Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage str...Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage strategies in a graph-frequency domain.However,they seldom consider the image attributes in their graph-filtering procedure.Consequently,the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods.To fully exploit the image attributes,we propose a guided intra-patch smoothing AWGF(AWGF-GPS)method for single-image denoising.Unlike AWGF,which employs graph topology on patches,AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch.This operation forces the restored pixels to smoothly evolve in local areas,where both intra-and inter-patch relationships of the image are utilized during patch restoration.Meanwhile,a guided-patch regularizer is incorporated into AWGF-GPS.The guided patch is obtained in advance using a maximum-a-posteriori probability estimator.Because the guided patch is considered as a sketch of a denoised patch,AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch.Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images.It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods.In particular,it has the advantage of managing images with significant noise.展开更多
Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based mar...Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma(ccRCC).Methods:The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC pa-tients were correspondingly deregulated in ccRCC tissues.We adopted a two-step strategy,including Lasso and bootstrapping,to construct a novel risk stratification system termed the TDERS(Tumor-Derived Exosome-Related Risk Score).During the testing and validation phases,we leveraged multiple external datasets containing over 2000 RCC cases from eight cohorts and one inhouse cohort to evaluate the accuracy of the TDERS.In addition,enrichment analysis,immune infiltration signatures,mutation landscape and therapy sensitivity between the high and low TDERS groups were compared.Finally,the impact of TDERS on the tumor microenvironment(TME)was also analysed in our single-cell datasets.Results:TDERS consisted of 12 mRNAs deregulated in both exosomes and tissues from patients with ccRCC.TDERS achieved satisfactory performance in both prognosis and immune checkpoint inhibitor(ICI)response across all ccRCC cohorts and other pathological types,since the average area under the curve(AUC)to predict 5-year overall survival(OS)was larger than 0.8 across the four cohorts.Patients in the TDERS high group were resistant to ICIs,while mercaptopurine might function as a promising agent for those patients.Patients with a high TDERS were characterized by coagulation and hypoxia,which induced hampered tumor antigen presentation and relative resistance to ICIs.In addition,single cells from 12 advanced samples validated this phenomenon since the interaction between dendritic cells and macrophages was limited.Finally,PLOD2,which is highly expressed in fibro-and epi-tissue,could be a potential therapeutic target for ccRCC patients since inhibiting PLOD2 altered the malignant phenotype of ccRCC in vitro.Conclusion:As a novel,non-invasive,and repeatable monitoring tool,the TDERS could work as a robust risk stratification system for patients with ccRCC and precisely inform treatment decisions about ICI therapy.展开更多
Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidpto...Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.展开更多
Background:Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management.There are limited biomarkers to predict the metastatic probability of renal cancer,especially in the early-stag...Background:Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management.There are limited biomarkers to predict the metastatic probability of renal cancer,especially in the early-stage subgroup.Here,our study applied robust machine-learning algorithms to identify metastatic and recurrence-related signatures across multiple renal cancer cohorts,which reached high accuracy in both training and testing cohorts.Methods:Clear cell renal cell carcinoma(ccRCC)patients with primary or metastatic site sequencing information from eight cohorts,including one outhouse cohort,were enrolled in this study.Three robust machine-learning algorithms were applied to identify metastatic signatures.Then,two distinct metastatic-related subtypes were identified and verified;matrix remodeling associated 5(MXRA5),as a promising diagnostic and therapeutic target,was investigated in vivo and in vitro.Results:We identified five stable metastasis-related signatures(renin,integrin subunit beta-like 1,MXRA5,mesenchyme homeobox 2,and anoctamin 3)from multicenter cohorts.Additionally,we verified the specificity and sensibility of these signatures in external and out-house cohorts,which displayed a satisfactory consistency.According to these metastatic signatures,patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1(MTCS1)and type 2(MTCS2).MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1.In addition,MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1.The MTCS2 subgroup was more sensitive to saracatinib,sunitinib,and several molecular targeted drugs.In addition,MTCS2 displayed a higher genome mutation burden and instability.Furthermore,we constructed a prognosis model based on subtype biomarkers,which performed well in training and validation cohorts.Finally,MXRA5,as a promising biomarker,significantly suppressed malignant ability,including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.Conclusions:This study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses,multiomics landscapes,and treatment options.The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision‐making in immunotherapy and chemotherapy.展开更多
基金This work is supported by Natural Science Foundation of Jiangsu Province,China[BK20170306]National Key R&D Program,China[2017YFC0306100].The initials of authors who received these grants are YZ and JL,respectively.It is also supported by Fundamental Research Funds for Central Universities,China[B200202217]Changzhou Science and Technology Program,China[CJ20200065].The initials of author who received these grants are YT.
文摘Graph filtering is an important part of graph signal processing and a useful tool for image denoising.Existing graph filtering methods,such as adaptive weighted graph filtering(AWGF),focus on coefficient shrinkage strategies in a graph-frequency domain.However,they seldom consider the image attributes in their graph-filtering procedure.Consequently,the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods.To fully exploit the image attributes,we propose a guided intra-patch smoothing AWGF(AWGF-GPS)method for single-image denoising.Unlike AWGF,which employs graph topology on patches,AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch.This operation forces the restored pixels to smoothly evolve in local areas,where both intra-and inter-patch relationships of the image are utilized during patch restoration.Meanwhile,a guided-patch regularizer is incorporated into AWGF-GPS.The guided patch is obtained in advance using a maximum-a-posteriori probability estimator.Because the guided patch is considered as a sketch of a denoised patch,AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch.Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images.It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods.In particular,it has the advantage of managing images with significant noise.
基金funded by grants from the National Natural Science Foundation of China(grant numbers:82002664,81872074,81772740,82173345 and 82373154)the Hanghai Jiading District Health Commission Scientific Research Project Youth Fund(grant num-ber:2020-QN-02)the Meng Chao Talent Training Plan-Youth Re-search Talent Training Program of Eastern Hepatobiliary Surgery Hos-pital and the Foundation for Distinguished Youths of Jiangsu Province(grant number:BK20200006).
文摘Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma(ccRCC).Methods:The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC pa-tients were correspondingly deregulated in ccRCC tissues.We adopted a two-step strategy,including Lasso and bootstrapping,to construct a novel risk stratification system termed the TDERS(Tumor-Derived Exosome-Related Risk Score).During the testing and validation phases,we leveraged multiple external datasets containing over 2000 RCC cases from eight cohorts and one inhouse cohort to evaluate the accuracy of the TDERS.In addition,enrichment analysis,immune infiltration signatures,mutation landscape and therapy sensitivity between the high and low TDERS groups were compared.Finally,the impact of TDERS on the tumor microenvironment(TME)was also analysed in our single-cell datasets.Results:TDERS consisted of 12 mRNAs deregulated in both exosomes and tissues from patients with ccRCC.TDERS achieved satisfactory performance in both prognosis and immune checkpoint inhibitor(ICI)response across all ccRCC cohorts and other pathological types,since the average area under the curve(AUC)to predict 5-year overall survival(OS)was larger than 0.8 across the four cohorts.Patients in the TDERS high group were resistant to ICIs,while mercaptopurine might function as a promising agent for those patients.Patients with a high TDERS were characterized by coagulation and hypoxia,which induced hampered tumor antigen presentation and relative resistance to ICIs.In addition,single cells from 12 advanced samples validated this phenomenon since the interaction between dendritic cells and macrophages was limited.Finally,PLOD2,which is highly expressed in fibro-and epi-tissue,could be a potential therapeutic target for ccRCC patients since inhibiting PLOD2 altered the malignant phenotype of ccRCC in vitro.Conclusion:As a novel,non-invasive,and repeatable monitoring tool,the TDERS could work as a robust risk stratification system for patients with ccRCC and precisely inform treatment decisions about ICI therapy.
基金supported by the National Natural Science Foundation of China(grant numbers:81902560,81730073).
文摘Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.
基金National Natural Science Foundation of China,Grant/Award Numbers:81730073,81872074。
文摘Background:Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management.There are limited biomarkers to predict the metastatic probability of renal cancer,especially in the early-stage subgroup.Here,our study applied robust machine-learning algorithms to identify metastatic and recurrence-related signatures across multiple renal cancer cohorts,which reached high accuracy in both training and testing cohorts.Methods:Clear cell renal cell carcinoma(ccRCC)patients with primary or metastatic site sequencing information from eight cohorts,including one outhouse cohort,were enrolled in this study.Three robust machine-learning algorithms were applied to identify metastatic signatures.Then,two distinct metastatic-related subtypes were identified and verified;matrix remodeling associated 5(MXRA5),as a promising diagnostic and therapeutic target,was investigated in vivo and in vitro.Results:We identified five stable metastasis-related signatures(renin,integrin subunit beta-like 1,MXRA5,mesenchyme homeobox 2,and anoctamin 3)from multicenter cohorts.Additionally,we verified the specificity and sensibility of these signatures in external and out-house cohorts,which displayed a satisfactory consistency.According to these metastatic signatures,patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1(MTCS1)and type 2(MTCS2).MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1.In addition,MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1.The MTCS2 subgroup was more sensitive to saracatinib,sunitinib,and several molecular targeted drugs.In addition,MTCS2 displayed a higher genome mutation burden and instability.Furthermore,we constructed a prognosis model based on subtype biomarkers,which performed well in training and validation cohorts.Finally,MXRA5,as a promising biomarker,significantly suppressed malignant ability,including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.Conclusions:This study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses,multiomics landscapes,and treatment options.The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision‐making in immunotherapy and chemotherapy.
