Construction of a novel model based on cell-in-cell-related genes and validation of KRT7 as a biomarker for predicting survival and immune microenvironment in pancreatic cancer

Single-cell transcriptome analysis identifies subclusters and signature with N-glycosylation in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is a prevalent gynecologic cancer, with worldwide increasing incidence and disease-associated mortality. N-glycosylation, a critical post-translational modification, has been implicated in cancer progression and immune response modulation. We aimed to elucidate the role of N-glycosylation-related genes on EC cell heterogeneity, prognosis, and immunotherapy response.

METHODS

Data from single-cell RNA sequencing (scRNA) of five patients with EC were acquired from the Gene Expression Omnibus (GEO) database. Nonnegative matrix factorization (NMF) was used to identify cell subtypes related to N-glycosylation from a scRNA matrix. Subsequently, a consensus prognostic signature by integrating 101 combinations of 10 machine learning algorithms. The response to immunotherapy in EC was further examined by multiple algorithms.

RESULTS

Our findings identified 11,020 differentially expressed genes (DEGs), of which 34 N-glycosylation-related DEGs were remarkably associated with overall survival (OS) in EC. Single-cell RNA sequencing analysis revealed 30,233 cells divided into eight clusters, with T cells and epithelial cells showing distinct functional characteristics. NMF clustering further classified malignant cells into four subtypes: N-glycosylation-C0, Glycosphingolipid-C1, O-GalNAc-C2, and Elongation-C3. The O-GalNAc-C2 subtype exhibited the highest metabolic pathway activity and activation of transcription factors SOX4, JUND, and FOS. Additionally, cell-cell interaction networks highlighted the MK signaling pathway as a critical mediator of intercellular communication. An integrated machine learning framework generated a prognostic model comprising eight DEGs (LAMC2, KRT7, IL32, KRT18, SERPINA1, PGR, AKAP12, EDN2), achieving an average C-index of 0.712 in training and validation cohorts. A low-risk score implies more significant immune cell infiltration and better response to immunotherapy.

CONCLUSIONS

Our study underscores the role of N-glycosylation-related genes in EC prognosis and immunotherapy response prediction, and may provide a basis for the development of targeted therapies and personalized treatment strategies.

Identification of prognostic and therapeutic biomarkers associated with macrophage and lipid metabolism in pancreatic cancer

Although macrophages and lipid metabolism significantly influence the progression of various cancers, their precise roles in pancreatic cancer (PC) remain unclear. This study focuses on identifying and validating biomarkers associated with macrophage-related genes (MRGs) and lipid metabolism-related genes (LMRGs), providing new targets and strategies for therapeutic intervention. This research utilized datasets from TCGA-PAAD, GSE62452, and GSE57495. Candidate genes were identified by overlapping differentially expressed genes with MRGs from WGCNA and LMRGs. Regression analyses were performed to pinpoint potential biomarkers and construct a risk model, which underwent evaluation. A nomogram was subsequently developed and validated. Additional analyses, including functional enrichment, somatic mutation profiling, immune landscape assessment, and RT-qPCR, were performed to investigate the underlying biological mechanisms in PC. The study identified ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT as biomarkers for PC. RT-qPCR results revealed reduced expression of ADH1A, ACACB, CD36, CERS4, PDE3B, and CRAT in tumor samples compared to adjacent tissues, whereas ALOX5 expression was significantly elevated in tumor samples. A risk model utilizing these biomarkers classified PC patients into high- and low-risk cohorts, with high-risk patients showing lower survival probabilities. Subsequently, risk score and N stage were identified as independent prognostic factors, leading to the development of a nomogram. Notably, both risk cohorts showed significant enrichment in the "cell cycle" pathway. Furthermore, TP53 mutations were prevalent in both high-risk (76%) and low-risk (50%) cohorts. Correlation analysis indicated that PVRL2 (an immunosuppressive factor), CD276 (an immunoactivator), and CCL20 (a chemotactic factor) had the highest positive correlation with the risk score. In this study, ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT were identified as biomarkers for PC, with their expression levels validated in clinical samples. These findings offered a potential theoretical foundation for developing targeted treatments for PC.

Biomarkers associated with cell-in-cell structure in kidney renal clear cell carcinoma based on transcriptome sequencing

Background

Kidney renal clear cell carcinoma (KIRC), the main histological subtype of renal cell carcinoma, has a high incidence globally. Cell-in-cell structures (CICs), as a cellular biological phenomenon, play pivotal roles in cell competition, immune evasion and tumor progression in the context of KIRC.

Methods

Data for this study were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were identified using the limma package. Enrichment analyses were performed using the clusterProfiler package. Support vector machine-recursive feature elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) regression, implemented via the caret and glmnet packages in R, were used to select biomarkers. The accuracy of these biomarkers was verified by using the receiver operating characteristic (ROC) curve as well as in vitro experiments (CCK-8 assay, wound healing assay, Transwell assay, and quantitative real-time PCR). The CIBERSORT algorithm was applied to explore the association between immune infiltration and the biomarkers. Further analysis explored the association between these biomarkers and clinicopathological characteristics of KIRC. For single-cell data, the Seurat package is used to read the sample data, and the SCTransform function is employed for normalization.

Results

This study identified 1,256 DEGs which enriched in T-cell immune system regulation processes. Five hub genes (CDKN2A, VIM, TGFB1, CTSS, and CDC20) were biomarkers with area under the curve (AUC) values > 0.8, indicating high predictive performance. In vitro validation experiments demonstrated that the expressions of all five biomarkers in KIRC cells were elevated, and the knockdown of CTSS could inhibit the migration and invasion of KIRC cells. Immune infiltration analysis showed higher proportions of T-cells and macrophages in tumor tissues. CDKN2A and CDC20 expressions correlated significantly with stage and grade, while TGFB1, CDKN2A, and CDC20 were highly expressed in proliferative tumor cells.

Conclusion

This study provides new biomarkers for KIRC, offering valuable insights into its developmental mechanisms for the research of CIC in this disease.

DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer

The existing gastric cancer (GC) risk prediction models based on biomarkers are limited. This study aims to identify new promising biomarkers for GC to develop a risk prediction model for effective assessment, screening, and early diagnosis. This study was conducted utilizing a large combined cohort for upper gastrointestinal cancer that was established in Hebei Province, China. General macro risk factors, Helicobacter pylori (H.pylori) infection status, and protein biomarkers were collected through questionnaire surveys and laboratory tests. Novel GC biomarkers were explored using data-independent acquisition (DIA) proteomics and enzyme-linked immunosorbent assay (ELISA). Multiple machine learning algorithms were used to identify key predictors for the GC risk prediction model, which was validated with an independent external cohort from multiple hospitals. A total of 530 participants aged 40 to 74 were analyzed, with 104 ultimately diagnosed with GC. Significant biomarkers in GC patients were identified by DIA combined ELISA, including elevated Keratin 7 (KRT7) and Mammary fibrostatin (SERPINB5) (P<0.001) and decreased Dickkopf-associated protein 3 (DKK3) (P<0.001). Factors such as sex, age, smoking status, alcohol consumption, family history of GC, H. pylori infection, DKK3 and SERPINB5 were used to create a multidimensional risk prediction model for GC. This model achieved an area under the curve (AUC) of 0.938 (95% confidence interval: 0.913-0.962). The risk prediction model developed in this study shows high accuracy and practical utility, serving as an effective preliminary screening tool for identifying high-risk individuals for GC.

Prognostic values of intracellular cell-related genes in esophageal cancer and their regulatory mechanisms

Esophageal cancer is a grave malignant condition. While radiotherapy, often in conjunction with chemotherapy, serves as a cornerstone in the management of locally advanced or metastatic cases, patient tolerance and treatment resistance frequently hinder its efficacy. Cell-in-cell structures, prevalent in various tumors, have been linked to prognosis. Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. Differentially expressed genes (DEGs) in ESCA samples were identified, with those related to intercellular structures designated cell-in-cell-related differential expression genes (CIC-related DEGs). Cox regression analysis was employed to identify prognostic genes, categorizing samples into high- and low-risk groups based on median risk scores. Validation was conducted using the GSE53624 risk model. Established methodologies included morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression validation, molecular docking, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) validation. Thirty-eight intersecting genes were identified between the disease and normal groups in ESCA samples. Stepwise multivariate Cox analysis pinpointed three prognostic genes: androgen receptor (AR), C-X-C motif chemokine ligand 8 (CXCL8), and epidermal growth factor receptor (EGFR). The risk model's applicability was confirmed in the GSE53624 dataset, revealing eight significantly different immune-related gene sets. Prognostic gene expression validation demonstrated significant differences between the disease and normal groups in both datasets. The proteins corresponding to the three prognostic genes interacted with gefitinib and osimertinib. RT-PCR results corroborated the differential expression of prognostic genes in esophageal cancer tissues. This study identified AR, CXCL8, and EGFR as prognostic genes and demonstrated their molecular interactions with gefitinib and osimertinib, providing a foundation for ESCA diagnosis and treatment.

The role of KRT7 in metastasis and prognosis of pancreatic cancer

PURPOSE

The aim of this study is to delve into the value of N6-Methyladenosine (m6A)-associated genes (MAGs) in pancreatic cancer (PC) prognosis.

METHODS

PC sequencing data and corresponding clinicopathological information were retrieved from GEO and TCGA databases. We filtered 19 MAGs in PC specimens and implemented functional annotation in biology. Later, the m6A modification pattern was stratified into m6Acluster A-B according to MAG expression levels, and further categorized into genecluster A-C based on differentially expressed genes between m6Acluster A and B. Next, a MAG-based prognostic prediction model was established by the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis. At last, the role of KRT7 in PC were explored.

RESULTS

We found m6Acluster A pattern presented enrichment pathways associated with cell apoptosis, proliferation, migration, and cancer pathways. Additionally, high-risk group displayed more dismal prognosis and a higher programmed death-ligand 1 expression. The survival prediction ability of the model was verified in three independent PC GEO datasets. KRT7 is the most momentous risk gene in the established prognostic model. Among 18 clinical samples, the KRT7 protein in the surviving patient samples is lower than that in the deceased patient samples. We also identified elevated expression of KRT7 in PC tumor tissues compared to normal tissues using GEPIA 2. Then, the metastasis of PC cells was promoted by overexpressed KRT7 in vitro and in vivo. And IGF2BP3 upregulated KRT7 by increasing the mRNA stability of KRT7.

CONCLUSIONS

The PPM built based on CXCL5, LY6K and KRT7 is an encouraging biomarker to define the prognosis. Additionally, IGF2BP3 promoted KRT7 by stabilizing mRNA of KRT7. And KRT7 promoted the metastasis of PC cells by promoting EMT.

Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma

Purpose

This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies.

Patients and Methods

The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR.

Results

In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients.

Conclusion

We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.

Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis

Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into cancer heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising publicly available cohorts and data generated by our research team, and established the Human Prostate Single cell Atlas (HuPSA) and Mouse Prostate Single cell Atlas (MoPSA) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution re-classified human PCa specimens, validating the presence of these novel subtypes. We then developed a user-friendly web application, "HuPSA-MoPSA" ( https://pcatools.shinyapps.io/HuPSA-MoPSA/ ), for visualizing gene expression across all newly established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background

Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models.

Methods

To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models.

Results

We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154).

Conclusion

The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

Clinical significance of KRT7 in bladder cancer prognosis

BACKGROUND

Typically, the overexpressed keratin 7 (KRT7) is considered a validated therapeutic target and prognosis marker in bladder cancer. However, the crucial roles of KRT7 in the clinical prognosis and immune microenvironment in bladder cancer remain unclear.

METHODS

Initially, the expression levels of KRT7 in public databases were analyzed that is,Tumor Immune Estimation Resource (TIMER) 2.0 and Gene Expression Profiling Interactive Analysis (GEPIA). Further, the clinical tissue samples from patients (n = 10 pairs) were collected to confirm the expression trends of KRT7 and detected by immunohistochemistry (IHC) analysis. Meanwhile, the relationship between KRT7 and the prognosis of bladder cancer patients was analyzed by Kaplan-Meier plotter estimation and Cox regression analysis. Finally, TIMER 2.0 and IHC staining analyses were performed to calculate the infiltration abundances of three kinds of immune cells in eligible bladder tumor samples.

RESULTS

The TIMER 2.0 and GEPIA datasets suggested the differences in the expression levels of KRT7 in tumors, in which KRT7 was significantly upregulated in bladder cancer. The KRT7 expression was closely associated with patients' gender, tumor histologic subtypes, T status, and American Joint Committee on Cancer stages. Notably, the increased KRT7 indicated poor overall survival and disease-free survival rates. Moreover, KRT7 expression could be responsible for immune infiltration in the cancer microenvironment of the bladder. Finally, the high expression level of KRT7 increased the presence of regulatory T cells (Tregs) but reduced the infiltration of CD8+ T and natural killer cells.

CONCLUSION

KRT7 as a biomarker potentiated the prediction of bladder cancer prognosis and the immune microenvironment.

Immune-related signature identifies IL1R2 as an immunological and prognostic biomarker in pancreatic cancer

Objective

Pancreatic cancer is one of the most aggressive malignancies, a robust prognostic signature and novel biomarkers are urgently needed for accurate stratification of the patients and optimization of clinical decision-making.

Methods

A list of bioinformatic analysis were applied in public dataset to construct an immune-related signature. Furthermore, the most pivotal gene in the signature was identified. The potential mechanism of the core gene function was revealed through GSEA, CIBERSORT, ESTIMATE, immunophenoscore (IPS) algorithm, single-cell analysis, and functional experiment.

Results

An immune-related prognostic signature and associated nomogram were constructed and validated. Among the genes constituting the signature, interleukin 1 receptor type II (IL1R2) was identified as the gene occupying the most paramount position in the risk signature. Meanwhile, knockdown of IL1R2 significantly inhibited the proliferation, invasion, and migration ability of pancreatic cancer cells. Additionally, high IL1R2 expression was associated with reduced CD8+ T cell infiltration in pancreatic cancer microenvironment, which may be due to high programmed cell death-ligand-1 (PD-L1) expression in cancer cells. Finally, the IPS algorithm proved that patients with high IL1R2 expression possessed a higher tumor mutation burden and a higher probability of benefiting from immunotherapy.

Conclusion

In conclusion, our study constructed an efficient immune-related prognostic signature and identified the key role of IL1R2 in the development of pancreatic cancer, as well as its potential to serve as a biomarker for immunotherapy efficacy prediction for pancreatic cancer.

PANoptosis subtypes predict prognosis and immune efficacy in gastric cancer

PANoptosis is a form of inflammatory programmed cell death that is regulated by the PANoptosome. This PANoptosis possesses key characteristics of pyroptosis, apoptosis, and necroptosis, yet cannot be fully explained by any of these cell death modes. The unique nature of this cell death mechanism has garnered significant interest. However, the specific role of PANoptosis-associated features in gastric cancer (GC) is still uncertain. Patients were categorized into different PAN subtypes based on the expression of genes related to the PANoptosome. We conducted a systematic analysis to investigate the variations in prognosis and tumor microenvironment (TME) among these subtypes. Furthermore, we developed a risk score, called PANoptosis-related risk score (PANS), which is constructed from genes associated with the PANoptosis. We comprehensively analyzed the correlation between PANS and GC prognosis, TME, immunotherapy efficacy and chemotherapeutic drug sensitivity. Additionally, we performed in vitro experiments to validate the impact of Keratin 7 (KRT7) on GC. We identified two PAN subtypes (PANcluster A and B). PANoptosome genes were highly expressed in PANcluster A. PANcluster A has the characteristics of favorable prognosis, abundant infiltration of anti-tumor lymphocytes, and sensitivity to immunotherapy, thus it was categorized as an immune-inflammatory type. Meanwhile, our constructed PANS can effectively predict the prognosis and immune efficacy of GC. Patients with low PANS have a good prognosis, and have the characteristics of high tumor mutation load (TMB), high microsatellite instability (MSI), low tumor purity and sensitivity to immunotherapy. In addition, PANS can also identify suitable populations for different chemotherapy drugs. Finally, we confirmed that KRT7 is highly expressed in GC. Knocking down the expression of KRT7 significantly weakens the proliferation and migration abilities of GC cells. The models based on PANoptosis signature help to identify the TME features of GC and can effectively predict the prognosis and immune efficacy of GC. Furthermore, the experimental verification results of KRT7 provide theoretical support for anti-tumor treatment.

Cancer-Associated Fibroblast Induces Acinar-to-Ductal Cell Transdifferentiation and Pancreatic Cancer Initiation Via LAMA5/ITGA4 Axis

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma (PDAC) is characterized by desmoplastic stroma surrounding most tumors. Activated stromal fibroblasts, namely cancer-associated fibroblasts (CAFs), play a major role in PDAC progression. We analyzed whether CAFs influence acinar cells and impact PDAC initiation, that is, acinar-to-ductal metaplasia (ADM). ADM connection with PDAC pathophysiology is indicated, but not yet established. We hypothesized that CAF secretome might play a significant role in ADM in PDAC initiation.

METHODS

Mouse and human acinar cell organoids, acinar cells cocultured with CAFs and exposed to CAF-conditioned media, acinar cell explants, and CAF cocultures were examined by means of quantitative reverse transcription polymerase chain reaction, RNA sequencing, immunoblotting, and confocal microscopy. Data from liquid chromatography with tandem mass spectrometry analysis of CAF-conditioned medium and RNA sequencing data of acinar cells post-conditioned medium exposure were integrated using bioinformatics tools to identify the molecular mechanism for CAF-induced ADM. Using confocal microscopy, immunoblotting, and quantitative reverse transcription polymerase chain reaction analysis, we validated the depletion of a key signaling axis in the cell line, acinar explant coculture, and mouse cancer-associated fibroblasts (mCAFs).

RESULTS

A close association of acino-ductal markers (Ulex europaeus agglutinin 1, amylase, cytokeratin-19) and mCAFs (α-smooth muscle actin) in LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1Cre (KPC) and LSL-KrasG12D/+; Pdx1Cre (KC) autochthonous progression tumor tissue was observed. Caerulein treatment-induced mCAFs increased cytokeratin-19 and decreased amylase in wild-type and KC pancreas. Likewise, acinar-mCAF cocultures revealed the induction of ductal transdifferentiation in cell line, acinar-organoid, and explant coculture formats in WT and KC mice pancreas. Proteomic and transcriptomic data integration revealed a novel laminin α5/integrinα4/stat3 axis responsible for CAF-mediated acinar-to-ductal cell transdifferentiation.

CONCLUSIONS

Results collectively suggest the first evidence for CAF-influenced acino-ductal phenotypic switchover, thus highlighting the tumor microenvironment role in pancreatic carcinogenesis inception.

KRT80 Promotes Lung Adenocarcinoma Progression and Serves as a Substrate for VCP

Background: Keratin 80(KRT80) encodes a type II intermediate filament protein, known for maintaining cell integrity of cells and its involvement in the tumorigenesis and progression of various cancers. However, comprehensive research on its relevance to lung adenocarcinoma remains limited. Methods: In this study, we utilized multiple databases to investigate the transcriptional expression of KRT80 and its correlation with clinicopathological features. A range of assays, including the Cell Counting Kit 8 assay, colony formation assay, cell migration assay, and flow cytometry, were employed to elucidate the impact of KRT80 on the malignant behavior of lung adenocarcinoma. Immunoprecipitation and mass spectrometry were also used to identify putative genes interacting with KRT80. Results: The expression of KRT80 was elevated in lung adenocarcinoma and patients with high levels of KRT80 expression had poor clinical outcomes. Silencing KRT80 suppressed cell viability, and migration, while overexpression had the opposite effect. In addition, Immunoprecipitation and mass spectrometry revealed an interaction between KRT80 and valosin-containing protein (VCP), with VCP knockdown reducing the stability of KRT80 protein. Overexpression of KRT80 mitigated the inhibitory effect of VCP knockdown to some extent. Conclusion: Our findings collectively suggest that KRT80 is a promising diagnostic and prognostic indicator for lung adenocarcinoma. Additionally, the interaction between KRT80 and VCP plays a crucial role in the progression of lung adenocarcinoma, which implies that KRT80 is a promising therapeutic target.

Emerging insights into keratin 7 roles in tumor progression and metastasis of cancers

Keratin 7 (KRT7), also known as cytokeratin-7 (CK-7) or K7, constitutes the principal constituent of the intermediate filament cytoskeleton and is primarily expressed in the simple epithelia lining the cavities of the internal organs, glandular ducts, and blood vessels. Various pathological conditions, including cancer, have been linked to the abnormal expression of KRT7. KRT7 overexpression promotes tumor progression and metastasis in different human cancers, although the mechanisms of these processes caused by KRT7 have yet to be established. Studies have indicated that the suppression of KRT7 leads to rapid regression of tumors, highlighting the potential of KRT7 as a novel candidate for therapeutic interventions. This review aims to delineate the various roles played by KRT7 in the progression and metastasis of different human malignancies and to investigate its prognostic significance in cancer treatment. Finally, the differential diagnosis of cancers based on the KRT7 is emphasized.

Improving the prognosis of pancreatic cancer: insights from epidemiology, genomic alterations, and therapeutic challenges

Pancreatic cancer, notorious for its late diagnosis and aggressive progression, poses a substantial challenge owing to scarce treatment alternatives. This review endeavors to furnish a holistic insight into pancreatic cancer, encompassing its epidemiology, genomic characterization, risk factors, diagnosis, therapeutic strategies, and treatment resistance mechanisms. We delve into identifying risk factors, including genetic predisposition and environmental exposures, and explore recent research advancements in precursor lesions and molecular subtypes of pancreatic cancer. Additionally, we highlight the development and application of multi-omics approaches in pancreatic cancer research and discuss the latest combinations of pancreatic cancer biomarkers and their efficacy. We also dissect the primary mechanisms underlying treatment resistance in this malignancy, illustrating the latest therapeutic options and advancements in the field. Conclusively, we accentuate the urgent demand for more extensive research to enhance the prognosis for pancreatic cancer patients.

Cell-in-cell structure in cancer: evading strategies from anti-cancer therapies

One of the regulated forms of cell death is the cell-in-cell (CIC) structure, in which a surviving cell is engulfed by another cell, a mechanism that causes the death of the engulfed cell by an adjacent cell. Several investigators have previously shown that the presence of CICs is an independent risk factor significantly associated with decreased survival in patients with various types of cancer. In this review, we summarize the role of CIC in the tumor microenvironment (TME), including changes and crosstalk of molecules and proteins in the surrounding CIC, and the role of these factors in contributing to therapeutic resistance acquisition. Moreover, CIC structure formation is influenced by the modulation of TME, which may lead to changes in cellular properties. Future use of CIC as a clinical diagnostic tool will require a better understanding of the effects of chemotherapy on CIC, biomarkers for each CIC formation process, and the development of automated CIC detection methods in tissue sections of tumor specimens.

The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis and in vitro validation

Pancreatic ductal adenocarcinoma (PDAC) is the leading cause of cancer-related mortality, primarily due to the abundance of cancer-associated fibroblasts (CAFs), depleted effector T cells, and increased tumor cell stemness; hence, there is an urgent need for efficient biomarkers with prognostic and therapeutic potential. Here, we identified BHLHE40 as a promising target for PDAC through comprehensive analysis and weighted gene coexpression network analysis of RNA sequencing data and public databases, taking into account the unique characteristics of PDAC such as cancer-associated fibroblasts, infiltration of effector T cells, and tumor cell stemness. Additionally, we developed a prognostic risk model based on BHLHE40 and three other candidate genes (ITGA2, ITGA3, and ADAM9) to predict outcomes in PDAC patients. Furthermore, we found that the overexpression of BHLHE40 was significantly associated with T stage, lymph node metastasis, and American Joint Committee on Cancer (AJCC) stage in a cohort of 61 PDAC patients. Moreover, elevated expression levels of BHLHE40 were validated to promote epithelial-mesenchymal transition (EMT) and stemness-related proteins in BXPC3 cell lines. Compared to the parent cells, BXPC3 cells with BHLHE40 overexpression showed resistance to anti-tumor immunity when co-cultured with CD8⁺ T cells. In summary, these findings suggest that BHLHE40 is a highly effective biomarker for predicting prognosis in PDAC and holds great promise as a target for cancer therapy.