AGR2 expression as a predictive biomarker for therapy response in esophageal squamous cell carcinoma

AGR2: The Covert Driver and New Dawn of Hepatobiliary and Pancreatic Cancer Treatment

The anterior gradient protein 2 (AGR2) plays a crucial role in facilitating the formation of protein disulfide bonds within the endoplasmic reticulum (ER). Research suggests that AGR2 can function as an oncogene, with its heightened expression linked to the advancement of hepatobiliary and pancreatic cancers through invasion and metastasis. Notably, AGR2 not only serves as a pro-oncogenic agent but also as a downstream targeting protein, indirectly fostering cancer progression. This comprehensive review delves into the established functions and expression patterns of AGR2, emphasizing its pivotal role in cancer progression, particularly in hepatobiliary and pancreatic malignancies. Furthermore, AGR2 emerges as a potential cancer prognostic marker and a promising target for immunotherapy, offering novel avenues for the treatment of hepatobiliary and pancreatic cancers and enhancing patient outcomes.

The Expression of TP63 as a Biomarker of Early Recurrence in Resected Esophageal Squamous Cell Carcinoma after Neoadjuvant Chemoradiotherapy

Esophageal cancer ranks among the ten most common cancers worldwide. Despite the adoption of neoadjuvant concurrent chemoradiotherapy (nCCRT) followed by surgery as the standard treatment approach in recent years, the local recurrence rate remains high. In this study, we employed RNA-seq to investigate distinctive gene expression profiles in esophageal squamous cell carcinoma (ESCC) with or without recurrence following a standard treatment course. Our findings indicate that recurrent ESCC exhibits heightened keratinizing and epidermis development activity compared to non-recurrent ESCC. We identified TP63 as a potential candidate for distinguishing clinical outcomes. Furthermore, immunohistochemistry confirmed the trend of TP63 overexpression in ESCC recurrence. Patients with elevated TP63 expression had poorer overall survival and lower 3-year recurrence-free survival. This study underscores the potential of TP63 as a biomarker for detecting cancer recurrence and suggests its role in guiding future treatment options.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Molecular Signatures Integral to Natural Reprogramming in the Pigment Epithelium Cells after Retinal Detachment in Pleurodeles waltl

The reprogramming of retinal pigment epithelium (RPE) cells into retinal cells (transdifferentiation) lies in the bases of retinal regeneration in several Urodela. The identification of the key genes involved in this process helps with looking for approaches to the prevention and treatment of RPE-related degenerative diseases of the human retina. The purpose of our study was to examine the transcriptome changes at initial stages of RPE cell reprogramming in adult newt Pleurodeles waltl. RPE was isolated from the eye samples of day 0, 4, and 7 after experimental surgical detachment of the neural retina and was used for a de novo transcriptome assembly through the RNA-Seq method. A total of 1019 transcripts corresponding to the differently expressed genes have been revealed in silico: the 83 increased the expression at an early stage, and 168 increased the expression at a late stage of RPE reprogramming. We have identified up-regulation of classical early response genes, chaperones and co-chaperones, genes involved in the regulation of protein biosynthesis, suppressors of oncogenes, and EMT-related genes. We revealed the growth in the proportion of down-regulated ribosomal and translation-associated genes. Our findings contribute to revealing the molecular mechanism of RPE reprogramming in Urodela.