Background: Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective: Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods: We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results: Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions: Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

Matrix metalloproteinase 2 (MMP2) plays critical roles in various diseases, such as atherosclerosis and cancer, and has been suggested to contribute to the instability of atherosclerotic plaque. To visualize MMP2 in pathologic tissues, we developed an aptamer targeting MMP2 protein by performing eight rounds of modified DNA systematic evolution of ligands by exponential enrichment (SELEX). The aptamer showed high affinity for MMP2 (K _d = 5.59 nM), precipitated MMP2, and detected MMP2 protein in pathological tissues such as atherosclerotic plaque and gastric cancer tissues. Furthermore, a MMP2 aptamer-conjugated fluorescent nanoprobe successfully visualized atherosclerotic plaques in apolipoprotein E (ApoE) knockout mice. These results suggest that the devised MMP2 aptamer could be useful for the development of various diagnostic tools.

Myoung-Eun Han, Si-Young Park, Sae-Ock Oh. Anat Cell Biol 2021;54:152-64. https://doi.org/10.5115/acb.20.305

Minjeong Kim, Kwang-Woo Jo, Hyojin Kim, Myoung-Eun Han, Sae-Ock Oh. Anat Cell Biol 2023;56:94-108. https://doi.org/10.5115/acb.22.161

BACKGROUND Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. OBJECTIVE Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. METHODS We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). RESULTS Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. CONCLUSIONS Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

The coatomer complex has been implicated in cancer progression; however, a comprehensive pan-cancer analysis is lacking. Therefore, it is essential to identify the critical roles and essentiality of coatomer genes across pan-cancer. We systematically profiled the genetic alterations, expression patterns, prognostic relevance, and functional dependencies of all coatomer subunits across multiple cancers using more than 10,000 tumor samples from The Cancer Genome Atlas, complemented by functional perturbation data from CRISPR (n = 1178) and RNAi (n = 707) screens in DepMap. Functional validation was also performed to identify the essentiality of selectively essential coatomer genes in hepatocellular carcinoma (HCC). Gene amplification, most notably of COPB2, was the most frequent alteration and was associated with poor survival in bladder and esophageal cancers. Mutations in COPA and SEC31A also demonstrated prognostic significance in endometrial carcinoma. Expression analyses revealed broad upregulation of coatomer genes across cancer types, with COPG1 and COPB1 emerging as strong risk-associated genes (HR > 2). Integrative functional dependency analyses identified COPG1 as selectively essential in multiple cancers, and its loss was associated with increased drug sensitivity. Functional validation in hepatocellular carcinoma revealed that COPG1 knockdown impaired malignant phenotypes and reduced tumorigenicity in vivo. Mechanistically, COPG1 depletion induced Golgi disruption and ER stress, increased ROS production, and suppressed PI3K–AKT signaling, thereby sensitizing cells to sorafenib and doxorubicin. Collectively, this pan-cancer analysis reveals the context-dependent roles of coatomer subunits and identifies COPG1 as a novel oncogenic driver and potential therapeutic target in HCC, mediating chemoresistance through redox modulation and PI3K–AKT pathway inhibition.