hepatitis B virus (HBV) infection playsa significant role in the etiology and progression of liver-relatedpathologies, encompassing chronic hepatitis, fibrosis, cirrhosis, and eventual hepatocellularcarcinoma (HCC). Notably, HBV infection stands as the primary etiologicalfactor driving the development of HCC. Given the significant contribution ofHBV infection to liver diseases, a comprehensive understanding of immunedynamics in the liver microenvironment, spanning chronic HBV infection,fibrosis, cirrhosis, and HCC, is essential. In this review, we focused on thefunctional alterations of CD8+ T cells within the pathogenic livermicroenvironment from HBV infection to HCC. We thoroughly reviewed the roles ofhypoxia, acidic pH, metabolic reprogramming, amino acid deficiency, inhibitory checkpointmolecules, immunosuppressive cytokines, and the gut-liver communication in shapingthe dysfunction of CD8+ T cells in the liver microenvironment. Thesefactors significantly impact the clinical prognosis. Furthermore, we comprehensivelyreviewed CD8+ T cell-based therapy strategies for liver diseases,encompassing HBV infection, fibrosis, cirrhosis, and HCC. Strategies includeimmune checkpoint blockades, metabolic T-cell targeting therapy, therapeuticT-cell vaccination, and adoptive transfer of genetically engineered CD8+ T cells, along with the combined usage of programmed cell death protein-1/programmeddeath ligand-1 (PD-1/PD-L1) inhibitors with mitochondria-targeted antioxidants.Given that targeting CD8+ T cells at various stages of hepatitis Bvirus-induced hepatocellular carcinoma (HBV + HCC) shows promise, we reviewedthe ongoing need for research to elucidate the complex interplay between CD8+ T cells and the liver microenvironment in the progression of HBV infection toHCC. We also discussed personalized treatment regimens, combining therapeuticstrategies and harnessing gut microbiota modulation, which holds potential forenhanced clinical benefits. In conclusion, this review delves into the immunedynamics of CD8+ T cells, microenvironment changes, and therapeuticstrategies within the liver during chronic HBV infection, HCC progression, andrelated liver diseases.

Inflammatory Response in the Pathogenesis and Treatment of Hepatocellular Carcinoma: A Double-Edged Weapon

Hepatocellular carcinoma (HCC) is the most frequent among primary liver tumors (90%) and one of the main causes of cancer-related death. It develops usually in a chronically inflamed environment, ranging from compensatory parenchymal regeneration to fibrosis and cirrhosis: carcinogenesis can potentially happen in each of these stages. Inflammation determined by chronic viral infection (hepatitis B, hepatitis C, and hepatitis delta viruses) represents an important risk factor for HCC etiology through both viral direct damage and immune-related mechanisms. The deregulation of the physiological liver immunological network determined by viral infection can lead to carcinogenesis. The recent introduction of immunotherapy as the gold-standard first-line treatment for HCC highlights the role of the immune system and inflammation as a double-edged weapon in both HCC carcinogenesis and treatment. In this review we highlight how the inflammation is the key for the hepatocarcinogenesis in viral, alcohol and metabolic liver diseases.

Challenges and Opportunities for Precision Surgery for Colorectal Liver Metastases

The incidence of colorectal cancer and colorectal liver metastases (CRLM) is increasing globally due to an interaction of environmental and genetic factors. A minority of patients with CRLM have surgically resectable disease, but for those who have resection as part of multimodal therapy for their disease, long-term survival has been shown. Precision surgery-the idea of careful patient selection and targeting of surgical intervention, such that treatments shown to be proven to benefit on a population level are the optimal treatment for each individual patient-is the new paradigm of care. Key to this is the understanding of tumour molecular biology and clinically relevant mutations, such as KRAS, BRAF, and microsatellite instability (MSI), which can predict poorer overall outcomes and a poorer response to systemic therapy. The emergence of immunotherapy and hepatic artery infusion (HAI) pumps show potential to convert previously unresectable disease to resectable disease, in addition to established systemic and locoregional therapies, but the surgeon must be wary of poor-quality livers and the spectre of post-hepatectomy liver failure (PHLF). Volume modulation, a cornerstone of hepatic surgery for a generation, has been given a shot in the arm with the advent of liver venous depletion (LVD) ensuring significantly more hypertrophy of the future liver remnant (FLR). The optimal timing of liver resection for those patients with synchronous disease is yet to be truly established, but evidence would suggest that those patients requiring complex colorectal surgery and major liver resection are best served with a staged approach. In the operating room, parenchyma-preserving minimally invasive surgery (MIS) can dramatically reduce the surgical insult to the patient and lead to better perioperative outcomes, with quicker return to function.