Development and validation of a hypoxia-related prognostic signature for breast cancer

A novel lineage-tracing tool reveals that hypoxic tumor cells drive tumor relapse after radiotherapy

PURPOSE

Tumor hypoxia imposes a main obstacle to the efficacy of anti-cancer therapy. Understanding the cellular dynamics of individual hypoxic cells before, during and post-treatment has been hampered by the technical inability to identify and trace these cells over time.

METHODS AND MATERIALS

Here, we present a novel lineage-tracing reporter for hypoxic cells based on the conditional expression of a HIF1a-CreERT2-UnaG biosensor that can visualize hypoxic cells in a time-dependent manner and trace the fate of hypoxic cells over time. We combine this system with multiphoton microscopy, flow cytometry, and immunofluorescence to characterize the role of hypoxic cells in tumor relapse after irradiation in H1299 tumor spheroids and in vivo xenografts.

RESULTS

We validate the reporter in monolayer cultures and we show that tagged cells colocalize in spheroids and human tumor xenografts with the hypoxic marker pimonidazole. We found that irradiation of H1299-HIFcreUnaG spheroids leads to preferential outgrowth of cells from the hypoxic core. Similarly, in xenografts tumors, although initially UnaG-positive-cells coincide with pimonidazole-positive tumor areas and they are merely quiescent, upon irradiation UnaG-positive cells enrich in regrowing tumors and are mainly proliferative.

CONCLUSIONS

Collectively, our data provide clear evidence that the hypoxic cells drive tumor relapse after irradiation.

Computational Pathology Detection of Hypoxia-Induced Morphologic Changes in Breast Cancer

Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of artificial intelligence in computational histopathology to evaluate hypoxia in breast cancer. Weakly supervised deep learning models can accurately detect morphologic changes associated with hypoxia in routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs). Our model, HypOxNet, was trained on H&E-stained WSIs from breast cancer primary sites (n = 1016) at ×40 magnification using data from The Cancer Genome Atlas. We used the Hypoxia Buffa signature to measure hypoxia scores, which ranged from -43 to 47, and stratified the samples into hypoxic and normoxic based on these scores. This stratification represented the weak labels associated with each WSI. HypOxNet achieved an average area under the curve of 0.82 on test sets, identifying significant differences in cell morphology between hypoxic and normoxic tissue regions. Importantly, once trained, the HypOxNet model requires only the readily available H&E-stained slides, making it especially valuable in low-resource settings where additional gene expression assays are not available. These artificial intelligence-based hypoxia detection models can potentially be extended to other tumor types and seamlessly integrated into pathology workflows, offering a fast, cost-effective alternative to molecular testing.

GRHPR, Targeted by miR-138-5p, Inhibits the Proliferation and Metastasis of Hepatocellular Carcinoma Through PI3K/AKT Signaling Pathway

Background: Hepatocellular carcinoma (HCC) is a highly aggressive cancer. This study elucidates the role of Glyoxylate reductase/hydroxypyruvate reductase (GRHPR) in HCC proliferation and metastasis, along with its molecular mechanism, and identifies miRNAs targeting GRHPR. Materials and Methods: Expression levels of GRHPR and miR-138-5p were assessed using real-time fluorescent quantitative polymerase chain reaction and Western blot techniques. Bioinformatic analysis was employed to identify miRNAs targeting GRHPR, and the results were confirmed via dual-luciferase reporter assays. HCC cell lines overexpressing GRHPR were established to investigate its roles in cell proliferation, migration, and invasion. The biological function of miR-138-5p targeting GRHPR in HCC cells was also evaluated. Furthermore, a xenograft mouse model was utilized to examine the in vivo functions of GRHPR. Results: GRHPR expression was downregulated in HCC, whereas miR-138-5p was upregulated. Overexpression of GRHPR suppressed HCC cell proliferation, migration, and invasion. Conversely, inhibition of GRHPR by miR-138-5p promoted HCC cell proliferation and invasive properties. MiR-138-5p was found to regulate Phosphoinositide 3-kinase (PI3K) and protein kinase B (AKT) phosphorylation levels by inhibiting GRHPR expression. Conclusion: This study highlights GRHPR's role as a tumor suppressor in HCC, with its function being regulated by miR-138-5p.

PPP1R14B-mediated phosphorylation enhances protein stability of RPS6KA1 to promote hepatocellular carcinoma tumorigenesis

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide with a poor clinical prognosis. Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an unidentified protein phosphatase 1 regulatory subunit that is associated with the occurrence and development of various cancers. Recently, PPP1R14B was found to contribute to paclitaxel resistance and cell progression in triple-negative breast cancer; however, the role of PPP1R14B in HCC is unknown. Here, we found that PPP1R14B was highly expressed in HCC tissues, which suggested a poor prognosis. Knockdown of PPP1R14B significantly inhibited the survival and tumorigenic ability of HCC cells, while overexpression of PPP1R14B had the opposite effects. Mechanistically, Ribosomal Protein S6 Kinase type 1(RPS6KA1) was identified as the target gene of PPP1R14B. PPP1R14B maintained the stability and phosphorylation of RPS6KA1, and positively regulated activation of the AKT/NF-κB pathway. Importantly, PPP1R14B-deficient tumor suppression could be partially restored by wild-type but not phosphorylated mutant RPS6KA1. Taken together, these findings shed light on the function and mechanism of PPP1R14B in HCC progression, indicating PPP1R14B is a promising molecular target for the treatment of HCC.

Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model

Background

Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.

Methods

We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts.

Results

The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with high MLTS scores exhibited increased tumor mutational burden, chromosomal instability, and poor survival outcomes. Single-cell RNA sequencing analysis further revealed higher MLTS scores in aneuploid tumor cells, suggesting a role in cancer progression. Immune profiling indicated distinct tumor microenvironment characteristics associated with MLTS scores, potentially guiding therapeutic decisions.

Conclusions

Our findings highlight the utility of MLTS as a robust prognostic biomarker for breast cancer. The ability of MLTS to predict patient outcomes and its association with key genomic and cellular features underscore its potential as a target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer

BACKGROUND

Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments.

MATERIALS AND METHODS

In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments.

RESULTS

In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions.

CONCLUSION

Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

ZC3H12A inhibits tumor growth and metastasis of breast cancer under hypoxic condition via the inactivation of IL-17 signaling pathway

Hypoxia is a major contributor to tumor microenvironment (TME) and metastasis in most solid tumors. We seek to screen hypoxia-related genes affecting metastasis in breast cancer and to reveal relative potential regulatory pathway. Based on gene expression profiling of GSE17188 dataset, differential expressed genes (DEGs) were identified between highly metastatic breast cancer cells under hypoxia and samples under normoxia. The protein-protein interaction (PPI) network was utilized to determine hub genes. The gene expression profiling interactive analysis database (GEPIA2) and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) were employed to quantify hub genes. Moreover, overexpression of zinc finger CCCH-type containing 12A (ZC3H12A) was performed both in breast cancer cells and xenograft mouse model to determine the role of ZC3H12A. We identified 134 DEGs between hypoxic and normoxic samples. Based on PPI analysis, 5 hub genes interleukin (IL)-6, GALN (GAL), CD22 molecule (CD22), ZC3H12A and TNF receptor associated factor 1 (TRAF1) were determined; the expression levels of TRAF1, IL-6, ZC3H12A and GAL were remarkably downregulated while CD22 was upregulated in breast cancer cells. Besides, patients with higher expression of ZC3H12A had favorable prognosis. Overexpression of ZC3H12A could inhibit metastasis and tumor growth of breast cancer; overexpression of ZC3H12A downregulated the expression of IL-17 signaling pathway-related proteins such as IL-17 receptor A (IL-17RA), IL-17A and nuclear factor κB activator 1 (Act1). This study reveals ZC3H12A and IL-17 signaling pathway as potential therapeutic targets for hypoxic breast cancer.

Identifying key genes related to the peritubular capillary rarefaction in renal interstitial fibrosis by bioinformatics

Renal interstitial fibrosis (RIF) is a key feature of progressive chronic kidney disease (CKD), characterized by tubular epithelial cell (TEC) hypoxia and peritubular capillary (PTC) rarefaction. However, the mechanisms underlying these processes remain poorly understood. To address this knowledge gap, we conducted a comparative transcriptome analysis of hypoxic and normoxic HK-2 cells, identifying 572 differentially expressed genes (DEGs). Subsequent Gene Ontology (GO), protein‒protein interaction (PPI) network, and hub gene analyses revealed significant enrichment of DEGs in the HIF-1 signaling pathway based on KEGG enrichment analysis. To further explore TEC modulation under hypoxic conditions, we performed chromatin immunoprecipitation (ChIP) sequencing targeting HIF-1α, identifying 2915 genes potentially regulated by HIF-1α. By comparing RNA sequencing and ChIP sequencing data, we identified 43 overlapping DEGs. By performing GO analysis and peak annotation with IGV, we identified two candidate molecules, VEGFA and BTG1, that are associated with angiogenesis and whose gene sequences were reliably bound by HIF-1α. Our study elucidates the molecular mechanisms underlying RIF, providing valuable insights for potential therapeutic interventions.

Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

BACKGROUND

Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated.

METHOD

The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted.

RESULT

An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies.

CONCLUSION

We established an ER Stress-based risk signature that could effectively predict EC patients' prognosis and their immune correlation.

Stromal-epithelial interaction induces GALNT14 in prostate carcinoma cells

Introduction

Cell-cell communication is an important process in healthy tissue but also gains enhanced attention regarding pathological tissue. To date, the tumor microenvironment is gradually brought into focus when studying tumorigenesis. In the prostate gland, stromal and epithelial cells greatly interact to maintain homeostasis or tissue integrity. This study focuses on an indirect communication via soluble factors.

Methods

To investigate the cell-cell interaction via soluble factors, the prostate carcinoma cell line LNCaP and the stromal primary cells p21 were co-cultured without direct contact and RNA was isolated at defined time points. Differences in gene expression were finally analyzed by RNA sequencing.

Results

RNA sequencing revealed a time-depending differential expression profile. Selected factors were subsequently characterized at molecular level and analyzed in human prostate tissue of different developmental stages as well as pathology. GALNT14 was one of the highest induced co-culture-specific genes in LNCaP cells. Detection in healthy tissue and BPH revealed an age-dependent decrease in GALNT14 expression. Moreover, in prostate carcinoma, GALNT14 expression heavily varied independent of the Gleason score.

Conclusion

Overall, this work provides a basis for further studies related to paracrine stromal-epithelial interaction in prostate carcinoma and highlights the importance of GALNT14.

VHL tumor suppressor as a novel potential candidate biomarker in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of endocrine cancer, with an increasing incidence worldwide. The treatment of PTC is currently the subject of clinical controversy, making it critically important to identify molecular markers that would help improve the risk stratification of PTC patients and optimize the therapeutic approach. The VHL tumor suppressor gene has been implicated in tumorigenesis of various types of carcinoma and linked with their aggressive biological behavior. The role of VHL in the origin and development of PTC has only recently begun to be revealed. In this narrative review we attempt to summarize the existing knowledge that implicates VHL in PTC pathogenesis and to outline its potential significance as a candidate molecular biomarker for the grouping of PTC patients into high and low risk groups.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

INTRODUCTION

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach.

MATERIALS AND METHODS

Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry.

RESULTS

Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues.

CONCLUSIONS

HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma

Cholangiocarcinoma (CHOL) is highly malignant and has a poor prognosis. This study is committed to creating a new prognostic model based on hypoxia related genes. Here, we established a novel tumor hypoxia-related prognostic model consisting of 6 hypoxia-related genes by univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict CHOL prognosis and then the risk score for each patient was calculated. The results showed that the patients with high-risk scores had poor prognosis compared with those with low-risk scores, which was verified as an independent predictor by multivariate analysis. The hypoxia-related prognostic model was validated in both TCGA and GEO cohorts and exhibited excellent performance in predicting overall survival in CHOL. The PPI results suggested that hypoxia-related genes involved in the model may play a central role in regulating the hypoxic state. In addition, the presence of IDH1 mutations in the high-risk group was high, and GSEA results showed that some metabolic pathways were upregulated, but immune response processes were generally downregulated. These factors may be potential reasons for the high-risk group with worse prognosis. The analysis of different immune regulation-related processes in the high- and low-risk groups revealed that the expression of genes related to immune checkpoints would show differences between these two groups. We further verified the expression of the oncogene PPFIA4 in the model, and found that compared with normal samples, CHOL patients were generally highly expressed, and the patients with high-expression of PPFIA4 had a poor prognosis. In summary, the present study may provide a valid prognostic model for bile duct cancer to inform better clinical management of patients.

Proteomics of restenosis model in LDLR-deficient hamsters coupled with the proliferative rat vascular smooth muscle cells reveals a new mechanism of vascular remodeling diseases

A major pathological mechanism involved in vascular remodeling diseases is the proliferation and migration of vascular smooth muscle cells. The lipid distribution of golden hamsters is similar to that of humans, which makes them an excellent study model for studying the pathogenesis and molecular characteristics of vascular remodeling diseases. We performed proteomic analysis on Sprague Dawley rat VSMCs (rVSMCs) and restenosis hamsters with low-density lipoprotein receptor (LDLR) deficiency as part of this study. We have also performed the enrichment analysis of differentially modified proteins in regards to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein domain. 1070 differentially abundant proteins were assessed in rVSMCs before and after platelet-derived growth factor-BB (PDGF-BB) stimulation. Specifically, 1246 proteins displayed significant differences in the restenosis model in LDLR-deficient hamsters. An analysis of crosstalk between LDLR^+/- hamsters artery restenosis and proliferating rVSMCs revealed 130 differentially expressed proteins, including 67 up-regulated proteins and 63 downregulated proteins. Enrichment analysis with KEGG showed differential proteins to be mainly concentrated in metabolic pathways. There are numerous differentially abundant proteins but particularly two proteins (phosphofructokinase 1 of liver type and lactate dehydrogenase A) were found to be up-regulated by PDGF-BB stimulation of rVSMCs and in a restenosis model of hamsters with LDLR^+/- expression. SIGNIFICANCE: Based on bioinformatics, we have found glycolysis pathway plays an important role in both the LDLR^+/- hamsters restenosis model and the proliferation of rVSMCs. Some key glycolysis enzymes may likely be developed either as new biomarkers or drug targets for vascular remodeling diseases.

ISG20: an enigmatic antiviral RNase targeting multiple viruses

Interferon-stimulated gene 20 kDa protein (ISG20) is a relatively understudied antiviral protein capable of inhibiting a broad spectrum of viruses. ISG20 exhibits strong RNase properties, and it belongs to the large family of DEDD exonucleases, present in both prokaryotes and eukaryotes. ISG20 was initially characterized as having strong RNase activity in vitro, suggesting that its inhibitory effects are mediated via direct degradation of viral RNAs. This mechanism of action has since been further elucidated and additional antiviral activities of ISG20 highlighted, including direct degradation of deaminated viral DNA and translational inhibition of viral RNA and nonself RNAs. This review focuses on the current understanding of the main molecular mechanisms of viral inhibition by ISG20 and discusses the latest developments on the features that govern specificity or resistance to its action.

Deciphering the Non-Coding RNA Landscape of Pediatric Acute Myeloid Leukemia

Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer that affects children. Although survival rates have significantly improved over the past few decades, 20-30% of children will succumb due to treatment-related toxicity or relapse. The molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it has become clear that non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA (n = 30,168) and miRNA (n = 627) expression in pedAML subpopulations (leukemic stem cells (LSCs) and leukemic blasts (L-blasts)) and their normal counterparts (hematopoietic stem cells and control myeloblasts). The potential regulatory activity of differentially expressed lncRNAs in LSCs (unique or shared with the L-blast comparison) on miRNAs was assessed. Moreover, pre-ranked gene set enrichment analyses of (anti-) correlated protein-coding genes were performed to predict the functional relevance of the differentially upregulated lncRNAs in LSCs (unique or shared with the L-blast comparison). In conclusion, this study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.

A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma

Background and Purpose

Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso).

Methods

In this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients.

Results

We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates.

Conclusions

We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

Claudin-2 promotes colorectal cancer growth and metastasis by suppressing NDRG1 transcription

Colorectal cancer (CRC) is one of the most common malignant tumours, with multiple driving factors and biological transitions involved in its development. Claudin-2 (CLDN2), a well-defined component of cellular tight junction, has been indicated to associate with CRC progression. However, the function of CLDN2 and the underlying mechanism whereby the downstream signalling transduction is regulated in CRC remains largely unclear. In this study, we demonstrated that CLDN2 is upregulated in CRC samples and associated with poor survival. And CLDN2 depletion significantly promotes N-myc downstream-regulated gene 1 (NDRG1) transcription, leading to termination of the CRC growth and metastasis in vitro and in vivo. Mechanistically, this process promotes CLDN2/ZO1/ZONAB complex dissociation and ZONAB shuttle into nucleus to enrich in the promoter of NDRG1. Thus, this study reveals a novel CLDN2/ZO1/ZONAB-NDRG1 axis in CRC by regulating the expression of EMT-related genes and CDKIs, suggesting CLDN2 may serve as a promising target for CRC treatment.

A Novel Prognostic and Predictive Signature for Lung Adenocarcinoma Derived from Combined Hypoxia and Infiltrating Immune Cell-Related Genes in TCGA Patients

Background

The hypoxia and immune status of the lung adenocarcinoma (LUAD) microenvironment appear to have combined impacts on prognosis. Therefore, deriving a prognostic signature by integrating hypoxia- and immune infiltrating cell-related genes (H&IICRGs) may add value over prognostic indices derived from genes driving either process alone.

Methods

Differentially expressed H&IICRGs (DE-H&IICRGs) were identified in The Cancer Genome Atlas transcriptomic data using limma, CIBERSORT, weighted gene co-expression network analysis, and intersection analysis. A stepwise Cox regression model was constructed to identify prognostic genes and to produce a gene signature based on DE-H&IICRGs. The potential biological functions associated with the gene signature were explored using functional enrichment analysis. The prognostic signature was externally validated in a separate cohort from the Gene Expression Omnibus database.

Results

Five prognostic genes associated with overall survival in LUAD were used in the DE-H&IICRG-based prognostic signature. Patients in the high-risk group had an inferior prognosis, which was validated in an independent external cohort, and had lower expression of most immune checkpoint genes. In multivariate analysis, only risk score and T stage were independent prognostic factors. Differentially expressed genes (DEGs) associated with the risk score were enriched for pathways related to cell cycle, hypoxia regulation, and immune response. TIDE analyses showed that low-risk LUAD patients might also respond better to immunotherapy.

Conclusion

This study establishes and validates a prognostic profile for LUAD patients that combines hypoxia and immune infiltrating cell-related genes. This signature may have clinical application both for prognostication and guiding individualized immunotherapy.

A TP53 mutation model for the prediction of prognosis and therapeutic responses in head and neck squamous cell carcinoma

BACKGROUND

Tumor protein p53 (TP53) is the most frequently mutated gene in head and neck squamous cell carcinoma (HNSC), and TP53 mutations are associated with inhibited immune signatures and poor prognosis. We established a TP53 mutation associated risk score model to evaluate the prognosis and therapeutic responses of patients with HNSC.

METHODS

Differentially expressed genes between patients with and without TP53 mutations were determined by using data from the HNSC cohort in The Cancer Genome Atlas database. Patients with HNSC were divided into high- and low-risk groups based on a prognostic risk score that was generated from ten TP53 mutation associated genes via the multivariate Cox regression model.

RESULTS

TP53 was the most common mutant gene in HNSC, and TP53 mutations were associated with immunogenic signatures, including the infiltration of immune cells and expression of immune-associated genes. Patients in the high-risk group had significantly poorer overall survival than those in the low-risk group. The high-risk group showed less response to anti-programmed cell death protein 1 (PD-1) therapy but high sensitivity to some chemotherapies.

CONCLUSION

The risk score based on our TP53 mutation model was associated with poorer survival and could act as a specific predictor for assessing prognosis and therapeutic response in patients with HNSC.

Identification of a Hypoxia-Related Molecular Classification and Hypoxic Tumor Microenvironment Signature for Predicting the Prognosis of Patients with Triple-Negative Breast Cancer

Purpose

The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of triple-negative breast cancer (TNBC), such as invasion, immune evasion, chemoresistance, and metastasis. However, a systematic analysis of the prognostic prediction models based on multiple hypoxia-related genes (HRGs) has not been established in TNBC before in the literature. We aimed to develop and verify a hypoxia gene signature for prognostic prediction in TNBC patients.

Methods

The RNA sequencing profiles and clinical data of TNBC patients were generated from the TCGA, GSE103091, and METABRIC databases. The TNBC-specific differential HRGs (dHRGs) were obtained from differential expression analysis of hypoxia cultured TNBC cell lines compared with normoxic cell lines from the GEO database. Non-negative matrix factorization (NMF) method was then performed on the TNBC patients using the dHRGs to explore a novel molecular classification on the basis of the dHRG expression patterns. Prognosis-associated dHRGs were identified by univariate and multivariate Cox regression analysis to establish the prognostic risk score model.

Results

Based on the expressions of 205 dHRGs, all the patients in the TCGA training cohort were categorized into two subgroups, and the patients in Cluster 1 demonstrated worse OS than those in Cluster 2, which was validated in two independent cohorts. Additionally, the effects of somatic copy number variation (SCNV), somatic single nucleotide variation (SSNV), and methylation level on the expressions of dHRGs were also analyzed. Then, we performed Cox regression analyses to construct an HRG-based risk score model (3-gene dHRG signature), which could reliably discriminate the overall survival (OS) of high-risk and low-risk patients in TCGA, GSE103091, METABRIC, and BMCHH (qRT-PCR) cohorts.

Conclusions

In this study, a robust predictive signature was developed for patients with TNBC, indicating that the 3-gene dHRG model might serve as a potential prognostic biomarker for TNBC.

The dynamic broad epigenetic (H3K4me3, H3K27ac) domain as a mark of essential genes

Transcriptionally active chromatin is marked by tri-methylation of histone H3 at lysine 4 (H3K4me3) located after first exons and around transcription start sites. This epigenetic mark is typically restricted to narrow regions at the 5`end of the gene body, though a small subset of genes have a broad H3K4me3 domain which extensively covers the coding region. Although most studies focus on the H3K4me3 mark, the broad H3K4me3 domain is associated with a plethora of histone modifications (e.g., H3 acetylated at K27) and is therein termed broad epigenetic domain. Genes marked with the broad epigenetic domain are involved in cell identity and essential cell functions and have clinical potential as biomarkers for patient stratification. Reducing expression of genes with the broad epigenetic domain may increase the metastatic potential of cancer cells. Enhancers and super-enhancers interact with the broad epigenetic domain marked genes forming a hub of interactions involving nucleosome-depleted regions. Together, the regulatory elements coalesce with transcription factors, chromatin modifying/remodeling enzymes, coactivators, and the Mediator and/or Integrator complex into a transcription factory which may be analogous to a liquid–liquid phase-separated condensate. The broad epigenetic domain has a dynamic chromatin structure which supports frequent transcription bursts. In this review, we present the current knowledge of broad epigenetic domains.

Tumor protein D52 is upregulated in oral squamous carcinoma cells under hypoxia in a hypoxia-inducible-factor-independent manner and is involved in cell death resistance

Background

Tumor protein D52 (TPD52) reportedly plays an important role in the proliferation and metastasis of various cancer cells, including oral squamous cell carcinoma (OSCC) cells, and is expressed strongly at the center of the tumor, where the microenvironment is hypoxic. Thus, the present study investigated the roles of TPD52 in the survival and death of OSCC cells under hypoxia, and the relationship with hypoxia-inducible factor (HIF). We examined the expression of TPD52 in OSCC cells under hypoxic conditions and analyzed the effects of HIF on the modulation of TPD52 expression. Finally, the combinational effects of TPD52 knockdown and HIF inhibition were investigated both in vitro and in vivo.

Results

The mRNA and protein levels of TPD52 increased in OSCC cells under hypoxia. However, the increase was independent of HIF transcription. Importantly, the observation was due to upregulation of mRNA stability by binding of mRNA to T-cell intercellular antigen (TIA) 1 and TIA-related protein (TIAR). Simultaneous knockdown of TPD52 and inhibition of HIF significantly reduced cell viability. In addition, the in vivo tumor-xenograft experiments showed that TPD52 acts as an autophagy inhibitor caused by a decrease in p62.

Conclusions

This study showed that the expression of TPD52 increases in OSCC cells under hypoxia in a HIF-independent manner and plays an important role in the proliferation and survival of the cells in concordance with HIF, suggesting that novel cancer therapeutics might be led by TPD52 suppression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-021-00634-0.

Identifying Dendritic Cell-Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

Determination of hypoxia signature to predict prognosis and the tumor immune microenvironment in melanoma

Melanoma is one of the highly malignant skin tumors, the incidence and death of which continue to increase. The hypoxic microenvironment drives tumor growth, progression, and heterogeneity; it also triggers a cascade of immunosuppressive responses and affects the levels of T cells, macrophages, and natural killer cells. Here, we aim to develop a hypoxia-based gene signature for prognosis evaluation and help evaluate the status of hypoxia and the immune microenvironment in melanoma. Based on the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, we performed integrated bioinformatics to analyze the hypoxia-related genes. Using Lasso Cox regression, a hypoxia model was constructed. The receiver operating characteristic and the Kaplan-Meier curve were used to evaluate the predictive capacity of the model. With the CIBERSORT algorithm, the abundance of 22 immune cells in the melanoma microenvironment was analyzed. A total of 20 hypoxia-related genes were significantly related to prognosis in the log-rank test. Lasso regression showed that FBP1, SDC3, FOXO3, IGFBP1, S100A4, EGFR, ISG20, CP, PPARGC1A, KIF5A, and DPYSL4 displayed the best features. Based on these genes, a hypoxia model was established, and the area under the curve for the model was 0.734. Furthermore, the hypoxia score was identified as an independent prognostic factor. Besides, the hypoxia score could also predict the immune microenvironment in melanoma. Down-regulated activated CD4 memory T cells, CD8 T cells, and M1-like macrophages, and up-regulated Tregs were observed in patients with a high hypoxia score. The hypoxia-related genes were identified, and the hypoxia score was found to be a prognostic factor for overall survival and a predictor for the immune microenvironment. Our findings provide new ideas for evaluation and require further validation in clinical practice.

Tumor Hypoxia Regulates Immune Escape/Invasion: Influence on Angiogenesis and Potential Impact of Hypoxic Biomarkers on Cancer Therapies

The environmental and metabolic pressures in the tumor microenvironment (TME) play a key role in molding tumor development by impacting the stromal and immune cell fractions, TME composition and activation. Hypoxia triggers a cascade of events that promote tumor growth, enhance resistance to the anti-tumor immune response and instigate tumor angiogenesis. During growth, the developing angiogenesis is pathological and gives rise to a haphazardly shaped and leaky tumor vasculature with abnormal properties. Accordingly, aberrantly vascularized TME induces immunosuppression and maintains a continuous hypoxic state. Normalizing the tumor vasculature to restore its vascular integrity, should hence enhance tumor perfusion, relieving hypoxia, and reshaping anti-tumor immunity. Emerging vascular normalization strategies have a great potential in achieving a stable normalization, resulting in mature and functional blood vessels that alleviate tumor hypoxia. Biomarkers enabling the detection and monitoring of tumor hypoxia could be highly advantageous in aiding the translation of novel normalization strategies to clinical application, alone, or in combination with other treatment modalities, such as immunotherapy.

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.