Endometrial Cancer-Adjacent Tissues Express Higher Levels of Cancer-Promoting Genes than the Matched Tumors

An update on nanoformulations with FDA approved drugs for female reproductive cancer

Female reproductive cancers, including ovarian, cervical, breast, gestational trophoblastic and endometrial cancer, present significant challenges in therapy and patient prognosis. Conventional chemotherapy often lacks selectivity, leading to systemic toxicity and reduced treatment efficacy. Nanotechnology has emerged as a promising approach to improve drug delivery and therapeutic outcomes. Encapsulation of FDA-approved drugs within nanocarriers such as liposomes, polymeric nanoparticles, and lipid nanoparticles enables controlled drug release, reduces off-target effects, and enhances drug accumulation at tumor sites. This targeted delivery minimizes damage to healthy tissues and improves patient survival rates. Additionally, nanoformulations facilitate combination therapy, overcoming drug resistance and maximizing therapeutic efficacy. Despite promising results, challenges like scalability, reproducibility, and regulatory approvals hinder widespread clinical applications. Developing personalized nanoformulations tailored to individual patient profiles offers potential for precision cancer therapy. This study explores the role of nanoformulations in enhancing the therapeutic potential of FDA-approved drugs for treating female reproductive cancers.

Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnosis and treatment, including surgery, chemotherapy, and immunotherapy, accurate clinical markers are still lacking. The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management.

METHOD

In this retrospective study, we used FFPE blocks of tissue samples from CRC patients at Augusta University (AU) to quantify a custom 15-gene panel. To differentiate the tumor and adjacent normal regions (NAT), H&E staining was utilized. For the quantification of transcripts, we used the NanoString nCounter platform. Kaplan-Meier and Log-rank tests were used to perform survival analyses. Several independent datasets were explored to validate the gene signature. Orthogonal analyses included single-cell profiling, differential gene expression, immune cell deconvolution, neoantigen prediction, and biological pathway assessment.

RESULTS

A 3-gene signature (GTF3A, PKM, and VEGFA) was found to be associated with overall survival in the AU cohort (HR = 2.26, 95% CI 1.05-4.84, p = 0.02, 93 patients), TCGA cohort (HR = 1.57, 95% CI 1.05-2.35, p < 0.02, 435 patients) and four other GEO datasets. Independent single-cell analysis identified relatively higher expression of the 3-gene signature in the tumor region. Differential analysis revealed dysregulated tissue inflammation, immune dysfunction, and neoantigen load of cell cycle processes among high-risk patients compared to low-risk patients.

CONCLUSION

We developed a 3-gene signature with the potential for prognostic and predictive clinical assessment of CRC patients. This gene-based stratification offers a cost-effective approach to personalized cancer management. Further research using similar methods could identify therapy-specific gene signatures to strengthen the development of personalized medicine for CRC patients.

Mitochondrial genome transfer drives metabolic reprogramming in adjacent colonic epithelial cells promoting TGFβ1-mediated tumor progression

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.

Transcriptomics analysis of the bovine endometrium during the perioestrus period

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.

Associations between Single Nucleotide Polymorphisms from the Genes of Chemokines and the CXCR2 Chemokine Receptor and an Increased Risk of Endometrial Cancer

Significant relationships with endometrial cancer were demonstrated, both for CCL2, CCL5, and CXCL8 chemokines and for the chemokine receptor CXCR2. The reported case-control study of genetic associations was designed to establish the role of selected single nucleotide polymorphisms (SNPs) of the CCL2, CCL5, CXCL8, and CXCR2 genes in the onset and progression of endometrial cancer. This study was conducted on 282 women, including 132 (46.8%) patients with endometrial cancer and 150 (53.2%) non-cancerous controls. The genotypes for CCL2 rs4586, CCL5 rs2107538 and rs2280789, CXCL8 rs2227532 and -738 T>A, and CXCR2 rs1126580 were determined, using PCR-RFLP assays. The AA homozygotes in CCL5 rs2107538 were associated with more than a quadruple risk of endometrial cancer (p ≤ 0.050). The GA heterozygotes in the CXCR2 SNP were associated with approximately threefold higher cancer risk (p ≤ 0.001). That association also remained significant after certain adjustments, carried out for age, diabetes mellitus, arterial hypertension, or endometrial thickness above 5 mm (p ≤ 0.050). The A-A haplotypes for the CCL5 polymorphisms and T-A-A haplotypes for the CCL2 and CCL5 SNPs were associated with about a twofold risk of endometrial cancer (p ≤ 0.050). In conclusion, CCL2 rs4586, CCL5 rs2107538 and rs2280789, and CXCR2 rs1126580 demonstrated significant associations with an increased risk of endometrial cancer.

Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling

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.

Radiomics and Molecular Classification in Endometrial Cancer (The ROME Study): A Step Forward to a Simplified Precision Medicine

Molecular/genomic profiling is the most accurate method to assess prognosis of endometrial cancer patients. Radiomic profiling allows for the extraction of mineable high-dimensional data from clinical radiological images, thus providing noteworthy information regarding tumor tissues. Interestingly, the adoption of radiomics shows important results for screening, diagnosis and prognosis, across various radiological systems and oncologic specialties. The central hypothesis of the prospective trial is that combining radiomic features with molecular features might allow for the identification of various classes of risks for endometrial cancer, e.g., predicting unfavorable molecular/genomic profiling. The rationale for the proposed research is that once validated, radiomics applied to ultrasonographic images would be an effective, innovative and inexpensive method for tailoring operative and postoperative treatment modalities in endometrial cancer. Patients with newly diagnosed endometrial cancer will have ultrasonographic evaluation and radiomic analysis of the ultrasonographic images. We will correlate radiomic features with molecular/genomic profiling to classify prognosis.