Cancer Research and Treatment

Original Articles

Paip1 Indicated Poor Prognosis in Cervical Cancer and Promoted Cervical Carcinogenesis: Nan Li, Junjie Piao, Xinyue Wang, Ki-Yeol Kim, Jung Yoon Bae, Xiangshan Ren, Zhenhua Lin; Cancer Res Treat. 2019;51(4):1653-1665. Published online April 19, 2019; DOI: https://doi.org/10.4143/crt.2018.544

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Purpose
This study was aimed to investigate the role of poly(A)-binding protein-interacting protein 1 (Paip1) in cervical carcinogenesis.
Materials and Methods
The expression of Paip1 in normal cervical epithelial tissues and cervical cancer (CC) tissues were detected by immunohistochemistry. In vivo and in vitro assays were performed to validate effect of Paip1 on CC progression.
Results
Paip1 was found to be up-regulated in CC, which was linked with shorter survival. Knockdown of Paip1 inhibited cell growth, induced apoptosis and cell cycle arrest in CC cells, whereas its overexpression reversed these effects. The in vivo tumor model confirmed the pro-tumor role of Paip1 in CC growth.
Conclusion
Altogether, the investigation demonstrated the clinical significance of Paip1 expression, which prompted that the up-regulated of Paip1 can presumably be a potential prognostic and progression marker for CC.

Citations

Citations to this article as recorded by

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach
T. Mallikarjuna, N. B. Thummadi, Vaibhav Vindal, P. Manimaran
Theory in Biosciences.2024; 143(3): 183. CrossRef
Knockdown of PAIP1 Inhibits Breast Cancer Cell Proliferation by Regulating Cyclin E2 mRNA Stability
Wenqing Yang, Qingkun Wang, Qi Li, Yue Han, Yu Zhang, Lu Zhu, Lianhua Zhu, Junjie Piao
Molecular Carcinogenesis.2024;[Epub] CrossRef
Human Proteome Microarray identifies autoantibodies to tumor‐associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma
Qian Yang, Hua Ye, Guiying Sun, Keyan Wang, Liping Dai, Cuipeng Qiu, Jianxiang Shi, Jicun Zhu, Xiao Wang, Peng Wang
Molecular Oncology.2023; 17(5): 887. CrossRef
PAIP1 regulates expression of immune and inflammatory response associated genes at transcript level in liver cancer cell
Jianfeng Zheng, Weiwei Fan, Xiaoyu Zhang, Weili Quan, Yunfei Wu, Mengni Shu, Moyang Chen, Ming Liang
PeerJ.2023; 11: e15070. CrossRef
Advances in HPV‐associated tumor management: Therapeutic strategies and emerging insights
Xiangjin Gong, Hao Chi, Zhijia Xia, Guanhu Yang, Gang Tian
Journal of Medical Virology.2023;[Epub] CrossRef
Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor
Neelesh Babu Thummadi, Mallikarjuna T., Vaibhav Vindal, Manimaran P.
Proteins: Structure, Function, and Bioinformatics.2022; 90(2): 363. CrossRef
Effect of PAIP1 on the metastatic potential and prognostic significance in oral squamous cell carcinoma
Neeti Swarup, Kyoung-Ok Hong, Kunal Chawla, Su-Jung Choi, Ji-Ae Shin, Kyu-Young Oh, Hye-Jung Yoon, Jae-Il Lee, Sung-Dae Cho, Seong-Doo Hong
International Journal of Oral Science.2022;[Epub] CrossRef
CRISPR/Cas9 Screening for Identification of Genes Required for the Growth of Ovarian Clear Cell Carcinoma Cells
Ayako Kawabata, Tomoatsu Hayashi, Yoko Akasu-Nagayoshi, Ai Yamada, Naomi Shimizu, Naoko Yokota, Ryuichiro Nakato, Katsuhiko Shirahige, Aikou Okamoto, Tetsu Akiyama
Current Issues in Molecular Biology.2022; 44(4): 1587. CrossRef
Circ_0005576 Exerts an Oncogenic Role in Cervical Cancer via miR-1305-Dependent Regulation of PAIP1
Yajing Wang, Fang Du, Zongyuan Xie, Junhao Lai, Yuanjie Li, Yongping Xu, Rui Tong
Reproductive Sciences.2022; 29(9): 2647. CrossRef
The p140Cap adaptor protein as a molecular hub to block cancer aggressiveness
Vincenzo Salemme, Costanza Angelini, Jennifer Chapelle, Giorgia Centonze, Dora Natalini, Alessandro Morellato, Daniela Taverna, Emilia Turco, Ugo Ala, Paola Defilippi
Cellular and Molecular Life Sciences.2021; 78(4): 1355. CrossRef
Effects of inducing apoptosis and inhibiting proliferation of siRNA on polyadenylate‐binding protein‐interacting protein 1 in tongue cell carcinoma
Huixu Xie, Lisa Yang, Qin Hu, Yingqi Song, Xiaoyi Wang, Liming Zhou, Longjiang Li
Head & Neck.2020; 42(12): 3623. CrossRef
Inhibition of carnitine palmitoyl transferase 1A-induced fatty acid oxidation suppresses cell progression in gastric cancer
Liqiang Wang, Changfeng Li, Yumei Song, ZhenKun Yan
Archives of Biochemistry and Biophysics.2020; 696: 108664. CrossRef
Whole-genome resequencing of Dulong Chicken reveal signatures of selection
Q. Wang, D. Li, A. Guo, M. Li, L. Li, J. Zhou, S. K. Mishra, G. Li, Y. Duan, Q. Li
British Poultry Science.2020; 61(6): 624. CrossRef

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Use of a Combined Gene Expression Profile in Implementing a Drug Sensitivity Predictive Model for Breast Cancer: Xianglan Zhang, In-Ho Cha, Ki-Yeol Kim; Cancer Res Treat. 2017;49(1):116-128. Published online May 18, 2016; DOI: https://doi.org/10.4143/crt.2016.085

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Purpose
Chemotherapy targets all rapidly growing cells, not only cancer cells, and thus is often associated with unpleasant side effects. Therefore, examination of the chemosensitivity based on genotypes is needed in order to reduce the side effects. Materials and Methods Various computational approaches have been proposed for predicting chemosensitivity based on gene expression profiles. A linear regression model can be used to predict the response of cancer cells to chemotherapeutic drugs, based on genomic features of the cells, and appropriate sample size for this method depends on the number of predictors. We used principal component analysis and identified a combined gene expression profile to reduce the number of predictors
Results
The coefficients of determinanation (R²) of prediction models with combined gene expression and several independent gene expressions were similar. Corresponding F values, which represent model significances were improved by use of a combined gene expression profile, indicating that the use of a combined gene expression profile is helpful in predicting drug sensitivity. Even better, a prediction model can be used even with small samples because of the reduced number of predictors. Conclusion Combined gene expression analysis is expected to contribute to more personalized management of breast cancer cases by enabling more effective targeting of existing therapies. This procedure for identifying a cell-type-specific gene expression profile can be extended to other chemotherapeutic treatments and many other heterogeneous cancer types.

Citations

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Clinical Oncogenomics and Personalized Medicine in Colorectal Cancer for the Surgeon: What We Need to Know and What the Future Holds
I. S. Reynolds, E. O’Connell, D. A. McNamara, J. H. M. Prehn, S. J. Furney, J. P. Burke
SN Comprehensive Clinical Medicine.2022;[Epub] CrossRef
OBIF: an omics-based interaction framework to reveal molecular drivers of synergy
Jezreel Pantaleón García, Vikram V Kulkarni, Tanner C Reese, Shradha Wali, Saima J Wase, Jiexin Zhang, Ratnakar Singh, Mauricio S Caetano, Humam Kadara, Seyed Javad Moghaddam, Faye M Johnson, Jing Wang, Yongxing Wang, Scott E Evans
NAR Genomics and Bioinformatics.2022;[Epub] CrossRef
Screening and Identification of Differentially Expressed Genes Expressed among Left and Right Colon Adenocarcinoma
Jing Han, Xue Zhang, Yang Yang, Li Feng, Gui-Ying Wang, Nan Zhang
BioMed Research International.2020; 2020: 1. CrossRef
Overexpression of miR-331 Indicates Poor Prognosis and Promotes Progression of Breast Cancer
Fuguo Jiang, Lei Zhang, Yunxia Liu, Yanhua Zhou, Honggang Wang
Oncology Research and Treatment.2020; 43(9): 441. CrossRef
Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis
Huan-Ming Hsu, Chi-Ming Chu, Yu-Jia Chang, Jyh-Cherng Yu, Chien-Ting Chen, Chen-En Jian, Chia-Yi Lee, Yueh-Tao Chiang, Chi-Wen Chang, Yu-Tien Chang
Scientific Reports.2019;[Epub] CrossRef
Prediction of Drug Target Sensitivity in Cancer Cell Lines Using Apache Spark
Shahid Hussain, Javed Ferzund, Raza Ul-Haq
Journal of Computational Biology.2019; 26(8): 882. CrossRef
Artificial Intelligence and Pharmacogenomics
Ravishankar K. Iyer, Arjun P. Athreya, Liewei Wang, Richard M. Weinshilboum
Advances in Molecular Pathology.2019; 2(1): 111. CrossRef

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Novel Methods for Clinical Risk Stratification in Patients with Colorectal Liver Metastases: Ki-Yeol Kim, Nam Kyu Kim, In-Ho Cha, Joong Bae Ahn, Jin Sub Choi, Gi-Hong Choi, Joon Suk Lim, Kang Young Lee, Seung Hyuk Baik, Byung Soh Min, Hyuk Hur, Jae Kyung Roh, Sang Joon Shin; Cancer Res Treat. 2015;47(2):242-250. Published online September 11, 2014; DOI: https://doi.org/10.4143/crt.2014.066

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Purpose
Colorectal cancer patients with liver-confined metastases are classified as stage IV, but their prognoses can differ from metastases at other sites. In this study, we suggest a novel method for risk stratification using clinically effective factors. Materials and Methods Data on 566 consecutive patients with colorectal liver metastasis (CLM) between 1989 and 2010 were analyzed. This analysis was based on principal component analysis (PCA). Results The survival rate was affected by carcinoembryonic antigen (CEA) level (p < 0.001; risk ratio, 1.90), distribution of liver metastasis (p=0.014; risk ratio, 1.46), and disease-free interval (DFI; p < 0.001; risk ratio, 1.98). When patients were divided into three groups according to PCA score using significantly affected factors, they showed significantly different survival patterns (p < 0.001). Conclusion The PCA scoring system based on CEA level, distribution of liver metastasis, and DFI may be useful for preoperatively determining prognoses in order to assist in clinical decisionmaking and designing future clinical trials for CLM treatment.

Citations

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Differential Perspectives by Specialty on Oligometastatic Colorectal Cancer: A Korean Oligometastasis Working Group’s Comparative Survey Study
Won Kyung Cho, Gyu Sang Yoo, Chai Hong Rim, Jae-Uk Jeong, Eui Kyu Chie, Yong Chan Ahn, Hyeon-Min Cho, Jun Won Um, Yang-Gun Suh, Ah Ram Chang, Jong Hoon Lee
Cancer Research and Treatment.2023; 55(4): 1281. CrossRef
Long disease-free interval diminishes the prognostic value of primary tumor stage for patients with colorectal cancer liver metastases
Jia-Ming Liu, Yan-Yan Wang, Wei Liu, Da Xu, Kun Wang, Bao-Cai Xing
HPB.2022; 24(5): 737. CrossRef
A Randomized Phase II Study of Perioperative Chemotherapy Plus Bevacizumab Versus Postoperative Chemotherapy Plus Bevacizumab in Patients With Upfront Resectable Hepatic Colorectal Metastases
You Jin Chun, Seong-Geun Kim, Keun-Wook Lee, Sang Hee Cho, Tae Won Kim, Ji Yeon Baek, Young Suk Park, Soojung Hong, Chong Woo Chu, Seung-Hoon Beom, Minkyu Jung, Sang Joon Shin, Joong Bae Ahn
Clinical Colorectal Cancer.2020; 19(3): e140. CrossRef
Preclinical Efficacy of [V4 Q5 ]dDAVP, a Second Generation Vasopressin Analog, on Metastatic Spread and Tumor-Associated Angiogenesis in Colorectal Cancer
Juan Garona, Natasha T. Sobol, Marina Pifano, Valeria I. Segatori, Daniel E. Gomez, Giselle V. Ripoll, Daniel F. Alonso
Cancer Research and Treatment.2019; 51(2): 438. CrossRef
A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach
Kanisha Shah, Shanaya Patel, Sheefa Mirza, Rakesh M. Rawal, Kapil Mehta
PLOS ONE.2018; 13(11): e0206400. CrossRef
A combined prognostic factor for improved risk stratification of patients with oral cancer
K‐Y Kim, X Zhang, S‐M Kim, B‐D Lee, I‐H Cha
Oral Diseases.2017; 23(1): 91. CrossRef

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An Attempt for Combining Microarray Data Sets by Adjusting Gene Expressions: Ki-Yeol Kim, Se Hyun Kim, Dong Hyuk Ki, Jaeheon Jeong, Ha Jin Jeong, Hei-Cheul Jeung, Hyun Cheol Chung, Sun Young Rha; Cancer Res Treat. 2007;39(2):74-81. Published online June 30, 2007; DOI: https://doi.org/10.4143/crt.2007.39.2.74

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Purpose

The diverse experimental environments in microarray technology, such as the different platforms or different RNA sources, can cause biases in the analysis of multiple microarrays. These systematic effects present a substantial obstacle for the analysis of microarray data, and the resulting information may be inconsistent and unreliable. Therefore, we introduced a simple integration method for combining microaray data sets that are derived from different experimental conditions, and we expected that more reliable information can be detected from the combined data set rather than from the separated data sets.

Materials and Methods

This method is based on the distributions of the gene expression ratios among the different microarray data sets and it transforms, gene by gene, the gene expression ratios into the form of the reference data set. The efficiency of the proposed integration method was evaluated using two microarray data sets, which were derived from different RNA sources, and a newly defined measure, the mixture score.

Results

The proposed integration method intermixed the two data sets that were obtained from different RNA sources, which in turn reduced the experimental bias between the two data sets, and the mixture score increased by 24.2%. A data set combined by the proposed method preserved the inter-group relationship of the separated data sets.

Conclusion

The proposed method worked well in adjusting systematic biases, including the source effect. The ability to use an effectively integrated microarray data set yields more reliable results due to the larger sample size and this also decreases the chance of false negatives.

Citations

Citations to this article as recorded by

spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics
Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li
GigaScience.2024;[Epub] CrossRef
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Briefings in Bioinformatics.2022;[Epub] CrossRef
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Harmonization strategies for multicenter radiomics investigations
R Da-Ano, D Visvikis, M Hatt
Physics in Medicine & Biology.2020; 65(24): 24TR02. CrossRef
A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies
Gift Nyamundanda, Pawan Poudel, Yatish Patil, Anguraj Sadanandam
Scientific Reports.2017;[Epub] CrossRef
Batch effect removal methods for microarray gene expression data integration: a survey
C. Lazar, S. Meganck, J. Taminau, D. Steenhoff, A. Coletta, C. Molter, D. Y. Weiss-Solis, R. Duque, H. Bersini, A. Nowe
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Identification of genes related to a synergistic effect of taxane and suberoylanilide hydroxamic acid combination treatment in gastric cancer cells
Hyun Chang, Sun Young Rha, Hei-Cheul Jeung, Jae-Jun Jung, Tae Soo Kim, Ho Jeong Kwon, Byung Soo Kim, Hyun Cheol Chung
Journal of Cancer Research and Clinical Oncology.2010; 136(12): 1901. CrossRef

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