The Overexpression of Keratin 23 Promotes Migration of Ovarian Cancer via Epithelial-Mesenchymal Transition

Investigating Snake-Venom-Induced Dermonecrosis and Inflammation Using an Ex Vivo Human Skin Model

Snakebite envenoming is a neglected tropical disease that causes >100,000 deaths and >400,000 cases of morbidity annually. Despite the use of mouse models, severe local envenoming, defined by morbidity-causing local tissue necrosis, remains poorly understood, and human-tissue responses are ill-defined. Here, for the first time, an ex vivo, non-perfused human skin model was used to investigate temporal histopathological and immunological changes following subcutaneous injections of venoms from medically important African vipers (Echis ocellatus and Bitis arietans) and cobras (Naja nigricollis and N. haje). Histological analysis of venom-injected ex vivo human skin biopsies revealed morphological changes in the epidermis (ballooning degeneration, erosion, and ulceration) comparable to clinical signs of local envenoming. Immunostaining of these biopsies confirmed cell apoptosis consistent with the onset of necrosis. RNA sequencing, multiplex bead arrays, and ELISAs demonstrated that venom-injected human skin biopsies exhibited higher rates of transcription and expression of chemokines (CXCL5, MIP1-ALPHA, RANTES, MCP-1, and MIG), cytokines (IL-1β, IL-1RA, G-CSF/CSF-3, and GM-CSF), and growth factors (VEGF-A, FGF, and HGF) in comparison to non-injected biopsies. To investigate the efficacy of antivenom, SAIMR Echis monovalent or SAIMR polyvalent antivenom was injected one hour following E. ocellatus or N. nigricollis venom treatment, respectively, and although antivenom did not prevent venom-induced dermal tissue damage, it did reduce all pro-inflammatory chemokines, cytokines, and growth factors to normal levels after 48 h. This ex vivo skin model could be useful for studies evaluating the progression of local envenoming and the efficacy of snakebite treatments.

Keratin gene signature expression drives epithelial-mesenchymal transition through enhanced TGF-β signaling pathway activation and correlates with adverse prognosis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) stands as the foremost histological subtype of non-small-cell lung cancer, accounting for approximately 40% of all lung cancer diagnoses. However, there remains a critical unmet need to enhance the prediction of clinical outcomes and therapy responses in LUAD patients. Keratins (KRTs), serving as the structural components of the intermediate filament cytoskeleton in epithelial cells, play a crucial role in the advancement of tumor progression. This study investigated the prognostic significance of the KRT family gene and developed a KRT gene signature (KGS) for prognostic assessment and treatment guidance in LUAD.

Methods

Transcriptome profiles and associated clinical details of LUAD patients were meticulously gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The KGS score was developed based on the expression of five prognostic KRT genes (KRT7, KRT8, KRT17, KRT18, and KRT80), and the upper quartile of the KGS score was chosen as the cutoff. The Kaplan-Meier method was evaluated to compare survival outcomes between KGS-high and KGS-low groups. The underlying mechanism was further investigated by GSEA, GSVA, and other bioinformatic algorithms.

Results

High expression of the KGS signature exhibited a robust association with poorer overall survival (OS) in the TCGA-LUAD dataset (HR: 1.81; 95% CI: 1.35-2.42, P = 0.00011). The association was further corroborated in three external GEO cohorts, including GSE31210 (HR: 3.31; 95% CI: 1.7-6.47, P = 0.00017), GSE72094 (HR: 1.95; 95% CI: 1.34-2.85, P = 0.00057) and GSE26939 (HR: 3.19; 95% CI: 1.74-5.84, P < 0.0001). Interestingly, KGS-high tumors revealed enrichments in TGF-β and WNT-β catenin signaling pathways, exhibited heightened activation of the epithelial-mesenchymal transition (EMT) pathway and proved intensified tumor stemness compared to their KGS-low counterparts. Additionally, KGS-high tumor cells exhibited increased sensitivity to several targeted agents, including gefitinib, erlotinib, lapatinib, and trametinib, in comparison to KGS-low cells.

Conclusion

This study developed a KGS score that independently predicts the prognosis in LUAD. High expression of KGS score, accompanied by upregulation of TGF-β and WNT-β catenin signaling pathways, confers more aggressive EMT and tumor progression.

Loss of keratin 23 enhances growth inhibitory effect of melatonin in gastric cancer

To investigate the effect of keratin 23 (KRT23) on the anticancer activity of melatonin (MLT) against gastric cancer (GC) cells, microarray analysis was applied to screen differentially expressed genes in AGS GC cells following MLT treatment. Western blotting was used to detect the expression of KRT23 in GC cells and normal gastric epithelial cell line GES‑1. KRT23 knockout was achieved by CRISPR/Cas9. Assays of cell viability, colony formation, cell cycle, electric cell‑substrate impedance sensing and western blotting were conducted to reveal the biological functions of KRT23‑knockout cells without or with MLT treatment. Genes downregulated by MLT were enriched in purine metabolism, pyrimidine metabolism, genetic information processing and cell cycle pathway. Expression levels of KRT23 were downregulated by MLT treatment. Expression levels of KRT23 in AGS and SNU‑216 GC cell lines were significantly higher compared with normal gastric epithelial cell line GES‑1. KRT23 knockout led to reduced phosphorylation of ERK1/2 and p38, arrest of the cell cycle and inhibition of GC cell proliferation. Moreover, KRT23 knockout further enhanced the inhibitory activity of MLT on the tumor cell proliferation by inhibiting the phosphorylation of p38/ERK. KRT23 knockout contributes to the antitumor effects of MLT in GC via suppressing p38/ERK phosphorylation. In the future, KRT23 might be a potential prognostic biomarker and a novel molecular target for GC.

Transcriptomic Analysis of the Aged Nulliparous Mouse Ovary Suggests a Stress State That Promotes Pro-Inflammatory Lipid Signaling and Epithelial Cell Enrichment

Ovarian cancer (OC) incidence and mortality peaks at post-menopause while OC risk is either reduced by parity or increased by nulliparity during fertile life. The long-term effect of nulliparity on ovarian gene expression is largely unknown. In this study, we describe a bioinformatic/data-mining analysis of 112 coding genes upregulated in the aged nulliparous (NP) mouse ovary compared to the aged multiparous one as reference. Canonical gene ontology and pathway analyses indicated a pro-oxidant, xenobiotic-like state accompanied by increased metabolism of inflammatory lipid mediators. Up-regulation of typical epithelial cell markers in the aged NP ovary was consistent with synchronized overexpression of Cldn3, Ezr, Krt7, Krt8 and Krt18 during the pre-neoplastic phase of mOSE cell cultures in a former transcriptome study. In addition, 61/112 genes were upregulated in knockout mice for Fshr and for three other tumor suppressor genes (Pten, Cdh1 and Smad3) known to regulate follicular homeostasis in the mammalian ovary. We conclude that the aged NP ovary displays a multifaceted stress state resulting from oxidative imbalance and pro-inflammatory lipid signaling. The enriched epithelial cell content might be linked to follicle depletion and is consistent with abundant clefts and cysts observed in aged human and mouse ovaries. It also suggests a mesenchymal-to-epithelial transition in the mOSE of the aged NP ovary. Our analysis suggests that in the long term, nulliparity worsens a variety of deleterious effects of aging and senescence thereby increasing susceptibility to cancer initiation in the ovary.

From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology

Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.

CAV1 and KRT5 are potential targets for prostate cancer

Prostate cancer is the most common malignant tumor of male urogenital system that occurs in prostate epithelium. However, relationship between CAV1 and KRT5 and prostate cancer remains unclear. The prostate cancer datasets GSE114740 and GSE200879 were downloaded from Gene Expression Omnibus generated by GPL11154 and GPL32170. De-batch processing was performed, differentially expressed genes (DEGs) were screened, and weighted gene co-expression network analysis. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis. Gene expression heat map was drawn and immune infiltration analysis was performed. Comparative toxicogenomics database analysis were performed to find the disease most related to core gene. In addition, the cell experiment was performed to verify the role of CAV1 and KRT5 by western blot. Divided into 4 groups: control, prostate cancer, prostate cancer-over expression, and prostate cancer- knock out. TargetScan screened miRNAs that regulated central DEGs; 770 DEGs were identified. According to Gene Ontology analysis, they were mainly concentrated in actin binding and G protein coupled receptor binding. In Kyoto Encyclopedia of Gene and Genome analysis, they were mainly concentrated in PI3K-Akt signal pathway, MAPK signal pathway, and ErbB signal pathway. The intersection of enrichment terms of differentially expressed genes and GOKEGG enrichment terms was mainly concentrated in ErbB signaling pathway and MAPK signaling pathway. Three important modules were generated. The protein-protein interaction network obtained 8 core genes (CAV1, BDNF, TGFB3, FGFR1, PRKCA, DLG4, SNAI2, KRT5). Heat map of gene expression showed that core genes (CAV1, TGFB3, FGFR1, SNAI2, KRT5) are highly expressed in prostate cancer tissues and low in normal tissues. Comparative toxicogenomics database analysis showed that core genes (CAV1, TGFB3, FGFR1, SNAI2, KRT5) were associated with prostate tumor, cancer, tumor metastasis, necrosis, and inflammation. CAV1 and KRT5 are up-regulated in prostate cancer. CAV1 and KRT5 are highly expressed in prostate cancer. The higher expression of CAV1 and KRT5, the worse prognosis.

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein-protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.

TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5

To investigate the specific role of TRIM29 in colon cancer progression, bioinformatic analysis was performed on TRIM29. Colon cancer tissues were collected and colon cancer cells were cultured for further experiments. Cell viability and proliferation were determined using CCK-8, colony formation, and EDU staining assays. The mRNA and protein levels of TRIM29 and KRT5 were determined using quantitative real-time PCR and western blotting, respectively. The interaction between TRIM29 and KRT5 was detected using a co-immunoprecipitation (CO-IP) assay. Cycloheximide treatment was performed to analyse the stability of KRT5. TRIM29 was upregulated in colon cancer tissues and cells. TRIM29 knockdown decreased the cell viability and proliferation and ubiquitination levels of KRT5 and enhanced the protein stability and expression of KRT5. The CO-IP assay confirmed that TRIM29 and KRT5 binded to each other. KRT5 knockdown neutralises the inhibitory effect of sh-TRIM29 on colon cancer cell growth and TRIM29 knockdown prevented the proliferation of colon cancer cells by decreasing ubiquitination of KRT5, which enhanced the protein stability and expression of KRT5 in cancer cells. Thus, targeting TRIM29-mediated ubiquitination levels of KRT5 might be a new direction for colon cancer therapy.

A Kaleidoscope of Keratin Gene Expression and the Mosaic of Its Regulatory Mechanisms

Keratins are a family of intermediate filament-forming proteins highly specific to epithelial cells. A combination of expressed keratin genes is a defining property of the epithelium belonging to a certain type, organ/tissue, cell differentiation potential, and at normal or pathological conditions. In a variety of processes such as differentiation and maturation, as well as during acute or chronic injury and malignant transformation, keratin expression undergoes switching: an initial keratin profile changes accordingly to changed cell functions and location within a tissue as well as other parameters of cellular phenotype and physiology. Tight control of keratin expression implies the presence of complex regulatory landscapes within the keratin gene loci. Here, we highlight patterns of keratin expression in different biological conditions and summarize disparate data on mechanisms controlling keratin expression at the level of genomic regulatory elements, transcription factors (TFs), and chromatin spatial structure.

Transcriptome Analysis Reveals the Immune Infiltration Profiles in Cervical Cancer and Identifies KRT23 as an Immunotherapeutic Target

Cervical cancer (CC) is one of the most common malignancies in women worldwide. Dismal prognosis rates have been associated with conventional therapeutic approaches, emphasizing the need for new strategies. Recently, immunotherapy has been used to treat various types of solid tumors, and different subtypes of the tumor microenvironment (TME) are associated with diverse responses to immunotherapy. Accordingly, understanding the complexity of the TME is pivotal for immunotherapy. Herein, we used two methods, “ssGSEA” and “xCell,” to identify the immune profiles in CC and comprehensively assess the relationship between immune cell infiltration and genomic alterations. We found that more adaptive immune cells were found infiltrated in tumor tissues than in normal tissues, whereas the opposite was true for innate cells. Consensus clustering of CC samples based on the number of immune cells identified four clusters with different survival and immune statuses. Then, we subdivided the above four clusters into “hot” and “cold” tumors, where hot tumors exhibited higher immune infiltration and longer survival time. Enrichment analyses of differentially expressed genes (DEGs) revealed that the number of activated immune signaling pathways was higher in hot tumors than that in cold tumors. Keratin, type I cytoskeletal 23 (KRT23), was upregulated in cold tumors and negatively correlated with immune cell infiltration. In vitro experiments, real-time reverse transcription-quantitative polymerase chain reaction, cytometric bead arrays, and ELISA revealed that knockdown of KRT23 expression could promote the secretion of C-C motif chemokine ligand-5 and promote the recruitment of CD8⁺ T cells. We also constructed a model based on DEGs that exhibited a high predictive power for the survival of CC patients. Overall, our study provides deep insights into the immune cell infiltration patterns of CC. Moreover, KRT23 has huge prospects for application as an immunotherapeutic target. Finally, our model demonstrated a good predictive power for the prognosis of CC patients and may guide clinicians during immunotherapy.

Identification of Key Genes Associated with Progression and Prognosis of Bladder Cancer through Integrated Bioinformatics Analysis

Simple Summary

Bladder cancer is a heterogeneous disease with high recurrence rates. The current prognostication depends on tumor stage and grade and there is a need for predictive biomarkers that can distinguish between progressive versus non-progressive disease. We have identified a 3-gene signature panel having prognostic value in bladder cancer, which could aid in clinical decision making.

Abstract

Bladder cancer prognosis remains dismal due to lack of appropriate biomarkers that can predict its progression. The study aims to identify novel prognostic biomarkers associated with the progression of bladder cancer by utilizing three Gene Expression Omnibus (GEO) datasets to screen differentially expressed genes (DEGs). A total of 1516 DEGs were identified between non-muscle invasive and muscle invasive bladder cancer specimens. To identify genes of prognostic value, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A total of seven genes, including CDKN2A, CDC20, CTSV, FOXM1, MAGEA6, KRT23, and S100A9 were confirmed with strong prognostic values in bladder cancer and validated by qRT-PCR conducted in various human bladder cancer cells representing stage-specific disease progression. ULCAN, human protein atlas and The Cancer Genome Atlas datasets were used to confirm the predictive value of these genes in bladder cancer progression. Moreover, Kaplan–Meier analysis and Cox hazard ratio analysis were performed to determine the prognostic role of these genes. Univariate analysis performed on a validation set identified a 3-panel gene set viz. CDKN2A, CTSV and FOXM1 with 95.5% sensitivity and 100% specificity in predicting bladder cancer progression. In summary, our study screened and confirmed a 3-panel biomarker that could accurately predict the progression and prognosis of bladder cancer.