Expression and prognostic significance of TAp73 and ΔNp73 in FIGO stage I-II cervical squamous cell carcinoma

Zhu, Weipei; Pan, Xiaohong; Yang, Zhujuan; Xing, Pengfei; Zhang, Yongshen; Li, Feng; Lu, Xiao‐Jie

doi:10.3892/ol.2015.3052

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Aberrant alternative splicing events are a common theme in cancer drug resistance and, therefore, strategies targeted to silence variants that promote drug resistance are highly warranted. Aberrant splice variants can promote resistance to chemotherapy and radiotherapy [24,[86][87][88] by mechanisms that include apoptotic regulation, modified drug metabolism, response to DNA damage, and regulation of cell proliferation (Figure 6) [89]. Radiotherapy is an important therapeutic modality for the management of advanced cervical cancer and radioresistance may be detrimental.…”

Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

“…Cervical cancer cells exposed to high-LET radiation degrade ∆Np73 to exhibit enhanced apoptosis and cell cycle arrest at the G2/M phase when compared with low-LET radiation [91]. In addition, ∆Np73 promotes malignant transformation by interacting with RAS and inducing drug resistance to chemotherapy and radiotherapy [87]. Furthermore, the HPV oncoprotein, E6, suppresses the activity of p53 expression and alters sensitivity to radiotherapy.…”

Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

“…By blocking the activity of Src and Akt through pharmacological inhibitors such as dasatinib [97] and fucoxanthin [100], respectively, aberrant splicing events that facilitate chemoresistance in cervical cancer can be reversed and promote a complete therapy response in advanced metastatic disease. In addition, splice variants may also alter drug targets that effect drug metabolism and lead to chemoresistance and alter the sensitivity to radiotherapy [86,87,89,95,[97][98][99].…”

Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

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Splicing Genomics Events in Cervical Cancer: Insights for Phenotypic Stratification and Biomarker Potency

Francies

Bassa

Chatziioannou

et al. 2021

Genes

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Gynaecological cancers are attributed to the second most diagnosed cancers in women after breast cancer. On a global scale, cervical cancer is the fourth most common cancer and the most common cancer in developing countries with rapidly increasing mortality rates. Human papillomavirus (HPV) infection is a major contributor to the disease. HPV infections cause prominent cellular changes including alternative splicing to drive malignant transformation. A fundamental characteristic attributed to cancer is the dysregulation of cellular transcription. Alternative splicing is regulated by several splicing factors and molecular changes in these factors lead to cancer mechanisms such as tumour development and progression and drug resistance. The serine/arginine-rich (SR) proteins and heterogeneous ribonucleoproteins (hnRNPs) have prominent roles in modulating alternative splicing. Evidence shows molecular alteration and expression levels in these splicing factors in cervical cancer. Furthermore, aberrant splicing events in cancer-related genes lead to chemo- and radioresistance. Identifying clinically relevant modifications in alternative splicing events and splicing variants, in cervical cancer, as potential biomarkers for their role in cancer progression and therapy resistance is scrutinised. This review will focus on the molecular mechanisms underlying the aberrant splicing events in cervical cancer that may serve as potential biomarkers for diagnosis, prognosis, and novel drug targets.

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Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

Section: Alternative Splicing and Therapy Resistancementioning

confidence: 99%

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Splicing Genomics Events in Cervical Cancer: Insights for Phenotypic Stratification and Biomarker Potency

Francies

Bassa

Chatziioannou

et al. 2021

Genes

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“…The imbalance between TAp73 and DNp73 isoforms may be useful to predict response to chemotherapy and prognosis (Muller et al, 2005;Lucena-Araujo et al, 2015). High DNp73 expression has strong correlation with unfavorable prognosis in several types of cancer patients, and DNp73-positive tumors show a reduced response to chemotherapy and irradiation (Uramoto et al, 2004;Di et al, 2013;Zhu et al, 2015). The upregulation of DNp73 was frequently detected in radioresistant cervical cancers (Liu et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients

et al. 2020

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Background: Rectal cancer is a disease characterized with tumor heterogeneity. The combination of surgery, radiotherapy, and chemotherapy can reduce the risk of local recurrence. However, there is a significant difference in the response to radiotherapy among rectal cancer patients even they have the same tumor stage. Despite rapid advances in knowledge of cellular functions affecting radiosensitivity, there is still a lack of predictive factors for local recurrence and normal tissue damage. The tumor protein DNp73 is thought as a biomarker in colorectal cancer, but its clinical significance is still not sufficiently investigated, mainly due to the limitation of human-based pathology analysis. In this study, we investigated the predictive value of DNp73 in patients with rectal adenocarcinoma using image-based network analysis. Methods: The fuzzy weighted recurrence network of time series was extended to handle multi-channel image data, and applied to the analysis of immunohistochemistry images of DNp73 expression obtained from a cohort of 25 rectal cancer patients who underwent radiotherapy before surgery. Two mathematical weighted network properties, which are the clustering coefficient and characteristic path length, were computed for the image-based networks of the primary tumor (obtained after operation) and biopsy (obtained before operation) of each cancer patient. Results: The ratios of two weighted recurrence network properties of the primary tumors to biopsies reveal the correlation of DNp73 expression and long survival time, and discover the non-effective radiotherapy to a cohort of rectal cancer patients who had short survival time. Conclusion: Our work contributes to the elucidation of the predictive value of DNp73 expression in rectal cancer patients who were given preoperative radiotherapy. Mathematical properties of fuzzy weighted recurrence networks of immunohistochemistry images are not only able to show the predictive factor of DNp73 expression in the patients, but also reveal the identification of non-effective application of radiotherapy to those who had poor overall survival outcome.

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“…There are mainly two isoforms: the oncogenic DNp73 isoform and the anti-oncogenic TAp73 isoform. Researchers have recently focused on DNp73 and found that high DNp73 expression has a strong correlation with unfavorable prognosis in several types of cancer pa-tients, and DNp73-positive tumors show a reduced response to chemotherapy and irradiation [4,5]. Previous findings indicated that DNp73 is increased in colon cancer cell line that is resistant to radiation [6].…”

Section: Introductionmentioning

confidence: 99%

AI-based Five-Year Survival Prediction and Prognosis of DNp73 Expression in Rectal Cancer Patients

Pham

Fan

Zhang

et al. 2020

Preprint

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Predictive and prognostic markers in oncology are important areas of clinical cancer research and play essential roles for optimal decision making on therapy and assessing treatment efficacy for precision medicine in cancer patients. While DNp73 has been thought as a biomarker in rectal cancer, its clinical significance is still controversial. Exploration on the role of DNp73 protein expression in rectal cancer is a challenging task because of the limitation of human-based pathology analysis of complex information in pathological images. In this paper, we investigated the power of DNp73 expression identified by immunohistochemistry as predictive and prognostic markers in a cohort of 143 rectal cancer patients from the Swedish rectal cancer trial of preoperative radiotherapy using state-of-the-art artificial intelligence (AI) for machine learning and classification. Average validation results show very high accuracy rates (greater than or equal to to 93%) for the 5-year prediction and prognosis of the rectal cancer patients either with or without preoperative radiotherapy. In comparison with the pathology-based analysis, where DNp73 did not provide any survival information (p > 0.05), not only the findings of the AI-based study are clinically significant, but also pave a new direction for the rapid exploration of biomarkers in oncology.

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Expression and prognostic significance of TAp73 and ΔNp73 in FIGO stage I-II cervical squamous cell carcinoma

Cited by 8 publications

References 18 publications

Splicing Genomics Events in Cervical Cancer: Insights for Phenotypic Stratification and Biomarker Potency

Splicing Genomics Events in Cervical Cancer: Insights for Phenotypic Stratification and Biomarker Potency

Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients

AI-based Five-Year Survival Prediction and Prognosis of DNp73 Expression in Rectal Cancer Patients

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