GSEA-InContext: identifying novel and common patterns in expression experiments

Identification of immune-related gene signature for predicting prognosis in uterine corpus endometrial carcinoma

The objective of this study is to develop a gene signature related to the immune system that can be used to create personalized immunotherapy for Uterine Corpus Endometrial Carcinoma (UCEC). To classify the UCEC samples into different immune clusters, we utilized consensus clustering analysis. Additionally, immune correlation algorithms were employed to investigate the tumor immune microenvironment (TIME) in diverse clusters. To explore the biological function, we conducted GSEA analysis. Next, we developed a Nomogram by integrating a prognostic model with clinical features. Finally, we performed experimental validation in vitro to verify our prognostic risk model. In our study, we classified UCEC patients into three clusters using consensus clustering. We hypothesized that cluster C1 represents the immune inflammation type, cluster C2 represents the immune rejection type, and cluster C3 represents the immune desert type. The hub genes identified in the training cohort were primarily enriched in the MAPK signaling pathway, as well as the PD-L1 expression and PD-1 checkpoint pathway in cancer, all of which are immune-related pathways. Cluster C1 may be a more suitable for immunotherapy. The prognostic risk model showed a strong predictive ability. Our constructed risk model demonstrated a high level of accuracy in predicting the prognosis of UCEC, while also effectively reflecting the state of TIME.

Ferroptosis-associated gene CISD2 suppresses colon cancer development by regulating tumor immune microenvironment

Background

Despite the association of ferroptosis with various tumors, the specific mechanism by which it influences colon adenocarcinoma (COAD) microenvironmental equilibrium remains elusive. This study aims to elucidate how ferroptosis affects COAD microenvironmental homeostasis and its potential impact on COAD research.

Objective

By employing genetic screening and single-cell analysis of tumor data, we investigated the role of ferroptosis genes in COAD microenvironmental homeostasis. The genes were correlated with immune cell infiltration in tissue samples and patient outcomes.

Methods

Ferroptosis-associated genes were initially identified through the FerrDb database. Utilizing the tidyverse and Seurat packages, genes with substantial expression differences were extracted, and clustering analysis was performed on the single-cell data. A Venn diagram depicted shared differential genes for ferroptosis and tumors. To screen key ferroptosis genes, further enrichment analysis and immune cell infiltration analysis were conducted. Lastly, human COAD cell lines were employed to overexpress CDGSH iron sulfur domain 2 (CISD2) through cellular assays to validate its function in COAD.

Results

Following screening of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, 414 COAD patient samples and 341 normal samples were included. Through the FerrDb database, 259 ferroptosis genes were identified. Clustering the single-cell data revealed 911 tumor marker genes, of which 18 were ferroptosis genes. Analysis of variance (ANOVA) and univariate regression analysis determined that only CISD2 was statistically significantly associated with clinical outcomes. Additionally, CISD2 was found to positively correlate with activated memory T cells and negatively correlate with regulatory T cells (Tregs) and plasma cells in COAD, as well as being significantly associated with several immune-related and cancer-related pathways. CISD2 expression was elevated in most tumors, likely due to cell cycle regulation and immune system activation. Moreover, CISD2 upregulation inhibited COAD cell proliferation and enhanced 5-fluorouracil (5-FU) sensitivity. Our findings indicate, for the first time, that CISD2 governs the cell cycle and stimulates the immune system to impede COAD progression.

Conclusion

By modulating the cell cycle and mediating immune infiltration, CISD2 may inhibit COAD development by influencing tumor immune microenvironment equilibrium, providing valuable insights into the relevance and potential impact of the research results on the COAD research field.

Exploring the Correlation Between GPR176, a Potential Target Gene of Gastric Cancer, and Immune Cell Infiltration

Introduction

GPR176, an orphan G protein-coupled receptor (GPCR), is essential for the progression of gastrointestinal cancers. However, it is still unclear how GPR176 affects tumor immunity and patient prognosis in gastric cancer (GC).

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were searched in this investigation to assess the expression patterns of GPR176 in GC tissues and normal gastric mucosa. The findings were further verified using immunohistochemical tests and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). The Kaplan-Meier method, univariate logistic regression, and Cox regression were then used to investigate the relationship between GPR176 and clinical traits. Additionally, the potential correlation between GPR176, immune checkpoint genes, and immune cell infiltration levels was investigated.

Results

As per the research findings, GC tissues had higher levels of GPR176 than normal tissues. Additionally, individuals with high expression of GPR176 had a worse 10-year overall survival (OS), in contrast with those having a low expression of GPR176 (p < 0.001). The OS of GC can be predicted using a validated nomogram model. The expression of GPR176 demonstrated a negative correlation with CD8+ T cells. When compared to the low-expression group of GPR176, Tumor Immune Dysfunction and Exclusion (TIDE) analysis demonstrated that the high-expression group had a considerably higher risk of immune evasion. A remarkable difference (variation) was observed in the levels of GPR176 expression across both groups, ie, low and high-risk groups, as determined by the immune phenomenon scores (IPS) immunotherapy assessment.

Conclusion

By examining GPR176 from various biological perspectives, it was determined that GPR176 can act as a predictive biomarker for poor patient prognosis in GC. Additionally, it was observed that GPR176 is capable of suppressing the proliferation of CD8+ T cells and facilitating immune evasion.

Erk5 functions in modulation of zebrafish intestinal permeability

The intestine of zebrafish consists of mucosa, muscularis and serosa. Intestinal epithelial cells (IECs) act as a physical and biochemical barrier to protect against invasion by external commensal bacteria. Cell junction is one of the crucial basis of the barrier function. When cell junctions were disrupted, intestinal permeability would be naturally impeded. Extracellular signal-regulated kinase 5 (ERK5), belonging to the Mitogen-activated protein kinase (MAPK) family, is involved in the normal physiological development of the cardiovascular system and nervous system. But the role of erk5 in intestinal morphogenesis and intestinal function is yet to know. Here, we showed that knockout of the erk5 in zebrafish larvae resulted in intestinal wall hypoplasia, including the thinned intestinal wall, reduced intestinal folds, and disrupted cell junctions. In addition, the intestinal permeability assay demonstrated that knockout of erk5 resulted in increased intestinal permeability. All of these showed that erk5 plays an essential role in the maintenance of intestinal barrier function. Thus, our data indicate that erk5 is a critical effector in intestinal morphogenesis and intestinal function, and dysfunction of erk5 would lead to intestinal diseases.

Ammonia-induced oxidative stress triggered proinflammatory response and apoptosis in pig lungs

Ammonia, a common toxic gas, is not only one of the main causes of haze, but also can enter respiratory tract and directly affect the health of humans and animals. Pig was used as an animal model for exploring the molecular mechanism and dose effect of ammonia toxicity to lung. In this study, the apoptosis of type II alveolar epithelial cells was observed in high ammonia exposure group using transmission electron microscopy. Gene and protein expression analysis using transcriptome sequencing and western blot showed that low ammonia exposure induced T-cell-involved proinflammatory response, but high ammonia exposure repressed the expression of DNA repair-related genes and affected ion transport. Moreover, high ammonia exposure significantly increased 8-hydroxy-2-deoxyguanosine (8-OHdG) level, meaning DNA oxidative damage occurred. In addition, both low and high ammonia exposure caused oxidative stress in pig lungs. Integrated analysis of transcriptome and metabolome revealed that the up-regulation of LDHB and ND2 took part in high ammonia exposure-affected pyruvate metabolism and oxidative phosphorylation progress, respectively. Inclusion, oxidative stress mediated ammonia-induced proinflammatory response and apoptosis of porcine lungs. These findings may provide new insights for understanding the ammonia toxicity to workers in livestock farms and chemical fertilizer plants.

Human umbilical cord mesenchymal stem cell-derived exosomes promote murine skin wound healing by neutrophil and macrophage modulations revealed by single-cell RNA sequencing

Introduction

Full-thickness skin wound healing remains a serious undertaking for patients. While stem cell-derived exosomes have been proposed as a potential therapeutic approach, the underlying mechanism of action has yet to be fully elucidated. The current study aimed to investigate the impact of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSC-Exosomes) on the single-cell transcriptome of neutrophils and macrophages in the context of wound healing.

Methods

Utilizing single-cell RNA sequencing, the transcriptomic diversity of neutrophils and macrophages was analyzed in order to predict the cellular fate of these immune cells under the influence of hucMSC-Exosomes and to identify alterations of ligand-receptor interactions that may influence the wound microenvironment. The validity of the findings obtained from this analysis was subsequently corroborated by immunofluorescence, ELISA, and qRT-PCR. Neutrophil origins were characterized based on RNA velocity profiles.

Results

The expression of RETNLG and SLC2A3 was associated with migrating neutrophils, while BCL2A1B was linked to proliferating neutrophils. The hucMSC-Exosomes group exhibited significantly higher levels of M1 macrophages (215 vs 76, p &lt; 0.00001), M2 macrophages (1231 vs 670, p &lt; 0.00001), and neutrophils (930 vs 157, p &lt; 0.00001) when compared to control group. Additionally, it was observed that hucMSC-Exosomes elicit alterations in the differentiation trajectories of macrophages towards more anti-inflammatory phenotypes, concomitant with changes in ligand-receptor interactions, thereby facilitating healing.

Discussion

This study has revealed the transcriptomic heterogeneity of neutrophils and macrophages in the context of skin wound repair following hucMSC-Exosomes interventions, providing a deeper understanding of cellular responses to hucMSC-Exosomes, a rising target of wound healing intervention.

RsERF40 contributes to cold stress tolerance and cell expansion of taproot in radish (Raphanus sativus L.)

The growth and development of taproots are inhibited by cold stress in radish (Raphanus sativus L.). Ethylene-responsive element binding factors (ERF) are key participators in the cold stress response and growth regulation of plants. However, the function of ERF genes in cold tolerance and root development in radish remains elusive. Here, we showed that the secondary growth of radish taproots was inhibited by cold stress. Comparative transcriptome analysis demonstrated that the RsERF40 gene is an important regulator of the cold stress response and root growth regulation. The cold tolerance of transgenic Arabidopsis plants overexpressing the RsERF40 gene was significantly improved. Overexpressing RsERF40 in the cold-sensitive radish genotype and silencing RsERF40 in the cold-tolerant radish genotype indicated that RsERF40 was beneficial for alleviating oxidative damage under cold stress in radish. Transgenic Arabidopsis seedlings showed an increase in the elongation and radial growth of dark-grown roots. RT-qPCR analysis showed that the expression of the cold-related genes (CORs) RsCOR78 and RsCOR413PM1 and the cell wall strengthening-related genes RsCESA6 and RsEXPB3 was upregulated in transgenic Arabidopsis seedlings. Yeast one-hybrid (Y1H) and dual-luciferase reporter assays (DLA) revealed that RsERF40 directly regulates RsCOR78, RsCOR413PM1, RsCESA6 and RsEXPB3 expression, illustrating that RsERF40 enhances cold tolerance and taproot growth by modulating osmotic adjustment and cell wall mechanical strength in radish. In this study, the RsERF40-regulon was firstly found to be a new cold response pathway independent of the CBF-COR pathway conferring cold stress tolerance with increasing radish taproot growth. These results provided novel insight into the molecular mechanism underlying cold stress response and would facilitate the genetic improvement of cold tolerance in radish and other root vegetable crops.

MARCO is a potential prognostic and immunotherapy biomarker

BACKGROUND

Macrophage receptor with collagenous structure (MARCO), a novel immune checkpoint expressed on tumor-associated macrophages, has antitumor therapeutic properties. However, the association between MARCO and patient prognosis, immune infiltration, and ICI immunotherapy needs to be studied urgently.

METHODS

MARCO distribution in cancer tissues was investigated using the TCGA and GTEx databases. The PrognoScan and KM Plotter databases was used to assess the MARCO prognosis. TIMER2.0, GEPIA, cBioPortal, and GSEA all confirmed the link between MARCO and immune infiltration, mutation profile, and enrichment pathway analysis. Data visualization was implemented by R language.

RESULTS

In general, MARCO had a substantial impact on the prognosis of cancer patients and was expressed differently in cancer and adjacent normal tissues. High expression of MARCO was associated with poorer OS in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), colon adenocarcinoma (COAD), and prostate adenocarcinoma (PRAD). However, high expression of MARCO had a better PFI in brain lower-grade glioma (LGG) and skin cutaneous melanoma (SKCM). We discovered that MARCO expression was lowest in pancreatic adenocarcinoma (PAAD) and rectum adenocarcinoma (READ) stage 1, BLCA stage 2, LUSC and stomach adenocarcinoma (STAD) stage 3, and liver hepatocellular carcinoma (LIHC) stage 4. Subsequently, we analyzed the correlation between MARCO and 47 immune checkpoints and observed that MARCO was positively connected with CD80, CD86, and leukocyte-associated immunoglobulin-like receptor 1(LAIR1) in most cancers. In COAD, MARCO has the most microsatellite instability (MSI). In addition, we discovered that high expression of MARCO patients had a better prognosis after immune checkpoint inhibitor (ICI) treatment in SKCM. Finally, GSEA revealed a significant correlation between MARCO and TNF/NFκB signaling, KRAS signaling, PI3K/AKT/mTOR pathway, IL-6-STAT3 signaling, TGFβ pathway, and p53 pathway.

CONCLUSION

This study comprehensively investigated the relationship between MARCO and clinical prognosis, immune infiltration, and ICI immunotherapy in various cancers. We demonstrated the potential of MARCO as an emerging biomarker, exploring new avenues for future tumor immunotherapy.

Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits

Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.

CD1C is associated with breast cancer prognosis and immune infiltrates

BACKGROUND

The tumor microenvironment (TME) in breast cancer plays a vital role in occurrence, development, and therapeutic responses. However, immune and stroma constituents in the TME are major obstacles to understanding and treating breast cancer. We evaluated the significance of TME-related genes in breast cancer.

METHODS

Invasive breast cancer (BRCA) samples were retrieved from the TCGA and GEO databases. Stroma and immune scores of samples as well as the proportion of tumor infiltrating immune cells (TICs) were calculated using the ESTIMATE and CIBERSORT algorithms. TME-related differentially expressed genes (DEGs) were analyzed by a protein interaction (PPI) network and univariate Cox regression to determine CD1C as a hub gene. Subsequently, the prognostic value of CD1C, its response to immunotherapy, and its mechanism in the TME were further studied.

RESULTS

In BRCA, DEGs were determined to identify CD1C as a hub gene. The expression level of CD1C in BRCA patients was verified based on the TCGA database, polymerase chain reaction (PCR) results, and western blot analysis. Immunohistochemical staining (IHC) results revealed a correlation between prognosis, clinical features, and CD1C expression in BRCA. Enrichment analysis of GSEA and GSVA showed that CD1C participates in immune-associated signaling pathways. CIBERSORT showed that CD1C levels were associated with tumor immune infiltrating cells (TILs), such as different kinds of T cells. Gene co-expression analysis showed that CD1C and the majority of immune-associated genes were co-expressed in BRCA. In renal cell carcinoma, patients with a high expression of CD1C had a better immunotherapy effect.

CONCLUSION

CD1C is an important part of the TME and participates in immune activity regulation in breast tumors. CD1C is expected to become a prognostic marker and a new treatment target for breast cancer.

Identification and verification of m7G-Related genes as biomarkers for prognosis of sarcoma

Background: Increasing evidence indicates a crucial role for N7-methylguanosine (m7G) methylation modification in human disease development, particularly cancer, and aberrant m7G levels are closely associated with tumorigenesis and progression via regulation of the expression of multiple oncogenes and tumor suppressor genes. However, the role of m7G in sarcomas (SARC) has not been adequately evaluated. Materials and methods: Transcriptome and clinical data were gathered from the TCGA database for this study. Normal and SARC groups were compared for the expression of m7G-related genes (m7GRGs). The expression of m7GRGs was verified using real-time quantitative PCR (RT-qPCR) in SARC cell lines. Then, differentially expressed genes (DEGs) were identified between high and low m7GRGs expression groups in SARC samples, and GO enrichment and KEGG pathways were evaluated. Next, prognostic values of m7GRGs were evaluated by Cox regression analysis. Subsequently, a prognostic model was constructed using m7GRGs with good prognostic values by Lasso regression analysis. Besides, the relationships between prognostic m7GRGs and immune infiltration, clinical features, cuproptosis-related genes, and antitumor drugs were investigated in patients with SARC. Finally, a ceRNA regulatory network based on m7GRGs was constructed. Results: The expression of ten m7GRGs was higher in the SARC group than in the control group. DEGs across groups with high and low m7GRGs expression were enriched for adhesion sites and cGMP-PKG. Besides, we constructed a prognostic model that consists of EIF4A1, EIF4G3, NCBP1, and WDR4 m7GRGs for predicting the survival likelihood of sarcoma patients. And the elevated expression of these four prognostic m7GRGs was substantially associated with poor prognosis and elevated expression in SARC cell lines. Moreover, we discovered that these four m7GRGs expressions were negatively correlated with CD4⁺ T cell levels, dendritic cell level and tumor purity, and positively correlated with tumor mutational burden, microsatellite instability, drug sensitivity and cuproptosis-related genes in patients with sarcomas. Then, a triple regulatory network of mRNA, miRNA, and lncRNA was established. Conclusion: The current study identified EIF4A1, EIF4G3, NCBP1, and WDR4 as prognostic genes for SARC that are associated with m7G.These findings extend our knowledge of m7G methylation in SARC and may guide the development of innovative treatment options.

Identification and validation of RB1 as an immune-related prognostic signature based on tumor mutation burdens in bladder cancer

Bladder cancer (BCa) is one of the most common malignant tumors in the urinary system. Developing effective prognostic gene and exploring the immune cells that affect the prognosis of tumor are required. Full transcriptome data ( n = 433), clinical information ( n = 581) and mutation sequencing ( n = 412) were obtained from The Cancer Genome Atlas and independent mutation sequencing data of 101 samples were acquired from International Cancer Genome Consortium. Statistical processing was conducted using R packages. Gene biologically functional research was performed with gene set enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes database. Twenty-two types of immune cell infiltration were assessed and calculated in 398 samples of BCa. Furthermore, the expression of immune-related prognostic signature was verified. The relationship between prognostic gene and immune cells was explored preliminarily. Tumor mutation burdens of mutant-type groups were higher than wild-type groups of 19 genes, except for FGFR3 and CREBBP. Kaplan-Meier analysis showed that high frequency of retinoblastomal 1 (RB1) mutation led to poor prognosis of BCa patients and was an independent prognostic factor ( P = 0.004; HR = 1.776). Proportions and correlation of 22 types of immune cells in 433 samples were determined. We found that RB1 expression decreased in BCa validated through quantitative PCR and immunohistochemistry. In addition, regulatory T cells (Tregs) were detected as a negatively correlated type of immune cell to mutation of RB1, whereas fluorescence costaining showed that Foxp3 expression of Tregs infiltration was negatively related to the expression of RB1. Mutation of RB1 can be identified as an independent prognostic predictor of BCa, and it may suppress the infiltration of Tregs in BCa tissues, increasing the incidence of tumor immune escape.

Role of PAX2 in breast cancer verified by bioinformatics analysis and in vitro validation

Background

Breast cancer (BC) is the most frequently diagnosed cancer in women and the second most common cancer among newly diagnosed cancers worldwide. Studies have shown that paired box 2 (PAX2) participates in the tumorigenesis of some cancer cells, but its role in BC is still unclear.

Methods

Transcriptome expression profiles and clinicopathological information of BC were downloaded from The Cancer Genome Atlas (TCGA) database to explore the expression level and prognostic value of PAX2. Gene set enrichment analysis (GSEA) and functional enrichment analysis were performed to investigate the functions and pathways of PAX2. Moreover, real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) was used to determine the expression of PAX2 in BC tissues, and the predictive value of PAX2 in clinical samples was assessed. Cell Counting Kit-8 (CCK-8) assay was used to evaluate cell growth. The migration and invasive capacities of cells were assessed by wound healing assay and Transwell assay.

Results

PAX2 was upregulated in the TCGA-BC datasets. GSEA suggested that PAX2 may be involved in the regulation of signaling pathways such as MAPK. Moreover, PAX2 was overexpressed in BC tissues, and PAX2 expression was associated with tumor size and lymph node metastasis. PAX2 deficiency could promote the growth, migration, and invasion of BC cells.

Conclusions

Upregulation of PAX2 inhibited BC cell growth, migration, and invasion, making PAX2 a potential therapeutic target for BC.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer

Background

Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages.

Methods

The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data.

Results

Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient's prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-β signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways.

Conclusions

Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

Gene set enrichment analysis and ingenuity pathway analysis to verify the impact of Wnt signaling in psoriasis treated with Taodan granules

BACKGROUND

Taodan granules (TDGs), traditional Chinese herbals, have effectiveness in relieving skin erythema, scales, and other symptoms of psoriasis. Yet mechanisms of TDGs remain indistinct.

OBJECTIVE

To indicate the molecular mechanisms of TDGs in treating psoriasis.

MATERIALS AND METHODS

Primarily, transcriptional profiling was applied to identify differentially expressed genes (DEGs), proceeding with Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) analysis were used for functional enrichment analysis. Subsequently, levels of selected genes were validated by RT-PCR and western blotting.

RESULTS

The GSEA results revealed TDGs could down-regulate the Wnt signaling pathway to ameliorate skin lesions of imiquimod (IMQ)-induced psoriatic models mice. IPA core network associated with Wnt signaling pathways in TDGs for psoriasis was established. Thereinto zeste homolog 2 (EZH2), CTNNB1, tumor protein p63 (TP63), and WD repeat domain 5 (WDR5) were considered as upstream genes in the Wnt signaling pathway. Experimental verification indicated TDGs could down-regulate EZH2, CTNNB1, and WDR5 at the mRNA and protein levels, along with up-regulate TP63 levels. Moreover, TDGs were confirmed to reduce RAC2 and WNT5A at mRNA and protein levels of the Wnt signaling pathway.

CONCLUSIONS

TDGs may improve psoriasis through the regulation for upstream genes (down-regulating levels of EZH2, CTNNB1, and WDR5; up-regulating TP63 levels) of Wnt signaling pathway, thus reducing levels of RAC2 and WNT5A in the Wnt signaling pathway.

Comprehensive analysis of cuproptosis-related prognostic gene signature and tumor immune microenvironment in HCC

Background: Copper is an indispensable mineral element involved in many physiological metabolic processes. Cuproptosis is associated with a variety of cancer such as hepatocellular carcinoma (HCC). The objective of this study was to examine the relationships between the expression of cuproptosis-related genes (CRGs) and tumor characteristics, including prognosis and microenvironment of HCC. Methods: The differentially expressed genes (DEGs) between high and low CRGs expression groups in HCC samples were identified, and further were analyzed for functional enrichment analysis. Then, CRGs signature of HCC was constructed and analyzed utilizing LASSO and univariate and multivariate Cox regression analysis. Prognostic values of CRGs signature were evaluated by Kaplan-Meier analysis, independent prognostic analysis and nomograph. The expression of prognostic CRGs was verified by Real-time quantitative PCR (RT-qPCR) in HCC cell lines. In addition, the relationships between prognostic CRGs expression and the immune infiltration, tumor microenvironment, antitumor drugs response and m6A modifications were further explored using a series of algorithms in HCC. Finally, ceRNA regulatory network based on prognostic CRGs was constructed. Results: The DEGs between high and low CRG expression groups in HCC were mainly enriched in focal adhesion and extracellular matrix organization. Besides, we constructed a prognostic model that consists of CDKN2A, DLAT, DLST, GLS, and PDHA1 CRGs for predicting the survival likelihood of HCC patients. And the elevated expression of these five prognostic CRGs was substantially in HCC cell lines and associated with poor prognosis. Moreover, immune score and m6A gene expression were higher in the high CRG expression group of HCC patients. Furthermore, prognostic CRGs have higher mutation rates in HCC, and are significantly correlated with immune cell infiltration, tumor mutational burden, microsatellite instability, and anti-tumor drug sensitivity. Then, eight lncRNA-miRNA-mRNA regulatory axes that affected the progression of HCC were predicted. Conclusion: This study demonstrated that the CRGs signature could effectively evaluate prognosis, tumor immune microenvironment, immunotherapy response and predict lncRNA-miRNA-mRNA regulatory axes in HCC. These findings extend our knowledge of cuproptosis in HCC and may inform novel therapeutic strategies for HCC.

Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.

Identifying TYMP as an Immune Prognostic Marker in Clear Cell Renal Cell Carcinoma

BACKGROUND

In clear cell renal cell carcinoma (ccRCC), only some patients can benefit from immunotherapy therapy, and it is urgent to find immune-related molecular markers and targets.

METHODS

Thymidine phosphorylase (TYMP) expression level and predictive value in pan-cancers were analyzed using TIMER, GEPIA2, and The Human Protein Atlas. We obtained ccRCC tissues to verify the differential expression of TYMP and confirmed the biological function in vitro. Subsequently, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) are used to explore the potential mechanism of TYMP. Finally, TIMER was used to analyze the infiltration levels and prognostic value of different immune cells.

RESULTS

TYMP is upregulated in various cancers, including ccRCC, and there is a certain degree of causality between high expression and poor prognosis in ccRCC. It was confirmed that TYMP knockdown could suppress cell aggressiveness, and cause cell death. Differential analysis showed that 55 differential genes were upregulated in the high-expression groups of TYMP. KEGG and GSEA analyses suggested that TYMP was linked to immune cell invasion, fatty acid metabolism, and P53 signaling pathway. Further investigation revealed that the expression level of TYMP linked positively to T-cell follicular helper and Tregs, but negatively with mast cell activation. Finally, a Nomogram was established on the base of expression level of TYMP and the clinical characteristics of ccRCC patients to predict prognosis.

CONCLUSIONS

Patient survival is poor and immune cell infiltration is abnormal when TYMP is highly expressed in ccRCC, suggesting that ccRCC patients could benefit from using TYMP as a molecular diagnostic and therapeutic target.

CCNE1 is a potential target of Metformin for tumor suppression of ovarian high-grade serous carcinoma

High-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer, accounting for 70%-80% of mortality. However, the treatment of HGSOC has improved little in the past few decades. Metformin is the first-line medication for the treatment of type 2 diabetes and has now gained more attention in cancer treatment. In this study, we sought to identify potential hub genes that metformin could target in the treatment of HGSOC. We downloaded GSE69428 and GSE69429 in the Gene Expression Omnibus database and performed the bioinformatics analysis. Subsequently, we analyzed the effect of Metformin in HGSOC through biological experiments. Molecular simulation docking was used to predict the interaction of Metformin and CCNE1. We chose CCNE1 for the study based on bioinformatics analysis, literature studies, and preliminary data. We evaluated that CCNE1 is overexpressed in HGSOC tissues and found that HGSOC cells with high CCNE1 expression increase sensitivity to Metformin treatment in the analysis of cell proliferation and anchorage-independent growth. Metformin could inhibit the expression of CCNE1, which is associated with the anti-proliferative effect of tumor cells. Moreover, Metformin could ameliorate the tumor growth in syngeneic orthotopic transplantation mouse models and xenograft tumorigenesis models. Furthermore, molecular simulation docking showed that Metformin may bind to CCNE1 protein, suggesting that CCNE1 could be a potential target for Metformin. Our data revealed that Metformin has antitumor effects on ovarian cancer and CCNE1 could be a potential target for Metformin.

Prognostic nomogram based on the lymph node metastasis indicators for patients with bladder cancer: A SEER population‐based study and external validation

PURPOSE

This study aimed to compare the prognostic value of multiple lymph node metastasis (LNM) indicators and to develop optimal prognostic nomograms for bladder cancer (BC) patients.

METHODS

BC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly partitioned into training and internal validation cohorts. Genomic and clinical data were collected from The Cancer Genome Atlas (TCGA) as external validation cohort. The predictive efficiency of LNM indicators was compared by constructing multivariate Cox regression models. We constructed nomograms on basis of the optimal models selected for overall survival (OS) and cause-specific survival (CSS). The performance of nomograms was evaluated with calibration plot, time-dependent area under the curve (AUC) and decision curve analysis (DCA) in three cohorts. We subsequently estimated the difference of biological function and tumor immunity between two risk groups stratified by nomograms in TCGA cohort.

RESULTS

Totally, 10,093 and 107 BC patients were screened from the SEER and TCGA databases. N classification, positive lymph nodes (PLNs), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were all independent predictors for OS and CSS. The filtered models containing LODDS had minimal Akaike Information Criterion, maximal concordance indexes and AUCs. Age, LODDS, T and M classification were integrated into nomogram for OS, while nomogram for CSS included gender, tumor grade, LODDS, T and M classification. The nomograms were successfully validated in predictive accuracy and clinical utility in three cohorts. Additionally, the tumor microenvironment was different between two risk groups.

CONCLUSIONS

LODDS demonstrated superior prognostic performance over N classification, PLN and LNR for OS and CSS of BC patients. The nomograms incorporating LODDS provided appropriate prediction of BC, which could contribute to the tumor assessment and clinical decision-making.

Organoids

Organoids have attracted increasing attention because they are simple tissue-engineered cell-based in vitro models that recapitulate many aspects of the complex structure and function of the corresponding in vivo tissue. They can be dissected and interrogated for fundamental mechanistic studies on development, regeneration, and repair in human tissues. Organoids can also be used in diagnostics, disease modeling, drug discovery, and personalized medicine. Organoids are derived from either pluripotent or tissue-resident stem (embryonic or adult) or progenitor or differentiated cells from healthy or diseased tissues, such as tumors. To date, numerous organoid engineering strategies that support organoid culture and growth, proliferation, differentiation and maturation have been reported. This Primer serves to highlight the rationale underlying the selection and development of these materials and methods to control the cellular/tissue niche; and therefore, structure and function of the engineered organoid. We also discuss key considerations for generating robust organoids, such as those related to cell isolation and seeding, matrix and soluble factor selection, physical cues and integration. The general standards for data quality, reproducibility and deposition within the organoid community is also outlined. Lastly, we conclude by elaborating on the limitations of organoids in different applications, and key priorities in organoid engineering for the coming years.

Positive regulators of T cell proliferation as biomarkers for predicting prognosis and characterizing the immune landscape in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the one of the most prevalent and fatal form of malignant tumors worldwide. Recently, immunotherapy is widely used in the treatment of patients with LUAD and has proved to be clinically effective in improve the prognosis of patients. But there still has been a tremendous thrust to further improve the efficacy of immunotherapy in individual patients with LUAD. The suppression of T cells and their effector functions in the tumor microenvironment (TME) of LUAD is one of the primary reasons for the low efficacy of immunotherapy in some patients with LUAD. Therefore, identifying positive regulators of T cell proliferation (TPRs) may offer novel avenues for LUAD immunotherapy. In this study, we comprehensively evaluated the infiltration patterns of TPRs in 1,066 patients with LUAD using unsupervised consensus clustering and identified correlations with genomic and clinicopathological characteristics. Three infiltrating TPR clusters were defined, and a TPR-related risk signature composed of nine TPRs was constructed using least absolute shrinkage and selection operator-Cox regression algorithms to classify the individual TPR infiltration patterns. Cluster 1 exhibited high levels of T cell infiltration and activation of immune-related signaling pathways, whereas cluster 2 was characterized by robust T cell immune infiltration and enrichment of pathways associated with carcinogenic gene sets and tumor immunity. Cluster 3 was characterized as an immune-desert phenotype. Moreover, the TPR signature was confirmed as an independent prognostic biomarker for drug sensitivity in patients with LUAD. In conclusion, the TPR signature may serve as a novel tool for effectively characterizing immune characteristics and evaluating the prognosis of patients with LUAD.

A Lamin Family-Based Signature Predicts Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

Background. Lamin family members play crucial roles in promoting oncogenesis and cancer development. The values of lamin family in predicting prognosis and immunotherapy response remain largely unclarified. Our research is aimed at comprehensively estimating the clinical significance of lamin family in hepatocellular carcinoma and constructing a novel lamin family-based signature to predict prognosis and guide the precise immunotherapy. Methods. The expression features and prognostic value of LMNA, LMNB1, and LMNB2 were explored in the TCGA and GEO databases. The biological functions of LMNB1 and LMNB2 were validated by in vitro assays. A lamin family-based signature was built using the TCGA training set. The TCGA test set, entire TCGA set, and GSE14520 set were used to validate its predictive power. Univariate and multivariate analyses were performed to evaluate the independence of the lamin family-based signature from other clinicopathological characteristics. A nomogram was constructed using the lamin family-based signature and TNM stage. The associations of this signature with molecular pathways, clinical characteristics, immune cell infiltration, and immunotherapy response were analyzed. Results. Lamin family members were upregulated in HCC. Upregulation of LMNB1 and LMNB2 promoted HCC proliferation, migration, and invasion. The predictive signature was initially established based on LMNB1 and LMNB2 which could effectively identify differences in overall survival, immune cell infiltration, and clinicopathological characteristics of high- and low-risk patients. The nomogram showed high prognostic predictive accuracy. Importantly, the lamin family-based signature was correlated with immune suppression and expression of immune checkpoint molecules. Conclusions. The lamin family-based signature is a robust biomarker to predict overall survival and immunotherapy response in HCC. High-risk score patients have a poorer overall survival and might be more sensitive to immunotherapy. This signature may contribute to improving individualized prognosis prediction and precision immunotherapy for HCC patients.

A novel classification of HCC basing on fatty-acid-associated lncRNA

Aberrant long noncoding RNA (lncRNA) expression and fatty acid signaling dysfunction both contribute to hepatocellular carcinoma (HCC) occurrence and development. However, the relationship and interaction mechanism between lncRNAs and fatty acid signaling in HCC remain unclear. Data regarding RNA expression and clinical outcomes for patients with HCC were obtained from The Cancer Genome Atlas (TCGA), HCCDB, and the Gene Expression Omnibus (GEO) databases. Hallmark pathways were identified using the single-sample gene set enrichment analysis (ssGSEA) method. ConsensusClusterPlus was used to establish a consistency matrix for classifying samples into three subtypes. A risk signature was established, and predictive values for key lncRNAs related to prognosis were evaluated using Kaplan-Meier analysis and receiver operating characteristic curves. The ESTIMATE algorithm, MCP-Counter, and ssGSEA were used to evaluate the characteristics of the tumor immune microenvironment. The CTRP2.0 and PRISM were used to analyze drug sensitivity in HCC subtypes. We discovered seven fatty-acid-associated lncRNAs with predictive prognostic capabilities, including TRAF3IP2-AS1, SNHG10, AL157392.2, LINC02641, AL357079.1, AC046134.2, and A1BG-AS. Three subtypes were obtained, which presented with differences in prognosis, clinical information, mutation features, pathway traits, immune characteristics, and drug sensitivity. The seven key lncRNAs identified in this study might serve as promising biomarkers for predicting prognosis in patients with HCC, and the three HCC subtypes classified according to lncRNA expression profiles could improve HCC classification.

CHRNB2 represses pancreatic cancer migration and invasion via inhibiting β-catenin pathway

Background

Pancreatic cancer is one of the most lethal disease with highly fatal and aggressive properties. Lymph node ratio (LNR), the ratio of the number of metastatic lymph nodes to the total number of examined lymph nodes, is an important index to assess lymphatic metastasis and predict prognosis, but the molecular mechanism underlying high LNR was unclear.

Methods

Gene expression and clinical information data of pancreatic cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Patients in TCGA were averagely divided into low and high LNR groups. Then, Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to build co-expression network to explore LNR-related modules and hub genes. GO and KEGG analysis was performed to find key pathways related to lymph node metastasis. Next, GSE101448 and the overall survival data in TCGA was employed to further select significant genes from hub genes. Considering the key role of CHRNB2 in LNR and survival, gene set enrichment analysis (GSEA) was applied to find pathways related to CHRNB2 expression in pancreatic cancer. The contribution of CHRNB2 to migrative and invasive ability of pancreatic cancer cells was confirmed by Transwell assays. We finally explored the role of CHRNB2 in EMT and β-catenin pathway via Western Blot.

Results

High LNR was significantly related to high T stages and poor prognosis. In WGCNA, 14 hub genes (COL5A1, FN1, THBS2, etc.) were positively related to high LNR, 104 hub genes (FFAR1, SCG5, TMEM63C, etc.) were negatively related to high LNR. After taking the intersection with GSE101448, 13 genes (CDK5R2, SYT7, CACNA2D2, etc.) which might prevent lymph node metastasis were further selected. Among them, CHRNB2 showed the strongest relationship with long survival. Moreover, CHRNB2 also negatively related to the T stages and LNR. Next, knockdown of CHRNB2 expression could acetylcholine (ACh)-independently increase the migration and invasion of pancreatic cancer cells, while CHRNB2 overexpression ACh-independently decrease the migration and invasion of pancreatic cancer cells. For exploring the underlying mechanism, CHRNB2 downregulated β-catenin pathway might through controlling its upstream regulators such as SOX6, SRY, SOX17, and TCF7L2.

Conclusions

CHRNB2 negatively relates to lymph node metastasis in pancreatic cancer patients. CHRNB2 could inhibit β-catenin pathway, EMT, migration and invasion of pancreatic cancer cells via ACh-independent mechanism.

Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma

Purpose

Renal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).

Methods

The Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| &gt; 1 and an adjusted p-value&lt; 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.

Results

Forty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.

Conclusion

GILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE's measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis

Objective

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.

Materials and methods

Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.

Results

A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.

Conclusion

DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

Analysis and Validation of Differentially Expressed Ferroptosis-Related Genes in Regorafenib-Induced Cardiotoxicity

Background

Although tyrosine kinase inhibitors (TKIs) constitute a type of anticancer drugs, the underlying mechanisms of TKI-associated cardiotoxicity remain largely unknown. Ferroptosis is a regulated cell death form that implicated in several tumors' biological processes. Our objective was to probe into the differential expression of ferroptosis-related genes in regorafenib-induced cardiotoxicity through multiple bioinformatics analysis and validation.

Methods and Materials

Four adult human cardiomyocyte cell lines treated with regorafenib were profiled using Gene Expression Omnibus (GEO) (GSE146096). Differentially expressed genes (DEGs) were identified using DESeq2 in R (V.3.6.3). Then, Gene Ontology (GO) Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, and Gene Set Enrichment Analysis (GSEA) were used to explore DEGs' bioinformatics functions and enriched pathways. We intersected DEGs with 259 ferroptosis-related genes from the FerrDb database. Finally, the mRNA levels of differentially expressed ferroptosis-related genes (DEFRGs) were validated in regorafenib-cultured cardiomyocytes to anticipate the link between DEFRGs and cardiotoxicity.

Results

747,1127,773 and 969 DEGs were screened out in adult human cardiomyocyte lines A, B, D, and E, respectively. The mechanism by which REG promotes cardiotoxicity associated with ferroptosis may be regulated by PI3K-Akt, TGF-beta, and MAPK. GSEA demonstrated that REG can promote cardiotoxicity by suppressing genes and pathways encoding extracellular matrix and related proteins, oxidative phosphorylation, or ATF-2 transcription factor network. After overlapping DEGs with ferroptosis-related genes, we got seven DEFRGs and found that ATF3, MT1G, and PLIN2 were upregulated and DDIT4 was downregulated. The ROC curve demonstrated that these genes predict regorafenib-induced cardiotoxicity well.

Conclusion

We identified four DEFRGs which may become potential predictors and participate in the regorafenib-induced cardiotoxicity. Our findings provide possibility that targeting these ferroptosis-related genes may be an alternative for clinical prevention and therapy of regorafenib-related cardiotoxicity.

Fucosyltransferase 4 Predicts Patient Outcome in Rectal Cancer through an Immune Microenvironment-Mediated Multi-Mechanism

Colorectal cancer is the most common type of gastrointestinal malignant tumors worldwide. Standardization of the strategy for the precise treatment of this cancer has been a major challenge. Enrichment analysis of six gene groups (colon cancer-specific genes (upregulated and downregulated); rectal cancer-specific genes (upregulated and downregulated); and common genes (upregulated and downregulated)) revealed the common and specific features of colon and rectal cancer, particularly a hyperactive immune response in rectal cancer. Key common genes exhibited a similar expression pattern, but were associated with distinct patient prognosis in colon and rectal cancer. FUT4 was a core regulatory gene in rectal cancer; it can decrease the level of infiltration by M2 macrophages in the tumor immune microenvironment and participate in the positive regulation of the immune system and glycoprotein biosynthetic process, thereby affecting the outcome of patients with rectal cancer. FUT4 co-expression genes can influence patient’s survival time by regulating the cell cycle. Among the regulators of FUT4 co-expression genes, checkpoint kinase 2 (CHEK2) was linked to patient outcome.

Comprehensive pan-cancer analysis identifies cellular senescence as a new therapeutic target for cancer: multi-omics analysis and single-cell sequencing validation

Although cellular senescence has long been recognized as an anti-tumor mechanism, mounting evidence suggests that in some circumstances, senescent cells promote tumor growth and malignancy spread. Therefore, research into the exact relationship between cellular senescence and tumor immunity is ongoing. We analyzed changes in the expression, copy number variation, single-nucleotide variation, methylation, and drug sensitivity of cellular senescence-related genes in 33 tumor types. The cellular senescence score was calculated using the single-sample gene-set enrichment analysis. The correlations between cellular senescence score and prognosis, tumor immune microenvironment (TIME), and expression of tumor immune-related genes were comprehensively analyzed. Single-cell transcriptome sequencing data were used to assess the activation state of cellular senescence in the tumor microenvironment (TME). The expression of cellular senescence-associated hub genes varied significantly across cancer types. In these genes, missense mutation was the major type of single nucleotide polymorphism, and heterozygous deletion and heterozygous amplification were the major types of copy number variation. Moreover, the cellular senescence pathway in tumors was sensitive to drugs such as XMD13-2, TPCA-1, methotrexate, and KIN001-102. Furthermore, the cellular senescence score was significantly higher in most cancer types, related to poor prognosis. The expression of immune checkpoint molecules such as NRP1, CD276, and CD44 was significantly correlated with the cellular senescence score. Monocyte cellular senescence was significantly higher in the TME of kidney renal clear cell carcinoma cells than in normal tissues. The findings of this study provide insights into the important role of cellular senescence in the TIME of human cancers and the effect of immunotherapy.

A comprehensive pancancer analysis reveals the potential value of RAR-related orphan receptor C (RORC) for cancer immunotherapy

Background: RAR-related orphan receptor C (RORC) plays an important role in autoimmune responses and inflammation. However, its function in cancer immunity is still unclear. Its potential value in cancer immunotherapy (CIT) needs to be further studied.

Methods: Expression and clinical data for 33 cancers were obtained from UCSC-Xena. The correlation between RORC expression and clinical parameters was analyzed using the limma software package to assess the prognostic value of RORC. Timer2.0 and DriverDBv3 were used to analyze the RORC mutation and methylation profiles. RORC-associated signaling pathways were identified by GSEA. The correlations of RORC expression with tumor microenvironment factors were further assessed, including immune cell infiltration (obtained by CIBERSORT) and immunomodulators (in pancancer datasets from the Tumor-Immune System Interactions and Drug Bank [TISIDB] database). In addition, the correlations of RORC with four CIT biomarkers (tumor mutational burden, microsatellite instability, programmed death ligand-1, and mismatch repair) were explored. Furthermore, three CIT cohorts (GSE67501, GSE168204, and IMvigor210) from the Gene Expression Omnibus database and a previously published study were used to determine the association between RORC expression and CIT response.

Results: RORC was differentially expressed in many tumor tissues relative to normal tissues (20/33). In a small number of cancers, RORC expression was correlated with age (7/33), sex (4/33), and tumor stage (9/33). Furthermore, RORC expression showed prognostic value in many cancers, especially in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and mesothelioma (MESO). The mutation rate of RORC in most cancer types was low, while RORC was hypermethylated or hypomethylated in multiple cancers. RORC was associated with a variety of biological processes and signal transduction pathways in various cancers. Furthermore, RORC was strongly correlated with immune cell infiltration, immunomodulators, and CIT biomarkers. However, no significant association was found between RORC and CIT response in the three CIT cohorts.

Conclusion Our findings revealed the potential immunotherapeutic value of RORC for various cancers and provides preliminary evidence for the application of RORC in CIT.

Exploration of Hub Genes in Retinopathy of Prematurity Based on Bioinformatics Analysis of the Oxygen-Induced Retinopathy Model

Retinopathy of prematurity (ROP) is a major blindness-causing disease that is characterized by an arrest of normal vascular development and neovascularization of the retina. Previous studies have shown that genetic factors may be associated with the development and severity of ROP. However, the genes and mechanisms underlying ROP remain unclear. We aimed to identify hub genes in ROP and drugs related to these genes by integrative analysis. The expression profiles of GSE158799 and GSE135844 were acquired from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Then, an integrative analysis was performed including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, transcription factor (TF)-gene, and miRNA-gene networks analysis. Moreover, we verified hub genes and identified potential drugs. 225 common DEGs were identified. Biological function analysis indicated that angiogenesis, cell surface, cell adhesion, extracellular matrix, and focal adhesion genes were enriched among DEGs. The PI3K/Akt signalingpathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction were markedly enriched in the KEGG pathway analysis. Finally, 5 hub genes related to the nosogenesis of ROP were identified and found to be targeted by VEGFA inhibitors, TLR4 antagonists, and sunitinib. The present study showed that VEGFA, ACTA2, MKI67, CD68, and TLR4 are potential hub genes involved in the pathogenesis of ROP. Moreover, TLR4 antagonists and sunitinib may be new candidate drugs for ROP therapy, in addition to VEGFA inhibitors.

A prognostic signature of pyroptosis-related lncRNAs verified in gastric cancer samples to predict the immunotherapy and chemotherapy drug sensitivity

Background: Pyroptosis is a recently identified mode of programmed inflammatory cell death that has remarkable implications for cancer development. lncRNAs can be involved in cellular regulation through various pathways and play a critical role in gastric cancer (GC). However, pyroptosis -related lncRNAs (PRlncRNAs) have been rarely studied in GC.

Methods: Pyroptosis-related gene were abstracted from the literature and GSEA Molecular Signatures data resource. PRlncRNAs were obtained using co-expression analysis. LASSO Cox regression assessment was employed to build a risk model. Kaplan-Meier (KM), univariate along with multivariate Cox regression analysis were adopted to verify the predictive efficiency of the risk model in terms of prognosis. qRT-PCR was adopted to validate the expression of PRlncRNAs in GC tissues. In addition, immune cell infiltration assessment and ESTIMATE score evaluation were adopted for assessing the relationship of the risk model with the tumor immune microenvironment (TME). Finally, immune checkpoint gene association analysis and chemotherapy drug sensitivity analysis were implemented to assess the worthiness of our risk model in immunotherapy and chemotherapy of GC.

Results: We identified 3 key PRlncRNAs (PVT1, CYMP-AS1 and AC017076.1) and testified the difference of their expression levels in GC tumor tissues and neighboring non-malignant tissues (p < 0.05). PRlncRNAs risk model was able to successfully estimate the prognosis of GC patients, and lower rate of survival was seen in the high-GC risk group relative to the low-GC risk group (p < 0.001). Other digestive system tumors such as pancreatic cancer further validated our risk model. There was a dramatic difference in TMB level between high-GC and low-GC risk groups (p < 0.001). Immune cell infiltration analysis and ESTIMATE score evaluation demonstrated that the risk model can be adopted as an indicator of TME status. Besides, the expressions of immunodetection site genes in different risk groups were remarkably different (CTLA-4 (r = −0.14, p = 0.010), VISTA (r = 0.15, p = 0.005), and B7-H3 (r = 0.14, p = 0.009)). PRlncRNAs risk model was able to effectively establish a connection with the sensitivity of chemotherapeutic agents.

Conclusion: The 3 PRlncRNAs identified in this study could be utilized to predict disease outcome in GC patients. It may also be a potential therapeutic target in GC therapy, including immunotherapy and chemotherapy.

SAAL1, a novel oncogene, is associated with prognosis and immunotherapy in multiple types of cancer

Serum amyloid A-like 1 (SAAL1) was recently identified as a novel oncogene in hepatocellular carcinoma (HCC). To explore the potential role of SAAL1 in other cancers, we conducted a pan-cancer analysis of SAAL1 expression and its association with tumor microenvironment (TME) immunological profiles, sensitivity to chemotherapy agents, response to immunotherapy, and patient prognosis. SAAL1 was overexpressed in most malignant tumors in association with poor prognosis. Moreover, its expression was positively correlated with TME-relevant immune and mismatch signatures, immunostimulatory infiltrating cells (CD4⁺ memory T cells, activated NK cells, M1 macrophages, and cytotoxic CD8⁺ T cells), microsatellite instability (MSI), tumor mutational burden (TMB), neoantigen load, and immune checkpoint markers (PD-L1, LAG-3 and CTLA-4) in multiple cancers. SAAL1 overexpression was also associated with immunotherapy response and overall survival (OS) in bladder cancer (BLCA) patients who had received anti-PD-L1 treatment. Gene set enrichment analysis (GSEA) further showed significant enrichment of SAAL1 in immune cell signaling, cell cycle, and cell adhesion pathways. Moreover, we detected tumor-specific correlations between SAAL1 expression and either chemoresistance or sensitivity to common chemotherapeutics. Lastly, we showed that SAAL1 silencing suppresses both malignant phenotype and expression of PD-L1 in lung cancer A549 cells in vitro. These findings suggest that SAAL1 contributes to tumorigenesis and antitumor immunity mechanisms in different cancer types, and may thus serve as both a prognostic biomarker and potential target for cancer immunotherapy.

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

Mutant KRAS regulates transposable element RNA and innate immunity via KRAB zinc-finger genes

RAS genes are the most frequently mutated oncogenes in cancer, yet the effects of oncogenic RAS signaling on the noncoding transcriptome remain unclear. We analyzed the transcriptomes of human airway and bronchial epithelial cells transformed with mutant KRAS to define the landscape of KRAS-regulated noncoding RNAs. We find that oncogenic KRAS signaling upregulates noncoding transcripts throughout the genome, many of which arise from transposable elements (TEs). These TE RNAs exhibit differential expression, are preferentially released in extracellular vesicles, and are regulated by KRAB zinc-finger (KZNF) genes, which are broadly downregulated in mutant KRAS cells and lung adenocarcinomas in vivo. Moreover, mutant KRAS induces an intrinsic IFN-stimulated gene (ISG) signature that is often seen across many different cancers. Our results indicate that mutant KRAS remodels the repetitive noncoding transcriptome, demonstrating the broad scope of intracellular and extracellular RNAs regulated by this oncogenic signaling pathway.

Many human cancers are driven by mutant KRAS, but its effects on noncoding RNA are unclear. Reggiardo et al. show that mutant KRAS regulates this RNA landscape by silencing KRAB zinc-finger genes that normally repress transposable element noncoding RNAs, which are preferentially released from mutant KRAS cells in extracellular vesicles.

Integrative Analysis and Experimental Validation Indicated That SNHG17 Is a Prognostic Marker in Prostate Cancer and a Modulator of the Tumor Microenvironment via a Competitive Endogenous RNA Regulatory Network

The incidence of prostate cancer (PC) is growing rapidly worldwide, and studies uncovering the molecular mechanisms driving the progression and modulating the immune infiltration and antitumor immunity of PC are urgently needed. The long noncoding RNA SNHG family has been recognized as a prognostic marker in cancers and contributes to the progression of multiple cancers, including PC. In this study, we aimed to clarify the prognostic values and underlying mechanisms of SNHGs in promoting the progression and modulating the tumor microenvironment of PC through data mining based on The Cancer Genome Atlas (TCGA) database. We identified that within the SNHG family, SNHG17 was most correlated with the overall survival of PC patients and could act as an independent predictor. Moreover, we constructed a competitive endogenous RNA (ceRNA) network by which SNHG17 promotes progression and potentially inhibits the immune infiltration and immune response of prostate cancer. By interacting with miR-23a-3p/23b-3p/23c, SNHG17 upregulates the expression of UBE2M and OTUB1, which have been demonstrated to play critical roles in the tumorigenesis of human cancers, more importantly promoting cancer cell immunosuppression and resistance to cytotoxic stimulation. Finally, we examined the correlation between SNHG17 expression and the clinical progression of PC patients based on our cohort of 52 PC patients. We also verified the SNHG17/miR-23a/OTUB1 axis in RV-1 and PC-3 cells by dual luciferase and RIP assays, and we further identified that SNHG17 promoted cellular invasive capacity by modulating OTUB1. In summary, the current study conducted a ceRNA-based SNHG17-UBE2M/OTUB1 axis and indicated that SNHG17 might be a novel prognostic factor associated with the progression, immunosuppression, and cytotoxic resistance of PC.

MDIG, a 2‑oxoglutarate‑dependent oxygenase, acts as an oncogene and predicts the prognosis of multiple types of cancer

Recent studies have indicated that mineral dust‑induced gene (MDIG) is an oncogene induced by environmental factors, which has a key role in the development and progression of various tumor types, through epigenetic modifications; however, there are no previous pan‑cancer analyses of MDIG. In the present study, a comprehensive pan‑cancer analysis of MDIG was performed using public databases. The results demonstrated that MDIG was upregulated in tumor tissue samples compared with normal tissue, that it was present in all cancer cell lines and it was closely associated with the prognosis of patients with different tumor types. Furthermore, MDIG expression was closely associated with the immunological characteristics of the tumor microenvironment (TME), such as the frequency of tumor‑infiltrating immune cells, TME‑relevant signatures, immunostimulatory genes, immune checkpoint genes, chemokine receptor genes, tumor mutational burden and microsatellite instability. In parallel, high expression of MDIG was associated with improved overall survival of patients and this was verified in a cohort of patients who had received anti‑programmed cell death 1 ligand 1 treatment. Furthermore, high expression of MDIG led to multiple drug resistance in The Cancer Genome Atlas‑lung adenocarcinoma cohort. In addition, gene set variant analysis and gene set enrichment analysis indicated that MDIG was involved in cell cycle regulation. In vitro experiments suggested that MDIG promoted cell proliferation through the mTOR complex 2/Akt and pyruvate dehydrogenase kinase 1/Akt signaling pathways. In summary, the present study suggests that MDIG may be a prognostic biomarker and therapeutic target for various cancer types.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

Background

Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa).

Methods

We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m⁶A methylation genes.

Results

Kaplan–Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma − associated herpesvirus infection, IL − 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K − Akt signaling pathway and HIF − 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m⁶A methylation-related genes were significantly differentially expressed between the two risk groups.

Conclusion

A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09805-9.

A Novel Overall Survival Prediction Signature Based on Comprehensive Research in Prostate Cancer Bone Metastases

BACKGROUND

Prostate adenocarcinoma (PRAD)-related bone metastases are a leading source of morbidity and mortality; however, good diagnostic biomarkers are not known yet. The aim of this study was to identify biomarkers and prognostic indicators for the diagnosis and treatment of PRAD-associated bone metastases.

METHODS

By combining the data from The Cancer Genome Atlas(TCGA) and PRAD SU2C 2019, We performed a comprehensive analysis of the expression differences, biological functions, and interactions of genes associated with PRAD bone metastasis. Annotation, visualization, and integrated discovery were accomplished through the use of gene ontology enrichment and gene set enrichment analysis. The protein-protein interaction network was constructed using the STRING database, and the diagnostic value of prognostic genes was validated using receiver-operating-characteristic and Kaplan-Meier curves.

RESULTS

Six genes (DDX47, PRL17, AS3MT, KLRK1, ISLR, and S100A8) associated with PRAD bone metastases were identified; these had prognostic value as well. Among them, enrichment was observed for the biological processes extracellular matrix tissue, extracellular structural tissue, steroid hormone response, and cell oxidative detoxification. KEGG analysis revealed enrichment in interactions with extracellular matrix receptors, diseases including Parkinson's disease and dilated cardiomyopathy, and estrogen signaling pathways. The area under the curve values of 0.8938, 0.9885, and 0.979, obtained from time-dependent receiver-operating-characteristic curve analysis for 1, 3, and 5-year overall survival confirmed the good performance of the model under consideration. S100A8 expression was not detected in the normal prostate tissue but was detected in PRAD.

CONCLUSIONS

We identified ISLR as a potential biomarker for PRAD bone metastasis. Moreover, the genes identified to have prognostic value may act as therapeutic targets for PRAD bone metastasis.

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (&lt;1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens &lt;1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

Comprehensive molecular analysis of a four-pyroptosis-gene signature with prognosis and immune landscape in lung adenocarcinoma

Pyroptosis plays an important role in tumor immunity. However, the biological behavior and prognostic significance of pyroptosis remain unclear. We identified 41 pyroptosis regulators differently expressed in lung adenocarcinoma (LUAD). All cases of LUAD can be classified into two molecular subtypes using unsupervised clustering algorithm. Using multiple analyses, a four-pyroptosis-gene signature was constructed, and all LUAD patients were categorized as low-risk or high-risk with a longer overall survival (OS) time in the low-risk group(P < 0.001). This signature had power prognosis and stratification which was validated by six independent datasets and clinical subtypes. Besides, this signature showed distinct clinical outcomes, immune landscapes in different risk groups. Moreover, the low-risk group had a higher response against immunotherapy with a lower TIDE score. Importantly, this signature surpassed other biomarkers (TIDE, TMB, PD-L1) in predicting prognosis. Overall, the current study might help with precise prognostic prediction and crucial treatment strategies, eventually promoting tailored therapy for LUAD patients.

In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer

Background

Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer.

Methods

RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation data and in vitro experiments were used to verify the results from in silico analysis.

Results

Bladder cancer samples were grouped into two clusters based on the NMF algorithm. A total of 1467 genes involved in coexpression modules were identified in WGCNA. We finally established a 5-gene signature (TM4SF1, KCNK5, FASN, IMPDH1, and KCNJ15) that exhibited good stability across different datasets and was also an independent risk factor for prognosis. Furthermore, the predictive efficacy of our model was generally higher than the predictive efficacy of other published models. Distinct risk groups of patients also showed significantly different immune infiltration cell patterns and associations with clinical variables. Moreover, the 5 signature genes were verified in clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry, which were in agreement with the in silico analysis. For in vitro experiments, knockdown of IMPDH1 markedly inhibited cell proliferation in bladder cancer.

Conclusion

We established a 5-gene prognosis signature based on lipid metabolism in bladder cancer, which could be an effective prognostic indicator.

Downregulation of Low-density lipoprotein receptor-related protein 1B (LRP1B) inhibits the progression of hepatocellular carcinoma cells by activating the endoplasmic reticulum stress signaling pathway

Hepatocellular carcinoma (HCC) has a high recurrence rate and mortality rate even after surgery. Low-density lipoprotein receptor-related protein 1B (LRP1B) has proven to be involved in tumor development and progression of multiple malignancies. However, the function of LRP1B in HCC progression has not been fully elucidated. Thus, we conducted this study to explore the relationship between LRP1B and HCC. Bioinformatic analyses implied that LRP1B was highly expressed in HCC tissues. High LRP1B expression was shown to be related to poor outcomes and the determination of HCC patients’ tumor stage. LRP1B deletion impeded the proliferation, migration, and invasion of HCC cells. Further investigation demonstrated that silencing LRP1B expression enhanced the sensitivity of HCC cells to doxorubicin. LRP1B deletion inhibited HCC progression by regulating the PERK-ATF4-CHOP signaling pathway. Additionally, we probed the genomic alterations of LRP1B in HCC and the impact on the prognosis of patients. Collectively, our results suggest that LRP1B plays an essential role in the promotion of HCC progression by regulating the PERK-ATF4-CHOP signaling pathway, which is a potential prognostic biomarker and a promising therapeutic target of HCC.

The value of WNT5A as prognostic and immunological biomarker in pan-cancer

Background

Finding new immune-related biomarkers is one of the promising research directions for tumor immunotherapy. The WNT5A gene could stimulate the WNT pathway and regulate the progression of various tumors. Recent studies have partially revealed the relationship between WNT5A and tumor immunity, but the correlation and underlying mechanisms in pan-cancer remain obscure. Thus, we conducted this study aiming to characterize the prognostic value and immunological portrait of WNT5A in cancer.

Methods

The data obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases was utilized to analyze WNT5A expression levels by Kruskal-Wallis test and correlation to prognosis by Cox regression test and Kaplan-Meier test, while the data was also used to study the association between WNT5A expression and immune microenvironment, immune neoantigens, immune checkpoints, tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer. Gene set enrichment analysis (GSEA) was used to clarify the relevant signaling pathways. The R package was used for data analysis and to create the plots.

Results

The pan-cancer analysis revealed that the expression level of WNT5A is generally elevated in most tumors (19/34, 55.88%), and high WNT5A expression was correlated with poor prognosis in esophageal carcinoma (ESCA, P<0.05), low-grade glioma (LGG, P<0.01), adrenocortical carcinoma (ACC, P<0.01), pancreatic adenocarcinoma (PAAD, P<0.01), and head and neck squamous cell carcinoma (HNSC, P<0.05). In addition, WNT5A expression was positively associated with immune infiltration, stromal score, and immune checkpoints in most cancers, and correlated to immune neoantigens, TMB, and MSI. Finally, GSEA indicated that WNT5A is implicated in the transforming growth factor β (TGFβ), Notch, and Hedgehog signaling pathways, which may be related to tumor immunity.

Conclusions

The expression of WNT5A is elevated in most tumors and associated with tumor prognosis. Furthermore, WNT5A is associated with tumor immunity and may be an immunological biomarker in cancer.