Abstract:Molecular alterations in tumor-adjacent tissues have recently been recognized in some types of cancer. This phenomenon has not been studied in endometrial cancer. We aimed to analyze the expression of genes associated with cancer progression and metabolism in primary endometrial cancer samples and the matched tumor-adjacent tissues and in the samples of endometria from cancer-free patients with uterine leiomyomas. Paired samples of tumor-adjacent tissues and primary tumors from 49 patients with endometrial can… Show more
“…Our data indicate that the regulation of angiogenesis-related genes in EC with prognostically less favourable characteristics is not limited to T tissue alone but rather spreads onto the non-cancerous TA tissue of the surrounding endometrium. This is in accordance with the fact that TA tissues are often involved in the development and progression of the tumour [70][71][72]. TA tissue is a distinct tissue type that presents a unique intermediate state between healthy tissue and tumour tissue [72].…”
Section: Discussionsupporting
confidence: 80%
“…The neoplastic and non-neoplastic cells in the microenvironment communicate to produce a microenvironment favourable for the progression of endometrial carcinogenesis [43,45]. According to different lines of evidence, genomic data from non-cancerous TA tissue can independently predict cancer survival, and, in some cases, provide even superior performance relative to models based on tumour-derived data alone [71,73,74]. We found fewer differences between angiogenesisrelated gene expression in T versus TA tissue in higher stages and grades of EC, indicating that the progression of the tumour was not only related to the expression of AFs in the tumorous tissue but also to the expression in the TA tissue samples.…”
Endometrial cancer (EC) is an increasing health concern, with its growth driven by an angiogenic switch that occurs early in cancer development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins in EC tissues, and compared them with adjacent control tissues. We identified nine genes with significant differential expression and selected six additional antiangiogenic genes from prior research for validation on EC tissue in a cohort of 36 EC patients. Using machine learning, we built a prognostic model for EC, combining our data with The Cancer Genome Atlas (TCGA). Our results revealed a significant up-regulation of IL8 and LEP and down-regulation of eleven other genes in EC tissues. These genes showed differential expression in the early stages and lower grades of EC, and in patients without deep myometrial or lymphovascular invasion. Gene co-expressions were stronger in EC tissues, particularly those with lymphovascular invasion. We also found more extensive angiogenesis-related gene involvement in postmenopausal women. In conclusion, our findings suggest that angiogenesis in EC is predominantly driven by decreased antiangiogenic factor expression, particularly in EC with less favourable prognostic features. Our machine learning model effectively stratified EC based on gene expression, distinguishing between low and high-grade cases.
“…Our data indicate that the regulation of angiogenesis-related genes in EC with prognostically less favourable characteristics is not limited to T tissue alone but rather spreads onto the non-cancerous TA tissue of the surrounding endometrium. This is in accordance with the fact that TA tissues are often involved in the development and progression of the tumour [70][71][72]. TA tissue is a distinct tissue type that presents a unique intermediate state between healthy tissue and tumour tissue [72].…”
Section: Discussionsupporting
confidence: 80%
“…The neoplastic and non-neoplastic cells in the microenvironment communicate to produce a microenvironment favourable for the progression of endometrial carcinogenesis [43,45]. According to different lines of evidence, genomic data from non-cancerous TA tissue can independently predict cancer survival, and, in some cases, provide even superior performance relative to models based on tumour-derived data alone [71,73,74]. We found fewer differences between angiogenesisrelated gene expression in T versus TA tissue in higher stages and grades of EC, indicating that the progression of the tumour was not only related to the expression of AFs in the tumorous tissue but also to the expression in the TA tissue samples.…”
Endometrial cancer (EC) is an increasing health concern, with its growth driven by an angiogenic switch that occurs early in cancer development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins in EC tissues, and compared them with adjacent control tissues. We identified nine genes with significant differential expression and selected six additional antiangiogenic genes from prior research for validation on EC tissue in a cohort of 36 EC patients. Using machine learning, we built a prognostic model for EC, combining our data with The Cancer Genome Atlas (TCGA). Our results revealed a significant up-regulation of IL8 and LEP and down-regulation of eleven other genes in EC tissues. These genes showed differential expression in the early stages and lower grades of EC, and in patients without deep myometrial or lymphovascular invasion. Gene co-expressions were stronger in EC tissues, particularly those with lymphovascular invasion. We also found more extensive angiogenesis-related gene involvement in postmenopausal women. In conclusion, our findings suggest that angiogenesis in EC is predominantly driven by decreased antiangiogenic factor expression, particularly in EC with less favourable prognostic features. Our machine learning model effectively stratified EC based on gene expression, distinguishing between low and high-grade cases.
“…Recent findings have highlighted molecular changes in tumor-adjacent “normal” tissues in several types of cancer. For instance, the tissues surrounding endometrial cancer express higher levels of tumor-promoting proteins than the matched tumors do 24 . In hepatocellular carcinoma, peritumoral liver tissues were found to exhibit a specific metabolic phenotype by proteomic analysis 25 .…”
Although nontumor components play an essential role in colon cancer (CC) progression, the intercellular communication between CC cells and adjacent colonic epithelial cells (CECs) remains poorly understood. Here, we show that intact mitochondrial genome (mitochondrial DNA, mtDNA) is enriched in serum extracellular vesicles (EVs) from CC patients and positively correlated with tumor stage. Intriguingly, circular mtDNA transferred via tumor cell-derived EVs (EV-mtDNA) enhances mitochondrial respiration and reactive oxygen species (ROS) production in CECs. Moreover, the EV-mtDNA increases TGFβ1 expression in CECs, which in turn promotes tumor progression. Mechanistically, the intercellular mtDNA transfer activates the mitochondrial respiratory chain to induce the ROS-driven RelA nuclear translocation in CECs, thereby transcriptionally regulating TGFβ1 expression and promoting tumor progression via the TGFβ/Smad pathway. Hence, this study highlights EV-mtDNA as a major driver of paracrine metabolic crosstalk between CC cells and adjacent CECs, possibly identifying it as a potential biomarker and therapeutic target for CC.
“…Suppression of these enzymes can induce apoptosis, cell cycle arrest, and differentiation. TWIST1 is listed as a transcriptional regulator at CIDR +12 h: this gene is associated with cancer metabolism and regulates the epithelial-to-mesenchymal transition [ 100 ]. The role of these regulators is consistent with the top diseases and functions categories that include Cancer , Cellular growth and proliferation and Organismal injury and abnormalities , supporting the cellular growth essential to the increased endometrial thickening observed in the bovine follicular phase in both natural and induced oestrus [ 74 ].…”
During the oestrous cycle, the bovine endometrium undergoes morphological and functional changes, which are regulated by alterations in the levels of oestrogen and progesterone and consequent changes in gene expression. To clarify these changes before and after oestrus, RNA-seq was used to profile the transcriptome of oestrus-synchronized beef heifers. Endometrial samples were collected from 29 animals, which were slaughtered in six groups beginning 12 h after the withdrawal of intravaginal progesterone releasing devices until seven days post-oestrus onset (luteal phase). The groups represented proestrus, early oestrus, metoestrus and early dioestrus (luteal phase). Changes in gene expression were estimated relative to gene expression at oestrus. Ingenuity Pathway Analysis (IPA) was used to identify canonical pathways and functional processes of biological importance. A total of 5,845 differentially expressed genes (DEGs) were identified. The lowest number of DEGs was observed at the 12 h post-oestrus time point, whereas the greatest number was observed at Day 7 post-oestrus onset (luteal phase). A total of 2,748 DEGs at this time point did not overlap with any other time points. Prior to oestrus, Neurological disease and Organismal injury and abnormalities appeared among the top IPA diseases and functions categories, with upregulation of genes involved in neurogenesis. Lipid metabolism was upregulated before oestrus and downregulated at 48h post-oestrus, at which point an upregulation of immune-related pathways was observed. In contrast, in the luteal phase the Lipid metabolism and Small molecule biochemistry pathways were upregulated.
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