Recently, long noncoding RNAs (lncRNAs) were recognized as significant therapeutic targets in tumors. Our previous microarray analysis showed that lncRNA TCONS_000026334 expression was reduced in metastatic colorectal cancer (CRC) tissues. The objective of this study was to research the biological functions of TCONS_000026334 and the potential mechanism during the development of CRC. TCONS_00026334 transcription levels were detected in CRC tissues from 86 patients and different CRC cell lines. The clinical prognosis factors related to TCONS_00026334 expression were then analyzed. TCONS_000026334 was overexpressed from plasmid pcDNA3.1-TCONS_ 000026334 or knocked down using a small interfering RNA (siRNA). Furthermore, bioinformatics approach and luciferase reporter gene assays were utilized to search for candidate miRNAs of TCONS_00026334 and identify the downstream target genes. The results indicated that TCONS_00026334 expression in 86 CRC tissues was markedly lower than that in non-cancerous tissues. The aberrant expression of TCONS_00026334 correlated negatively with larger tumor size, distant metastasis, serological carcinoembryonic antigen level, and unfavorable survival of patients with CRC. TCONS_00026334 overexpression could inhibit the aggressive phenotypes of CRC in vitro and in vivo. Conversely, TCONS_00026334 silencing accelerated CRC cell proliferation and invasion. We then verified that TCONS_00026334 upregulated the expression level of TP53INP1, a target gene of miR-548n, via direct binding to miR-548n as a competing endogenous RNA. Taken together, our study showed that TCONS_00026334 acts as an anti-tumor and anti-metastatic gene by regulating the miR548n/TP53INP1 axis in the development of CRC.
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http://dx.doi.org/10.1002/cam4.3473 | DOI Listing |
J Exp Clin Cancer Res
January 2025
Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Background: Colorectal cancer (CRC) has high incidence and mortality rates, with severe prognoses during invasion and metastasis stages. Despite advancements in diagnostic and therapeutic technologies, the impact of the tumour microenvironment, particularly extracellular matrix (ECM) stiffness, on CRC progression and metastasis is not fully understood.
Methods: This study included 107 CRC patients.
Ann Clin Microbiol Antimicrob
January 2025
Department of Science and Environment, Roskilde University, Roskilde, Denmark.
Background: Highly frequent colorectal cancer (CRC) is predicted to have 3.2 million novel cases by 2040. Tumor microenvironment (TME) bacteriome and metabolites are proposed to be involved in CRC development.
View Article and Find Full Text PDFClin Adv Periodontics
January 2025
Department of Periodontics and Implantology, SRM Dental College and Hospital, Chennai, India.
Background: Newer generation platelet concentrates, such as advanced platelet-rich fibrin plus (A-PRF+) obtained following low-speed centrifugation concept and horizontal platelet-rich fibrin (H-PRF) obtained from swing out and bucket system, showed increased platelet entrapment and growth factor release in the in-vitro studies. This prospective study aimed to evaluate and compare the clinical outcomes of A-PRF+ and H-PRF membranes in the treatment of gingival recession defects. The objectives of this study were to compare the changes in the recession height (RH) and the mean root coverage percentage (MRC%) between and within the research groups.
View Article and Find Full Text PDFJ Exp Clin Cancer Res
January 2025
Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
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View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism.
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