Browsing by Author "Contreras, Sebastián"
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Publication Connexin46 in the nucleus of cancer cells: a possible role as transcription modulator(2025) Fernández, Ainoa; Orellana, Viviana; Llanquinao, Jesús; Nuñez, Gonzalo; Perez Moreno, Pablo; Contreras, Sebastián; Martin, Alberto; Mammano, Fabio; Alfaro, Ivan; Calderón, Juan; Stehberg, Jimmy; Sáez, Mauricio; Retamal, Mauricio A.Background: Oncogenes drive cancer progression, but few are active exclusively in tumor cells. Connexins (Cxs), traditionally recognized as ion channel proteins, can localize to the nucleus and regulate gene expression, playing key roles in both physiological and pathological processes. Cx46, once thought to be restricted to the eye lens, has been implicated in tumor growth, though its underlying mechanisms remain unclear. This study investigates the nuclear presence of Cx46 in cancer cells and its potential role as a transcriptional modulator. Methods: We employed ChIP-Seq, confocal immunofluorescence, and nuclear protein purification to assess Cx46 localization and DNA interactions. Functional assays were conducted to evaluate its effects on invasion, division, spheroid formation, and mesenchymal marker expression. Single-point mutations and molecular dynamics simulations were used to explore potential Cx46-DNA interactions. Results: Cx46 mRNA upregulation was found in a variety of tumors compared to adjacent healthy tissue. In HeLa cells, which do not express Cx46, its transfection promoted proliferation, invasion and self-renewal capacity, cancer stem cell traits and mesenchymal features. Consistently, in Sk-Mel-2, which naturally express Cx46, reduced Cx46 expression led to a decrease in the similar parameters. In HeLa cells, nuclear Cx46 was detected in two forms, full length 46 kDa and a 30 kDa fragment (GJA3-30 k), ChIP-Seq experiments revealed that Cx46 binds to the DNA at intergenic and promoter regions, leading to the activation of oncogenic pathways. Molecular dynamics simulations suggest that GJA3-30 k dimerizes in a RAD50-like structure, forming stable DNA complexes. Cx46 and in some cases GJA3-30 k were detected in the nuclei of multiple cancer cell lines, including prostate, breast and skin cancers. Conclusions: Our findings reveal a novel nuclear role for Cx46 in cancer, demonstrating its function as a transcriptional regulator and its potential as a therapeutic target.Item Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients(2022) Oróstica, Karen; Saez, Juan; De Santiago, Pamela; Rivas, Solange; Contreras, Sebastián; Navarro, Gonzalo; Asenjo, Juan; Olivera, Álvaro; Armisén, RicardoAbstract Background: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohortsof cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), sum‑marises the mutational and clinical profles of diferent subtypes of Lung Cancer (LC). Mutational and clinical signa‑ tures have been used independently for tumour typifcation and prediction of metastasis in LC patients. Is it then possible to achieve better typifcations and predictions when combining both data streams? Methods: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for diferent classes of patients. Using the TML and diferent sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classifcation models that calculate the likelihood of developing metastasis. Results: We found that LC patients diferent in age, smoking status, and tumour type had signifcantly diferent mean TMLs. Although TML was an informative feature, its efect was secondary to the "tumour stage" feature. However, its contribution to the classifcation is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest perfor‑mance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). Conclusions: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evi‑ dence of the need for comprehensive diagnostic tools for metastasis.