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  • Springer Science and Business Media LLC  (6)
  • Cai, Sanjun  (6)
  • Zhang, Long  (6)
  • English  (6)
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  • Springer Science and Business Media LLC  (6)
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  • English  (6)
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  • 1
    Online Resource
    Online Resource
    Antisense lncRNA LDLRAD4-AS1 promotes metastasis by decreasing the expression of LDLRAD4 and predicts a poor prognosis in colorectal cancer
    Springer Science and Business Media LLC ; 2020
    In:  Cell Death & Disease Vol. 11, No. 2 ( 2020-02-28)
    Details
    In: Cell Death & Disease, Springer Science and Business Media LLC, Vol. 11, No. 2 ( 2020-02-28)
    Abstract: Long noncoding RNAs (lncRNAs) have been revealed to play critical roles in tumor initiation and progression. The antisense lncRNA LDLRAD4-AS1 is the longest lncRNA of LDLRAD4, and its expression levels, cellular localization, precise function, and mechanism in colorectal cancer (CRC) remain unknown. In this study, we observed that lncRNA LDLRAD4-AS1 was located in the nucleus of CRC cells and that lncRNA LDLRAD4-AS1 was upregulated in most CRC specimens and cell lines. Overexpression of lncRNA LDLRAD4-AS1 was correlated with poor prognosis in CRC patients. LncRNA LDLRAD4-AS1 upregulation enhanced the migration and invasion of CRC cells in vitro and facilitated CRC metastasis in vivo. Mechanistic investigations suggested that lncRNA LDLRAD4-AS1 could decrease the expression of LDLRAD4 by disrupting the stability of LDLRAD4 mRNA, resulting in epithelial-to-mesenchymal transition (EMT) through upregulating Snail, thereby promoting metastasis in CRC. Our results demonstrated a previously unrecognized LDLRAD4-AS1-LDLRAD4-Snail regulatory axis involved in epigenetic and posttranscriptional regulation that contributes to CRC progression and metastasis.
    Type of Medium: Online Resource
    ISSN: 2041-4889
    URL: Article
    DOI: 10.1038/s41419-020-2338-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2541626-1
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  • 2
    Online Resource
    Online Resource
    Prognostic nomograms for predicting cause-specific survival and overall survival of stage I–III colon cancer patients: a large population-based study
    Springer Science and Business Media LLC ; 2019
    In:  Cancer Cell International Vol. 19, No. 1 ( 2019-12)
    Details
    In: Cancer Cell International, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2019-12)
    Abstract: The purpose of this study was to build functional nomograms based on significant clinicopathological features to predict cause-specific survival (CSS) and overall survival (OS) in patients with stage I–III colon cancer. Methods Data on patients diagnosed with stage I–III colon cancer between 2010 and 2015 were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, which were used to construct nomograms to predict the probabilities of CSS and OS. The performance of the nomogram was assessed by C-indexes, receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was used to compare clinical usage between the nomogram and the tumor–node–metastasis (TNM) staging system. Results Based on the univariate and multivariate analyses, features that correlated with survival outcomes were used to establish nomograms for CSS and OS prediction. The nomograms showed favorable sensitivity at predicting 1-, 3-, and 5-year CSS and OS, with a C-index of 0.78 (95% confidence interval (CI) 0.77–0.80) for CSS and 0.74 (95% CI 0.73–0.75) for OS. Calibration curves and ROC curves revealed excellent predictive accuracy. The clinically and statistically significant prognostic performance of the nomogram generated with the entire group of patients and risk scores was validated by a stratified analysis. DCA showed that the nomograms were more clinically useful than TNM stage. Conclusion Novel nomograms based on significant clinicopathological characteristics were developed and can be used as a tool for clinicians to predict CSS and OS in stage I–III colon cancer patients. These models could help facilitate a personalized postoperative evaluation.
    Type of Medium: Online Resource
    ISSN: 1475-2867
    URL: Article
    DOI: 10.1186/s12935-019-1079-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2091573-1
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  • 3
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    Online Resource
    GLUT3 induced by AMPK/CREB1 axis is key for withstanding energy stress and augments the efficacy of current colorectal cancer therapies
    Springer Science and Business Media LLC ; 2020
    In:  Signal Transduction and Targeted Therapy Vol. 5, No. 1 ( 2020-09-02)
    Details
    In: Signal Transduction and Targeted Therapy, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2020-09-02)
    Abstract: Cancer cells are usually characterized by hyperactive glucose metabolism, which can often lead to glucose scarcity; thus, alternative pathways to rewire cancer metabolism are required. Here, we demonstrated that GLUT3 was highly expressed in colorectal cancer (CRC) and negatively linked to CRC patient outcomes, whereas GLUT1 was not associated with CRC prognosis. Under glucose-limiting conditions, GLUT3 expedited CRC cell growth by accelerating glucose input and fuelling nucleotide synthesis. Notably, GLUT3 had a greater impact on cell growth than GLUT1 under glucose-limiting stress. Mechanistically, low-glucose stress dramatically upregulated GLUT3 via the AMPK/CREB1 pathway. Furthermore, high GLUT3 expression remarkably increased the sensitivity of CRC cells to treatment with vitamin C and vitamin C-containing regimens. Together, the results of this study highlight the importance of the AMPK/CREB1/GLUT3 pathway for CRC cells to withstand glucose-limiting stress and underscore the therapeutic potential of vitamin C in CRC with high GLUT3 expression.
    Type of Medium: Online Resource
    ISSN: 2059-3635
    URL: Article
    DOI: 10.1038/s41392-020-00220-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2886872-9
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  • 4
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    Online Resource
    Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection
    Springer Science and Business Media LLC ; 2022
    In:  Molecular Cancer Vol. 21, No. 1 ( 2022-12)
    Details
    In: Molecular Cancer, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2022-12)
    Abstract: Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients ( N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.
    Type of Medium: Online Resource
    ISSN: 1476-4598
    URL: Article
    DOI: 10.1186/s12943-022-01594-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2091373-4
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  • 5
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    Online Resource
    Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma
    Springer Science and Business Media LLC ; 2021
    In:  Journal of Hematology & Oncology Vol. 14, No. 1 ( 2021-12)
    Details
    In: Journal of Hematology & Oncology, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2021-12)
    Abstract: Previous studies on liquid biopsy-based early detection of advanced colorectal adenoma (advCRA) or adenocarcinoma (CRC) were limited by low sensitivity. We performed a prospective study to establish an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for accurately and cost-effectively detecting early-stage CRC and advCRA. The training cohort enrolled 310 participants, including 149 early-stage CRC patients, 46 advCRA patients and 115 healthy controls. Plasma cfDNA samples were prepared for whole-genome sequencing. An ensemble stacked model differentiating healthy controls from advCRA/early-stage CRC patients was trained using five machine learning models and five cfDNA fragmentomic features based on the training cohort. The model was subsequently validated using an independent test cohort ( N  = 311; including 149 early-stage CRC, 46 advCRA and 116 healthy controls). Our model showed an area under the curve (AUC) of 0.988 for differentiating advCRA/early-stage CRC patients from healthy individuals in an independent test cohort. The model performed even better for identifying early-stage CRC (AUC 0.990) compared to advCRA (AUC 0.982). At 94.8% specificity, the sensitivities for detecting advCRA and early-stage CRC reached 95.7% and 98.0% (0: 94.1%; I: 98.5%), respectively. Promisingly, the detection sensitivity has reached 100% and 97.6% in early-stage CRC patients with negative fecal occult or CEA blood test results, respectively. Finally, our model maintained promising performances (AUC: 0.982, 94.4% sensitivity at 94.8% specificity) even when sequencing depth was down-sampled to 1X. Our integrated predictive model demonstrated an unprecedented detection sensitivity for advCRA and early-stage CRC, shedding light on more accurate noninvasive CRC screening in clinical practice.
    Type of Medium: Online Resource
    ISSN: 1756-8722
    URL: Article
    DOI: 10.1186/s13045-021-01189-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2429631-4
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  • 6
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    Online Resource
    Establishment and validation of a novel nomogram incorporating clinicopathological parameters into the TNM staging system to predict prognosis for stage II colorectal cancer
    Springer Science and Business Media LLC ; 2020
    In:  Cancer Cell International Vol. 20, No. 1 ( 2020-12)
    Details
    In: Cancer Cell International, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2020-12)
    Abstract: Survival outcomes are significantly different in stage II colorectal cancer (CRC) patients with diverse clinicopathological features. The objective of this study is to establish a credible prognostic nomogram incorporating easily obtained parameters for stage II CRC patients. Methods A total of 1708 stage II CRC patients seen at Fudan University Shanghai Cancer Center (FUSCC) from 2008 to 2013 were retrospectively analyzed in this study. Cases were randomly separated into a training set (n = 1084) and a validation set (n = 624). Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors that were subsequently incorporated into a nomogram. The performance of the nomogram was evaluated by the predicted concordance index (C-index) and ROC curve to calculate the area under the curve (AUC). The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Results In univariate and multivariate analyses, eight parameters were correlated with disease-free survival (DFS), which were subsequently selected to generate a prognostic nomogram based on DFS. For DFS predictions, the C-index values of the nomogram were 0.842 (95% confidence interval (CI) 0.710–0.980), and 0.701 (95% CI 0.610–0.770) for the training and validation sets, respectively. The AUC values of the ROC curves for the nomogram to predicted 1, 3 and 5-year survival were 0.869, 0.858, and 0.777 (training group) and 0.673, 0.714, and 0.706 (validation group), respectively. The recurrence probability calibration curve showed good consistency between actual observations and nomogram-based predictions. DCA showed better clinical application value for the nomogram than the TNM staging system. Conclusion A novel nomogram was established and validated in a large population, and the nomogram is a simple-to-use tool for physicians to facilitate postoperative personalized prognostic evaluation and determine therapeutic strategies for stage II CRC patients.
    Type of Medium: Online Resource
    ISSN: 1475-2867
    URL: Article
    DOI: 10.1186/s12935-020-01382-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2091573-1
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