Oral squamous cell carcinoma(OSCC)is the most common head and neck malignancy worldwide,accounting for more than 90%of all oral cancers,and is characterized by high invasiveness and poor long-term prognosis.Its etiolo...Oral squamous cell carcinoma(OSCC)is the most common head and neck malignancy worldwide,accounting for more than 90%of all oral cancers,and is characterized by high invasiveness and poor long-term prognosis.Its etiology is multifactorial,involving tobacco use,alcohol consumption,and human papillomavirus(HPV)infection.Oral leukoplakia and erythroplakia are the main precancerous lesions lesions,with oral leukoplakia being the most common.Both OSCC and premalignant lesions are closely associated with aberrant activation of multiple signaling pathways.Post-translational modifications(such as ubiquitination and deubiquitination)play key roles in regulating these pathways by controlling protein stability and activity.Growing evidence indicates that dysregulated ubiquitination/deubiquitination can mediate OSCC initiation and progression via aberrant activation of signaling pathways.The ubiquitination/deubiquitination process mainly involves E3 ligases(E3s)that catalyze substrate ubiquitination,deubiquitinating enzymes(DUBs)that remove ubiquitin chains,and the 26S proteasome complex that degrades ubiquitinated substrates.Abnormal expression or mutation of E3s and DUBs can lead to altered stability of critical tumorrelated proteins,thereby driving OSCC initiation and progression.Therefore,understanding the aberrantly activated signaling pathways in OSCC and the ubiquitination/deubiquitination mechanisms within these pathways will help elucidate the molecular mechanisms and improve OSCC treatment by targeting relevant components.Here,we summarize four aberrantly activated signaling pathways in OSCC―the PI3K/AKT/mTOR pathway,Wnt/β-catenin pathway,Hippo pathway,and canonical NF-κB pathway―and systematically review the regulatory mechanisms of ubiquitination/deubiquitination within these pathways,along with potential drug targets.PI3K/AKT/mTOR pathway is aberrantly activated in approximately 70%of OSCC cases.It is modulated by E3s(e.g.,FBXW7 and NEDD4)and DUBs(e.g.,USP7 and USP10):FBXW7 and USP10 inhibit signaling,while NEDD4 and USP7 potentiate it.Aberrant activation of the Wnt/β-catenin pathway leads toβ-catenin nuclear translocation and induction of cell proliferation.This pathway is modulated by E3s(e.g.,c-Cbl and RNF43)and DUBs(e.g.,USP9X and USP20):c-Cbl and RNF43 inhibit signaling,while USP9X and USP20 potentiate it.Hippo pathway inactivation permits YAP/TAZ to enter the nucleus and promotes cancer cell metastasis.This pathway is modulated by E3s(e.g.,CRL4^(DCAF1) and SIAH2)and DUBs(e.g.,USP1 and USP21):CRL4^(DCAF1) and SIAH2 inhibit signaling,while USP1 and USP21 potentiate it.Persistent activation of the canonical NF-κB pathway is associated with an inflammatory microenvironment and chemotherapy resistance.This pathway is modulated by E3s(e.g.,TRAF6 and LUBAC)and DUBs(e.g.,A20 and CYLD):A20 and CYLD inhibit signaling,while TRAF6 and LUBAC potentiate it.Targeting these E3s and DUBs provides directions for OSCC drug research.Small-molecule inhibitors such as YCH2823(a USP7 inhibitor),GSK2643943A(a USP20 inhibitor),and HOIPIN-8(a LUBAC inhibitor)have shown promising antitumor activity in preclinical models;PROTAC molecules,by binding to surface sites of target proteins and recruiting E3s,achieve targeted ubiquitination and degradation of proteins insensitive to small-molecule inhibitors,for example,PU7-1-mediated USP7 degradation,offering new strategies to overcome traditional drug limitations.Currently,NX-1607(a Cbl-b inhibitor)has entered phase I clinical trials,with preliminary results confirming its safety and antitumor activity.Future research on aberrant E3s and DUBs in OSCC and the development of highly specific inhibitors will be of great significance for OSCC precision therapy.展开更多
Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accura...Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.展开更多
Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predic...Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8^(+)T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS 2,and TNFRSF1B was constructed.The risk score model was well validated through an independent external validation cohort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P<0.0001)and lower CD8^(+)T cells infiltration(P<0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P<0.01).Drug sensitivity analysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene model was verified by immunohistochemistry.In summary,the establishment and validation of a CD8^(+)T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.展开更多
文摘Oral squamous cell carcinoma(OSCC)is the most common head and neck malignancy worldwide,accounting for more than 90%of all oral cancers,and is characterized by high invasiveness and poor long-term prognosis.Its etiology is multifactorial,involving tobacco use,alcohol consumption,and human papillomavirus(HPV)infection.Oral leukoplakia and erythroplakia are the main precancerous lesions lesions,with oral leukoplakia being the most common.Both OSCC and premalignant lesions are closely associated with aberrant activation of multiple signaling pathways.Post-translational modifications(such as ubiquitination and deubiquitination)play key roles in regulating these pathways by controlling protein stability and activity.Growing evidence indicates that dysregulated ubiquitination/deubiquitination can mediate OSCC initiation and progression via aberrant activation of signaling pathways.The ubiquitination/deubiquitination process mainly involves E3 ligases(E3s)that catalyze substrate ubiquitination,deubiquitinating enzymes(DUBs)that remove ubiquitin chains,and the 26S proteasome complex that degrades ubiquitinated substrates.Abnormal expression or mutation of E3s and DUBs can lead to altered stability of critical tumorrelated proteins,thereby driving OSCC initiation and progression.Therefore,understanding the aberrantly activated signaling pathways in OSCC and the ubiquitination/deubiquitination mechanisms within these pathways will help elucidate the molecular mechanisms and improve OSCC treatment by targeting relevant components.Here,we summarize four aberrantly activated signaling pathways in OSCC―the PI3K/AKT/mTOR pathway,Wnt/β-catenin pathway,Hippo pathway,and canonical NF-κB pathway―and systematically review the regulatory mechanisms of ubiquitination/deubiquitination within these pathways,along with potential drug targets.PI3K/AKT/mTOR pathway is aberrantly activated in approximately 70%of OSCC cases.It is modulated by E3s(e.g.,FBXW7 and NEDD4)and DUBs(e.g.,USP7 and USP10):FBXW7 and USP10 inhibit signaling,while NEDD4 and USP7 potentiate it.Aberrant activation of the Wnt/β-catenin pathway leads toβ-catenin nuclear translocation and induction of cell proliferation.This pathway is modulated by E3s(e.g.,c-Cbl and RNF43)and DUBs(e.g.,USP9X and USP20):c-Cbl and RNF43 inhibit signaling,while USP9X and USP20 potentiate it.Hippo pathway inactivation permits YAP/TAZ to enter the nucleus and promotes cancer cell metastasis.This pathway is modulated by E3s(e.g.,CRL4^(DCAF1) and SIAH2)and DUBs(e.g.,USP1 and USP21):CRL4^(DCAF1) and SIAH2 inhibit signaling,while USP1 and USP21 potentiate it.Persistent activation of the canonical NF-κB pathway is associated with an inflammatory microenvironment and chemotherapy resistance.This pathway is modulated by E3s(e.g.,TRAF6 and LUBAC)and DUBs(e.g.,A20 and CYLD):A20 and CYLD inhibit signaling,while TRAF6 and LUBAC potentiate it.Targeting these E3s and DUBs provides directions for OSCC drug research.Small-molecule inhibitors such as YCH2823(a USP7 inhibitor),GSK2643943A(a USP20 inhibitor),and HOIPIN-8(a LUBAC inhibitor)have shown promising antitumor activity in preclinical models;PROTAC molecules,by binding to surface sites of target proteins and recruiting E3s,achieve targeted ubiquitination and degradation of proteins insensitive to small-molecule inhibitors,for example,PU7-1-mediated USP7 degradation,offering new strategies to overcome traditional drug limitations.Currently,NX-1607(a Cbl-b inhibitor)has entered phase I clinical trials,with preliminary results confirming its safety and antitumor activity.Future research on aberrant E3s and DUBs in OSCC and the development of highly specific inhibitors will be of great significance for OSCC precision therapy.
文摘Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.
基金国家自然科学基金项目(No.81902513)山西省应用基础研究计划项目(No.202303021211114 and 202103021224228)山西省高等教育百亿工程“科技引导”专项(No.BYJL047)资助。
文摘Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8^(+)T cell immune infiltration and immune suppression.We constructed a CD8^(+)T cells related risk score model to predict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8^(+)T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS 2,and TNFRSF1B was constructed.The risk score model was well validated through an independent external validation cohort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P<0.0001)and lower CD8^(+)T cells infiltration(P<0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P<0.01).Drug sensitivity analysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene model was verified by immunohistochemistry.In summary,the establishment and validation of a CD8^(+)T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.
