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We are analyzing https://link.springer.com/article/10.1007/s10495-023-01900-x.

Title:
Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma | Apoptosis
Description:
Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {πŸ“š}

  • Education
  • Science
  • Telecommunications

Content Management System {πŸ“}

What CMS is link.springer.com built with?

Custom-built

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Traffic Estimate {πŸ“ˆ}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 7,642,828 visitors per month in the current month.

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How Does Link.springer.com Make Money? {πŸ’Έ}

We don't see any clear sign of profit-making.

Many websites are intended to earn money, but some serve to share ideas or build connections. Websites exist for all kinds of purposes. This might be one of them. Link.springer.com could be secretly minting cash, but we can't detect the process.

Keywords {πŸ”}

pubmed, article, google, scholar, cas, central, cancer, data, cell, hepatocellular, carcinoma, ferroptosis, zhejiang, zhang, analysis, nat, cao, immune, research, jing, sun, singlecell, access, liver, chen, china, information, machine, yang, junhui, cells, tumor, hangzhou, privacy, cookies, content, apoptosis, score, cong, tiantian, pan, death, hcc, drf, immunotherapy, treatment, author, rev, biol, medicine,

Topics {βœ’οΈ}

pd-1–pd-l1 immune-checkpoint blockade jun-hui sun month download article/chapter disulfidoptosis-related ferroptosis genes multimodal single-cell data single-cell rna-seq single-cell level function disulfidoptosis-related ferroptosis liver cancer article apoptosis aims full article pdf interpreting omics data immune checkpoint blockade single immune cells rna-seq data liver disease liver sensitive privacy choices/manage cookies related subjects low-dimensional data programmed cell death hepatocellular carcinoma published bulk rna-seq t-regulatory cells article zhang primary distinguished factor bayesian mixture model nonapoptotic cell death immunogenic cell death li jing combining ferroptosis induction tissue-infiltrating immune immune cell types central laboratory low-score groups machine learning tissue expression profiles holds exclusive rights zhejiang university school cell cycle pathway stromal cell populations immune checkpoint inhibitors european economic area biochim biophys acta el-deiry ws actin cytoskeleton vulnerability iron-dependent form manually curated database egln2 dna methylation early-stage nsclc

Schema {πŸ—ΊοΈ}

WebPage:
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         headline:Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma
         description:Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.
         datePublished:2023-10-24T00:00:00Z
         dateModified:2023-10-24T00:00:00Z
         pageStart:103
         pageEnd:120
         sameAs:https://doi.org/10.1007/s10495-023-01900-x
         keywords:
            Disulfidoptosis
            Ferroptosis
            HCC
            Machine learning
            Prognosis
            Cancer Research
            Cell Biology
            Oncology
            Biochemistry
            general
            Virology
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            name:Apoptosis
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               1573-675X
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               name:Cong Zhang
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ScholarlyArticle:
      headline:Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma
      description:Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.
      datePublished:2023-10-24T00:00:00Z
      dateModified:2023-10-24T00:00:00Z
      pageStart:103
      pageEnd:120
      sameAs:https://doi.org/10.1007/s10495-023-01900-x
      keywords:
         Disulfidoptosis
         Ferroptosis
         HCC
         Machine learning
         Prognosis
         Cancer Research
         Cell Biology
         Oncology
         Biochemistry
         general
         Virology
      image:
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      isPartOf:
         name:Apoptosis
         issn:
            1573-675X
            1360-8185
         volumeNumber:29
         type:
            Periodical
            PublicationVolume
      publisher:
         name:Springer US
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Cong Zhang
            affiliation:
                  name:Zhejiang University School of Medicine
                  address:
                     name:Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
                     type:PostalAddress
                  type:Organization
                  name:Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases
                  address:
                     name:Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, China
                     type:PostalAddress
                  type:Organization
                  name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases
                  address:
                     name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases, Hangzhou, China
                     type:PostalAddress
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            type:Person
            name:Tiantian Xu
            affiliation:
                  name:Zhejiang University School of Medicine
                  address:
                     name:Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
                     type:PostalAddress
                  type:Organization
                  name:Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases
                  address:
                     name:Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, China
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                  name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases
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                     name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases, Hangzhou, China
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            name:Kun Ji
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                  name:Zhejiang University School of Medicine
                  address:
                     name:Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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                  address:
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                     type:PostalAddress
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                  address:
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            name:Jing Ai
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                  name:Zhejiang University School of Medicine
                  address:
                     name:Department of Ophthalmology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
                     type:PostalAddress
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            type:Person
            name:Junhan Pan
            affiliation:
                  name:Zhejiang University
                  address:
                     name:Department of Radiology, The First Affiliated Hospital College of Medicine, Zhejiang University, Hangzhou, China
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Yunbo Cao
            affiliation:
                  name:Zhejiang University School of Medicine
                  address:
                     name:Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
                     type:PostalAddress
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                  address:
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                  name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases
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                     name:Zhejiang Provincial Research Center for Diagnosis and Treatment of Hepatobiliary Diseases, Hangzhou, China
                     type:PostalAddress
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            name:Yuning Yang
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