Article
Biochemical Research Methods
Isabelle Bichindaritz, Guanghui Liu, Christopher Bartlett
Summary: The study proposes an adaptive multi-task learning method for survival prediction of breast cancer patients using multi-modal learning, achieving more effective results compared to existing approaches. By combining different gene features, reducing dimensions, and introducing auxiliary loss, an ordinal Cox hazards model is built to predict patients' survival risk.
Article
Biochemical Research Methods
Maryam Pouryahya, Jung Hun Oh, Pedram Javanmard, James C. Mathews, Zehor Belkhatir, Joseph O. Deasy, Allen R. Tannenbaum
Summary: In this study, a novel network-based multiomics clustering method called aWCluster is proposed. By accentuating the genes that have concordant multiomics measurements in their interaction network, aWCluster successfully clusters different cancer types into classes with significantly different survival rates.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Francois Fauteux, Anuradha Surendra, Scott McComb, Youlian Pan, Jennifer J. Hill
Summary: Classification of lung cancer subtypes based on different gene expression profiling technologies can inform personalized treatment approaches. By integrating microarray and RNA-seq data and utilizing specific preprocessing, cross-platform normalization, and unsupervised feature selection methods, robust gene expression subtypes can be identified. This study confirms the existence of three lung adenocarcinoma transcriptional subtypes, two squamous cell carcinoma subtypes, and shows that these tumor subtypes are associated with distinct patterns of genomic alterations in therapeutic target genes. Integration of quantitative proteomics data allows for the identification of tumor subtype biomarkers that effectively classify samples based on both gene and protein expression, providing a basis for further integrative data analysis across gene and protein expression profiling platforms.
SCIENTIFIC REPORTS
(2021)
Article
Medicine, General & Internal
Weinan Zheng, Fuyuan Jin, Fang Wang, Luyue Wang, Shaowei Fu, Zemin Pan, Haichen Long
Summary: This study aimed to explore and analyze the expression of eukaryotic translation elongation factor 1 alpha 2 (eEF1A2) gene in cervical cancer tissues and its relationship with patient survival, gene mutations, and changes in copy number. Gene expression profile interactive analysis and cBioPortal were used to analyze eEF1A2 expression and gene changes in cervical cancer tissues. The eEF1A2 copy number was determined using real-time fluorescence quantitative polymerase chain reaction. The Human Protein Atlas was utilized to analyze the relationship between eEF1A2 protein expression and clinical stage, pathological grade, and patient survival.
Article
Multidisciplinary Sciences
Shideh Mirhadi, Shirley Tam, Quan Li, Nadeem Moghal, Nhu-An Pham, Jiefei Tong, Brian J. Golbourn, Jonathan R. Krieger, Paul Taylor, Ming Li, Jessica Weiss, Sebastiao N. Martins-Filho, Vibha Raghavan, Yasin Mamatjan, Aafaque A. Khan, Michael Cabanero, Shingo Sakashita, Kugeng Huo, Sameer Agnihotri, Kota Ishizawa, Thomas K. Waddell, Gelareh Zadeh, Kazuhiro Yasufuku, Geoffrey Liu, Frances A. Shepherd, Michael F. Moran, Ming-Sound Tsao
Summary: Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide, and the development of targeted therapies is crucial. This study generated 137 NSCLC patient-derived xenografts, and through proteome analysis, identified different proteotypes associated with patient outcomes, protein-phosphotyrosine profiles, and candidate targets. These findings provide insights for NSCLC classification and treatment.
NATURE COMMUNICATIONS
(2022)
Article
Genetics & Heredity
Tingting Li, Ruifeng Li, Xuan Dong, Lin Shi, Miao Lin, Ting Peng, Pengze Wu, Yuting Liu, Xiaoting Li, Xuheng He, Xu Han, Bin Kang, Yinan Wang, Zhiheng Liu, Qing Chen, Yue Shen, Mingxiang Feng, Xiangdong Wang, Duojiao Wu, Jian Wang, Cheng Li
Summary: The study explored the 3D genome structure of clinical lung cancer samples using Hi-C experiments and RNA sequencing analysis. It demonstrated the feasibility of studying the 3D genome of clinical lung cancer samples with a small number of cells, identified different spatial chromatin structures between normal and cancer samples, and showed that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Kailin Tang, Xuejie Ji, Mengdi Zhou, Zeliang Deng, Yuwei Huang, Genhui Zheng, Zhiwei Cao
Summary: Rank-In is a method that corrects nonbiological effects in mixed microarray and RNA-seq data for integrated analysis. It has been validated to accurately classify samples and achieve high accuracy in predicting differentially expressed genes.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Biochemistry & Molecular Biology
Kailin Tang, Xuejie Ji, Mengdi Zhou, Zeliang Deng, Yuwei Huang, Genhui Zheng, Zhiwei Cao
Summary: Although transcriptomics technologies have advanced rapidly in the past decades, integrating mixed data from microarray and RNA-seq remains challenging due to inherent variability differences. Rank-In is a novel method proposed to correct nonbiological effects and enable consolidated analysis of blended data. Validated on public cell and tissue samples, Rank-In demonstrated superior classification and prediction accuracy, showing potential for integrative study of cancer profiles.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Oncology
Vincent Vuaroqueaux, Alexandra Musch, Dennis Kobelt, Thomas Risch, Pia Herrmann, Susen Burock, Anne-Lise Peille, Marie-Laure Yaspo, Heinz-Herbert Fiebig, Ulrike Stein
Summary: This study investigates the contribution of chromosomal instability and somatic copy number alterations (SCNA) to the dysregulation of MACC1 in colorectal cancer (CRC). The findings suggest that elevated MACC1 expression is largely driven by chromosomal instability (CIN), SCNA gains, and molecular subtypes, and it has prognostic and predictive impacts on metastasis and survival. These insights could potentially serve as a basis for personalized treatment decisions.
Article
Cell Biology
Mingyang Ma, Yang Chen, Xiaoyi Chong, Fangli Jiang, Jing Gao, Lin Shen, Cheng Zhang
Summary: This study identified high consistency between DNA copy number variations and abnormal methylations in esophageal cancer, and determined three subtypes with different molecular traits, prognostic characteristics, and tumor immune microenvironment features. The study also identified 4 prognostic genes differentially expressed in the three subtypes, which could be used as representative biomarkers for precision medication in esophageal cancer.
Article
Multidisciplinary Sciences
Yeonghun Lee, Hyunju Lee
Summary: Analyzing structural variations and karyotyping in cancer cells is challenging, but InfoGenomeR, a graph-based framework, shows promise in reconstructing individual SVs into karyotypes based on whole-genome sequencing data. By identifying private and shared mutations between primary and metastatic cancer sites, InfoGenomeR has the potential to guide targeted therapies based on cancer-specific SVs.
NATURE COMMUNICATIONS
(2021)
Article
Medicine, Research & Experimental
Qihang Zhong, Minzhen Lu, Wanqiong Yuan, Yueyi Cui, Hanqiang Ouyang, Yong Fan, Zhaohui Wang, Congying Wu, Jie Qiao, Jing Hang
Summary: This study identified three molecular subtypes of cervical cancer through multi-omics integration analysis, incorporating 617 differentially expressed lncRNAs and 1395 differentially expressed PCGs. The functional enrichment analysis revealed that the identified lncRNAs were mainly involved in tumor metabolism, immunity, and pathways related to cervical cancer development. A prognostic risk model based on lncRNAs was established and validated to be effective in prognosis management of cervical cancer patients.
JOURNAL OF TRANSLATIONAL MEDICINE
(2021)
Article
Biotechnology & Applied Microbiology
Sayyed Sajjad Moravveji, Samane Khoshbakht, Majid Mokhtari, Mahdieh Salimi, Ali Masoudi-Nejad
Summary: Omics data integration is crucial in revealing hidden insights about cancer, and through the examination of GI cancers from various perspectives, it was discovered that dysfunction in the cell cycle may largely result from combinatorial abnormalities.
Article
Oncology
Xiaodong Liu, Yanjin Li, Xiang Zhou, Sinan Zhu, Neslihan A. Kaya, Yun Shen Chan, Liang Ma, Miao Xu, Weiwei Zhai
Summary: In this study, the largest cohort of nasopharyngeal carcinoma (NPC) was collected and analyzed, leading to the identification of novel drivers and mutational signatures. By comparing NPC with other cancer types, unique processes driving a viral-positive cancer were discovered. An integrative survival model for NPC was constructed, providing valuable insights for patient prognosis and stratification.
Article
Psychiatry
Kazusa Miyahara, Mizuki Hino, Risa Shishido, Atsuko Nagaoka, Ryuta Izumi, Hideki Hayashi, Akiyoshi Kakita, Hirooki Yabe, Hiroaki Tomita, Yasuto Kunii
Summary: In this study, the postmortem brains of 26 patients with schizophrenia and 51 controls were used to identify gene sets associated with schizophrenia symptoms. The research also investigated the correlation between these gene sets and genetic background. Pathway and upstream analysis revealed the functions and regulators of these gene sets. This study provides insights into the pathophysiology of schizophrenia and identifies potential therapeutic targets.
TRANSLATIONAL PSYCHIATRY
(2023)