Clinical Efficacy and Mechanism of Vitamin D2 in Treating Hashimoto's Thyroiditis

Objective

Hashimoto's thyroiditis (HT) is one of the most common autoimmune diseases, with the highest incidence rate among autoimmune thyroid disorders. Vitamin D2 may have therapeutic effects on HT. This study aimed to elucidate the molecular mechanisms underlying vitamin D2 therapy for HT.

Methods

Differentially expressed genes (DEGs) associated with vitamin D2-treated HT were identified, and the DEG-associated gene enrichment pathway was explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The correlation between the hub genes and infiltrating immune cells was investigated, and the interactions among the hub genes and target drug and competing endogenous RNA (ceRNA; long non-coding RNA [lncRNA]-microRNA [miRNA]-messenger RNA [mRNA]) regulatory networks were determined.

Results

GO and KEGG enrichment analyses identified a total of 102 DEGs (6 upregulated and 96 downregulated) in the vitamin D2-treated group samples. The area under the curve values of the identified 10 hub genes was as follows: CCR1(0.920), CXCL1 (0.960), CXCL8 (0.960), EGR1 (0.960), FCGR3B (0.920), FOS (1.000), FPR1 (0.840), MMP9 (0.720), PTGS2 (0.960), and TREM1 (1.000). The immune enrichment scores of the mast cell (P = 0.008), neutrophil (P = 0.016), and plasmacytoid dendritic cell (P = 0.016) were significantly decreased in the vitamin D2-treated group (P < 0.05). The hub gene/drug regulatory network included 8 hub genes, 108 molecular drugs, and 114 interaction relationship pairs. The ceRNA regulatory network included 129 lncRNAs, 145 miRNAs, mRNAs (hub genes), and 324 interaction relationship pairs.

Conclusion

Vitamin D2 may play an immunomodulatory role by regulating the aforementioned immune-related molecules and immune cells, thereby improving its therapeutic effects on HT.

Multi-Omics Analysis of the Oncogenic Value of Pituitary Tumor-Transforming Gene 1 (PTTG1) in Human Cancers

BACKGROUND

The pituitary tumor-transforming gene 1 (PTTG1), also recognized as securin, plays a crucial role in diverse biological processes, such as restraining sister chromatid segregation, facilitating DNA repair, contributing to organ development, and governing angiogenesis. Additionally, it regulates the expression and secretion of transfer factors. The epigenetic characteristics of PTTG1 suggest its potential in elucidating the progression of malignant tumors in pan-cancer. Nevertheless, the current comprehension of this relationship remains limited, necessitating further comprehensive studies to delve into the underlying pathogenesis.

METHODS

This investigation aimed to explore the potential functions of PTTG1 in pan-cancer by leveraging existing databases, such as TCGA and GTEx. Notably, PTTG1 was overexpressed in nearly all tumors, indicating promising prognostic and diagnostic capabilities. Moreover, the observed correlation between PTTG1 and immune cell infiltration, immune checkpoint genes, tumor mutational burden (TMB), microsatellite instability (MSI), and other immune features suggests its potential utility as a guide for immunotherapy.

RESULTS

The study unveils that the downregulation of PTTG1 expression in neuroblastoma results in reduced cell proliferation and increased apoptosis, substantiating the proposition that PTTG1 could serve as both a prognostic biomarker and a potential target for immunotherapy across various cancer types.

CONCLUSIONS

This study centers on the exploration of the expression and role of PTTG1 in both tumors and the tumor microenvironment (TME), offering valuable insights for the development of cancer therapeutic strategies. These discoveries present potential alternative avenues for addressing clinically resistant cancers.

Mitochondrial-related genes PDK2, CHDH, and ALDH5A1 served as a diagnostic signature and correlated with immune cell infiltration in ulcerative colitis

We conducted an investigation to determine the potential of mitochondrial-related genes as diagnostic biomarkers in ulcerative colitis (UC), while also examining their association with immune cell infiltration. To achieve this, we acquired four datasets pertaining to UC, which included gene expression arrays and clinical data, from the GEO database. Subsequently, we selected three signature genes (PDK2, CHDH, and ALDH5A1) to construct a diagnostic model for UC. The nomogram and ROC curves exhibited exceptional diagnostic efficacy. Following this, quantitative real-time polymerase chain reaction and western blotting assays validated the decreased mRNA and protein expression of PDK2, CHDH, and ALDH5A1 in the model of UC cells and dextran sulfate sodium salt (DSS)-induced mice colitis tissues, aligning with the findings in the risk model. This investigation suggested a negative correlation between the expression of ALDH5A1, CHDH, and PDK2 and the infiltration of M1 macrophages. Then, immunofluorescence analysis confirmed the augmented expression of CD86 in the tissue of mice subjected to DSS, while a diminished expression of ALDH5A1, CHDH, and PDK2 was observed. Consequently, it can be inferred that targeting mitochondria-associated genes, namely PDK2, CHDH, and ALDH5A1, holds potential as a viable strategy for prognostic prediction and the implementation of immune therapy for UC.

CD274 (PD-L1) negatively regulates M1 macrophage polarization in ALI/ARDS

Background

Acute lung injury (ALI)/severe acute respiratory distress syndrome (ARDS) is a serious clinical syndrome characterized by a high mortality rate. The pathophysiological mechanisms underlying ALI/ARDS remain incompletely understood. Considering the crucial role of immune infiltration and macrophage polarization in the pathogenesis of ALI/ARDS, this study aims to identify key genes associated with both ALI/ARDS and M1 macrophage polarization, employing a combination of bioinformatics and experimental approaches. The findings could potentially reveal novel biomarkers for the diagnosis and management of ALI/ARDS.

Methods

Gene expression profiles relevant to ALI were retrieved from the GEO database to identify co-upregulated differentially expressed genes (DEGs). GO and KEGG analyses facilitated functional annotation and pathway elucidation. PPI networks were constructed to identify hub genes, and differences in immune cell infiltration were subsequently examined. The expression of hub genes in M1 versus M2 macrophages was evaluated using macrophage polarization datasets. The diagnostic utility of CD274 (PD-L1) for ARDS was assessed by receiver operating characteristic (ROC) analysis in a validation dataset. Experimental confirmation was conducted using two LPS-induced M1 macrophage models and an ALI mouse model. The role of CD274 (PD-L1) in M1 macrophage polarization and associated proinflammatory cytokine production was further investigated by siRNA-mediated silencing.

Results

A total of 99 co-upregulated DEGs were identified in two ALI-linked datasets. Enrichment analysis revealed that these DEGs were mainly involved in immune-inflammatory pathways. The following top 10 hub genes were identified from the PPI network: IL-6, IL-1β, CXCL10, CD274, CCL2, TLR2, CXCL1, CCL3, IFIT1, and IFIT3. Immune infiltration analysis revealed a significantly increased abundance of M1 and M2 macrophages in lung tissue from the ALI group compared to the control group. Subsequent analysis confirmed that CD274 (PD-L1), a key immunological checkpoint molecule, was highly expressed within M1 macrophages. ROC analysis validated CD274 (PD-L1) as a promising biomarker for the diagnosis of ARDS. Both in vitro and in vivo experiments supported the bioinformatics analysis and confirmed that the JAK-STAT3 pathway promotes CD274 (PD-L1) expression on M1 macrophages. Importantly, knockdown of CD274 (PD-L1) expression potentiated M1 macrophage polarization and enhanced proinflammatory cytokines production.

Conclusion

This study demonstrates a significant correlation between CD274 (PD-L1) and M1 macrophages in ALI/ARDS. CD274 (PD-L1) functions as a negative regulator of M1 polarization and the secretion of proinflammatory cytokines in macrophages. These findings suggest potential new targets for the diagnosis and treatment of ALI/ARDS.

Notch signaling pathway-based classification of bladder cancer in relation to tumor immune infiltration

Introduction

The role of the Notch signaling pathway in the development of various tumors has received increasing attention, but the relationship between the Notch signaling pathway and the prognosis of bladder cancer has rarely been studied. The aim of this study was to investigate the function and risk evaluation value of Notch signaling pathway-related genes (NRGs) in bladder cancer.

Material and methods

The list of genes related to the Notch signaling pathway was obtained from the molecular signature database. The bladder cancer dataset was obtained from The Cancer Genome Atlas (TCGA) database. Cox regression analysis and Lasso regression analysis were used to construct the characteristics for predicting the overall survival of patients with bladder cancer. The CIBERSORT algorithm was used to evaluate the infiltration of peripheral immune cells in different risk subgroups.

Results

NRG expression was remarkably dysregulated in bladder cancer. Next, bladder cancer was classified into two subtypes (C1 and C2) based on NRG expression levels. The two subtypes had a significant difference in prognosis and were closely related to clinical characteristics. Further analysis showed that immune cell infiltration and immune scores were also significantly different between the two subtypes.

Conclusions

Notch signaling pathway-based bladder cancer typing has different prognoses and may be related to tumor immunity. NRGs can be identified for risk evaluation and help improve clinical decision-making.

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma

Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.

Identification of hypoxic-related lncRNAs prognostic model for revealing clinical prognostic and immune infiltration characteristic of cutaneous melanoma

BACKGROUND

Cutaneous melanoma (CM) remains a significant threat to human health. There are clues to the potential role of hypoxia in CM progression. However, the role of hypoxia-related lncRNAs (HRLs) in CM has not been clarified.

METHODS

We obtained hypoxia related genes from MSigDB database and subsequently identified HRLs by applying TCGA database. LASSO-univariate and multivariate Cox analysis were used to comprehensively analyze the survival characteristics and HRLs expressions, and a novel HRLs-related prognostic risk model was subsequently established for comprehensive analysis.

RESULTS

The established risk model could evaluate the clinical outcome of CM accurately. The ability of the model-related risk score was also validated as an independent prognostic indicator of CM. Immune infiltration, TMB analysis, drug sensitivity analysis and immunotherapy evaluation were conducted to comprehensively assess the possible causes of the difference in prognosis. The reliability of bioinformatics results was partially verified by RT-qPCR.

CONCLUSION

We established a new HRLs related risk model and discussed the potential role of hypoxia in the development of CM, which provided a novel basis for CM risk stratification.

FLNB overexpression promotes tumor progression and associates with immune suppression, evasion and stemness in pancreatic cancer

Pancreatic cancer (PC) is an immunosuppressive cancer. Immune-based therapies that enhance or recruit antitumor immune cells into the tumor microenvironment (TME) remain promising strategies for PC treatment. Consequently, a deeper understanding of the molecular mechanisms involved in PC immune suppression is critical for developing immune-based therapies to improve survival rates. In this study, weighted gene co-expression network analysis (WGCNA) was used to identify Filamin B (FLNB) correlated with the infiltration of CD8+ T cells and tumor-associated macrophages (TAMs). The clinical significance and potential biological function of FLNB were evaluated using bioinformatic analysis. The oncogenic role of FLNB in PC was determined using in vitro and in vivo studies. We further analyzed possible associations between FLNB expression and tumor immunity using CIBERSORT, single sample gene set enrichment analysis, and ESTIMATE algorithms. We found FLNB was overexpressed in PC tissues and was correlated with poorer overall survival, tumor recurrence, larger tumor size, and higher histologic grade. Moreover, FLNB overexpression was associated with the mutation status and expression of driver genes, especially for KRAS and SMAD4. Functional enrichment analysis identified the role of FLNB in the regulation of cell cycle, focal adhesion, vascular formation, and immune regulation. Knockdown of FLNB expression inhibited cancer cell proliferation and migration in-vitro and suppressed tumor growth in-vivo. Furthermore, FLNB overexpression caused high infiltration of Treg cells, Th2 cells, and TAMs, but reduced infiltration of CD8+ T cells and Th1/Th2. Collectively, our findings suggest FLNB promotes PC progression and may be a novel biomarker for PC.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning

Background

The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.

Methods

Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.

Results

Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.

Conclusion

In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies.

OBJECTIVE

To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD.

METHODS

We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD.

RESULTS

Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes.

CONCLUSIONS

Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

Assessment the value of Pyroptosis-Associated Gasdermin family genes in hepatocellular carcinoma: A Multi-Omics Comprehensive Analysis

Background: Hepatocellular carcinoma (HCC) is one of the common primary cancers of the liver worldwide and leading cause of mortality. Gasdermins (GSDMs) family genes play an important role in the regulation of the normal physiological processes and have been implicated in multiple diseases. However, little is known about the relationship between different GSDMs proteins and HCC. The aim of this study was to explore the potential relationship between the expression, prognosis, genetic variation and immune infiltration of GSDMs family genes and HCC. Methods: We used different bioinformatics common public databases such as GSCA, GEPIA, UALCAN, HPA, Kaplan-Meier Plotter, LinkedOmics, GeneMANIA, STRING, cBioPortal, TIMER and TISIDB to analyze the differential expression of the different GSDMs, prognostic value, genetic alterations, immune cell infiltration and their functional networks in HCC patients. Results: All the members of the GSDMs family exhibited elevated mRNA expression levels in LIHC compared to the normal tissues, while only GSDMB, GSDMD and GSDME showed enhanced protein expression. The mRNA expression of most GSDMs members was found to be elevated in HCC patients at stages I-III (clinical stage) compared to the normal subjects. The expression of GSDMD was correlated with OS and DSS of patients, whereas GSDME was correlated with OS, DSS and RFS of patients. Gene amplification was observed to be main mode of variation in members of the GSDMs family. KEGG pathway analysis showed that genes associated with different members of the GSDMs family were enriched in the pathways of S. aureus infection, intestinal immunity, ribosome and protein assembly, oxidative phosphorylation, osteoclast differentiation and Fc gamma (γ) R-mediated phagocytosis. In addition, expression of both GSDMA and GSDME were found to be correlated most significantly with infiltration of immune cells, while GSDMA and GSDME somatic cell copy number alteration (CAN) were correlated significantly with the infiltration of immune cells. All GSDMs were noted to be associated with distinct subtypes of immune cells, except GSDMC. Conclusions: Our findings have provided useful insights to better understand the roles and functions of GSDMs in HCC that can provide novel direction for developing therapeutic modalities for HCC, including immunotherapy.

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation

Introduction

Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.

Methods

We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.

Results

In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.

Conclusion

This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

Comprehensive analysis of prognostic value and immune infiltration of Regulator of Chromosome Condensation 2 in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) incidence and mortality take the leading place of most malignancies. Previous studies have revealed the regulator of chromosome condensation 1 (RCC1) family members played an essential role during tumorigenesis. However, its biological functions in LUAD still need further investigation. Methods: Several databases were applied to explore potential effects of RCC1 family members on LUAD, such as Oncomine, GEPIA, and cBioPortal. Real-time PCR and immunohistochemistry were used to verify the expression of RCC2 in stage I LUAD. H1975 and A549 were selected to explore the biological function of RCC2 in cellular malignant phenotype. Results: The expressions of RCC1 and RCC2 showed marked differences in malignant tissue compared to lung tissue. The higher the expression levels of RCC1 or RCC2 in LUAD patients, the shorter their overall survival (OS). In normal lung tissues, RCC1 expression was highly enriched in alveolar cells and endothelial cells. Compare with RCC1, RCC2 expression in normal lung tissue was significantly enriched in macrophages, B cells and granulocytes. Additionally, RCC2 expression level was correlated with multiple immune cell infiltration in LUAD. Moreover, the mutation or different sCNA status of RCC2 exerted influence on multiple immune cell infiltration distribution. We found that the upregulation of RCC1 and RCC2 were obviously related to TP53 mutation. GSEA analysis revealed that RCC2 was involved in the process of DNA replication, nucleotide excision repair and cell cycle, which might affect tumor progression through P53 signaling pathway. We further elucidated that downregulation of RCC2 could dramatically repress the migration and invasion of LUAD cells. Conclusions: The study demonstrated that RCC1 and RCC2 expression were markedly increased in early-stage of LUAD. Patients with high expression of RCC1 or RCC2 had a worse prognosis. Based on our analysis, RCC1 and RCC2 might exert influence on LUAD process through DNA replication, nucleotide excision repair and cell cycle, as well as cells migration and invasion. Different from RCC1, RCC2 also involved in immune infiltration. These analyses provided a novel insight into the identification of diagnostic biomarker.

SQLE is a promising prognostic and immunological biomarker and correlated with immune Infiltration in Sarcoma

Squalene epoxidase (SQLE) is an essential enzyme involved in cholesterol biosynthesis. However, its role in sarcoma and its correlation with immune infiltration remains unclear. All original data were downloaded from The Cancer Genome Atlas (TCGA). SQLE expression was explored using the TCGA database, and correlations between SQLE and cancer immune characteristics were analyzed via the TISIDB databases. Generally, SQLE is predominantly overexpressed and has diagnostic and prognostic value in sarcoma. Upregulated SQLE was associated with poorer overall survival, poorer disease-specific survival, and tumor multifocality in sarcoma. Mechanistically, we identified a hub gene that included a total of 82 SQLE-related genes, which were tightly associated with histone modification pathways in sarcoma patients. SQLE expression was negatively correlated with infiltrating levels of dendritic cells and plasmacytoid dendritic cells and positively correlated with Th2 cells. SQLE expression was negatively correlated with the expression of chemokines (CCL19 and CX3CL1) and chemokine receptors (CCR2 and CCR7) in sarcoma. In conclusion, SQLE may be used as a prognostic biomarker for determining prognosis and immune infiltration in sarcoma.

Hypoxia-derived molecular subtype and gene signature characterize prognoses and therapeutic responses in head and neck squamous cell carcinoma

Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. This study aims to investigate the role of hypoxia-related genes (HRG) in the immune landscape, treatment response, and prognosis of head and neck squamous cell carcinoma (HNSCC). The transcriptome and clinical data of HNSCC were downloaded from TCGA and GEO databases, and HNSCC molecular subtypes were identified using non-negative matrix factorization (NMF) clustering. Prognostic models were constructed using univariate, Lasso, and multivariate Cox regression analyses. The relationship between HRGs and immune cell infiltration, immune therapy response, and drug sensitivity was evaluated, and a nomogram was constructed. 47 HRGs were differentially expressed in HNSCC, among which 10 genes were significantly associated with HNSCC prognosis. Based on these 10 genes, 2 HNSCC molecular subtypes were identified, which showed significant heterogeneity in terms of prognosis, immune infiltration, and treatment response. A total of 3280 differentially expressed genes were identified between the subtypes. After univariate, Lasso, and multivariate Cox regression analysis, 18 genes were selected to construct a novel prognostic model, which showed a significant correlation with B cells, T cells, and macrophages. Using this model, HNSCC was classified into high-risk and low-risk groups, which exhibited significant differences in terms of prognosis, immune cell infiltration, immune therapy response, and drug sensitivity. Finally, a nomogram based on this model and radiotherapy was constructed, which showed good performance in predicting HNSCC prognosis and guiding personalized treatment strategies. The decision curve analysis demonstrated its better clinical applicability compared to other strategies. HRGs can identify 2 HNSCC molecular subtypes with significant heterogeneity, and the HRG-derived risk model has the potential for prognostic prediction and guiding personalized treatment strategies.

Single-cell and Bulk RNA-Seq reveal angiogenic heterogeneity and microenvironmental features to evaluate prognosis and therapeutic response in lung adenocarcinoma

Background

Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma (LUAD), intricately shaping the tumor microenvironment (TME) and influencing LUAD progression. It emerges as a promising therapeutic target for LUAD, affecting patients' prognosis. However, its role in TME, LUAD prognosis, and its clinical applicability remain shrouded in mystery.

Methods

We employed integrated single-cell and bulk transcriptome sequencing to unravel the heterogeneity of angiogenesis within LUAD cells. Through "consensus clustering", we delineated distinct angiogenic clusters and deciphered their TME features. "Monocle2" was used to unravel divergent trajectories within malignant cell subpopulations of LUAD. Additionally, regulon submodules and specific cellular communication patterns of cells in different angiogenic states were analyzed by "pyscenic" and "Cellchat" algorithms. The "univariate Cox" and "LASSO" algorithms were applied to build angiogenic prognostic models. Immunohistochemistry (IHC) on clinical samples validated the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM databases for pinpointing sensitive drugs against lung adenocarcinoma.

Results

Two clusters for the activation of angiogenesis were identified, with Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three differentiated states of malignant epithelial LUAD cells were identified, which had different degrees of angiogenic activation, were regulated by three different regulon submodules, and had completely different crosstalk from other cells in TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS (Angiogenesis related score) had a high prognostic value; low ARSs showed immunotherapy benefits, whereas high ARSs were sensitive to 15 chemotherapeutic agents.

Conclusion

The assessment of angiogenic clusters helps to determine the prognostic and TME characteristics of LUAD. Angiogenic prognostic models can be used to assess the prognosis, immunotherapeutic response, and chemotherapeutic drug sensitivity of LUAD.

Mismatch repair gene MSH6 correlates with the prognosis, immune status and immune checkpoint inhibitors response of endometrial cancer

Objective

Many patients treated with immune checkpoint inhibitors (ICIs) developed primary or secondary drug resistance for unknown reasons. This study investigates whether mismatch repair (MMR) genes are responsible for this therapeutic restriction.

Methods

We obtained the transcriptional, clinical and single nucleotide polymorphism data for endometrial cancer (EC) from The Cancer Genome Atlas and the immunophenoscore data of EC from The Cancer Immunome Atlas, then analyzed in R to evaluate the relationship between MMR genes and clinicopathological features, prognosis, immune infiltration, immune checkpoint expression and responsiveness to ICIs in EC. We used differentially expressed genes in the MSH6 high and low expression groups to conduct GO and KEGG analyses to explore the impact of MSH6 on the biological functions of EC. Finally, we verified the bioinformatics results with in vitro experiments.

Results

Our analyses showed that compared with the high MSH6 expression group, the low MSH6 expression group had better survival outcomes and less aggressive clinicopathological features. In the multivariate Cox analysis, MSH6 was the only independent risk factor that could predict the prognosis of EC. Besides, the low MSH6 expression group also had a higher immune score, more active immune infiltration and higher immune checkpoint expression, resulting in better responsiveness to ICIs treatment, consistent with the enrichment of GO terms and KEGG pathways related to immune response in this group. Meanwhile, the GO and KEGG enrichment results of the MSH6 high expression group were associated with cell cycle, DNA damage repair and tumorigenesis. To exclude the influence of MSH6 mutations, we performed the previous analyses on the MSH6 wild-type tumor samples and obtained consistent results. In vitro experiments also confirmed that after knocking down MSH6 in endometrial cancer cells, their proliferation, migration and invasion abilities were weakened, while the expression levels of PD-L1 and PD-L2 were elevated. In comparison, overexpression of MSH6 showed an opposite trend.

Conclusion

Reduced MSH6 expression could serve as a potential biomarker for predicting better prognosis, active immune status, higher immune checkpoint expression level and better responsiveness to ICIs treatment in EC. MSH6 may become a potential target for treating solid tumors.

Machine learning-based B cell-related diagnostic biomarker signature and molecular subtypes characteristic of ulcerative colitis

As an inflammatory bowel disease, ulcerative colitis (UC) does not respond well to current treatments. It is of positive clinical significance to further study the pathogenesis of UC and find new therapeutic targets. B lymphocytes play an important role in the pathogenesis of UC. The effect of anti-CD20 therapy on UC also provides new evidence for the involvement of B cells in UC process additionally, suggesting the important role and potential therapeutic value of B cells in UC. In this study, we screened the most critical immune cell-related gene modules associated with UC and found that activated B cells were closely related to the gene modules. Subsequently, key activated B cell-associated gene (BRG) signatures were obtained based on WGCNA and differential expression analysis, and three overlapping BRG-associated genes were obtained by RF and LASSO algorithms as BRG-related diagnostic biomarkers for UC. Nomogram model was further performed to evaluate the diagnostic ability of BRG-related diagnostic biomarkers, subsequently followed by UC molecular subsets identification and immunoinfiltration analysis. We also further verified the expressions of the three screened BRGs in vitro by using an LPS-induced NCM460 cell line model. Our results provide new evidence and potential intervention targets for the role of B cells in UC from a new perspective.

An individual-based model to explore the impact of psychological stress on immune infiltration into tumour spheroids

In recentin vitroexperiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-γand IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-γand IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

Purpose

Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC.

Methods

We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC.

Results

Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo.

Conclusion

Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

BZW1 is a prognostic and immunological biomarker in pancreatic adenocarcinoma

Pancreatic adenocarcinoma is the most common malignant tumor of the digestive system and is called the "king of cancer" because it has been labeled with high malignancy, rapid progression, poor survival, and poor prognosis. Previously, it was reported that the basic leucine zipper and W2 domains 1 (BZW1) is involved in the progression of many tumors. However, its research in digestive system tumors such as pancreatic cancer is rarely studied. To explore potential biomarkers related to survival and prognosis of pancreatic cancer and provide a new targeted therapy for it. We first analyzed the mRNA and protein expression of BZW1 in pancreatic cancer. We then explored the correlation of BZW1 with survival prognosis and immune infiltration in pancreatic cancer patients. Finally, we explored BZW1-related gene enrichment analysis, including protein-protein interaction networks, gene ontology functional enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The mRNA and protein expression of the BZW1 gene in pancreatic cancer tissues were higher than those in adjacent normal tissues, and pancreatic cancer patients with high BZW1 expression had a poor prognosis. In addition, the expression of BZW1 was positively or negatively correlated with different immune cells of pancreatic cancer, such as CD4 + T lymphocytes, CD8 + T lymphocytes, B cells, macrophages, neutrophils, etc. Correlation enrichment analysis showed that we obtained 50 available experimentally determined BZW1-binding proteins and 100 targeted genes related to BZW1, and the intersection genes were eukaryotic translation termination factor 1 and Guanine nucleotide binding protein, alpha inhibiting activity polypeptide 3. Moreover, there was a positive correlation between BZW1 and eukaryotic translation termination factor 1 and Guanine nucleotide binding protein, alpha inhibiting activity polypeptide 3 genes in pancreatic cancer. Gene ontology enrichment analysis showed BZW1 was mainly related to biological processes such as "mRNA processing," "RNA splicing," "regulation of translational initiation," and "activation of innate immune response." The results of Kyoto Encyclopedia of Genes and Genomes pathway analysis further indicated that BZW1 may be involved in pancreatic carcinogenesis through the "spliceosome" and "ribosome." The BZW1 gene may be a potential immunotherapy target and a promising prognostic marker for pancreatic cancer.

Pan-cancer analyses of bromodomain containing 9 as a novel therapeutic target reveals its diagnostic, prognostic potential and biological mechanism in human tumours

BACKGROUND

Mutations in one or more genes responsible for encoding subunits within the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin-remodelling complexes are found in approximately 25% of cancer patients. Bromodomain containing 9 (BRD9) is a more recently identified protein coding gene, which can encode SWI/SNF chromatin-remodelling complexes subunits. Although initial evaluations of the potential of BRD9-based targeted therapy have been explored in the clinical application of a small number of cancer types, more detailed study of the diagnostic and prognostic potential, as well as the detailed biological mechanism of BRD9 remains unreported.

METHODS

We used various bioinformatics tools to generate a comprehensive, pan-cancer analyses of BRD9 expression in multiple disease types described in The Cancer Genome Atlas (TCGA). Experimental validation was conducted in tissue microarrays and cell lines derived from lung and colon cancers.

RESULTS

Our study revealed that BRD9 exhibited elevated expression in a wide range of tumours. Analysis of survival data and DNA methylation for BRD9 indicated distinct conclusions for multiple tumours. mRNA splicing and molecular binding were involved in the functional mechanism of BRD9. BRD9 may affect cancer progression through different phosphorylation sites or N6 -methyladenosine site modifications. BRD9 could potentially serve as a novel biomarker for diagnosing different cancer types, especially could accurately forecast the prognosis of melanoma patients receiving anti-programmed cell death 1 immunotherapy. BRD9 has the potential to serve as a therapeutic target, when pairing with etoposide in patients with melanoma. The BRD9/SMARCD1 axis exhibited promising discriminative performance in forecasting the prognosis of patients afflicted with liver hepatocellular carcinoma (LIHC) and mesothelioma. Additionally, this axis appears to potentially influence the immune response in LIHC by regulating the programmed death-ligand 1 immune checkpoint. For experimental validation, high expression levels of BRD9 were observed in tumour tissue samples from both lung and colon cancer patients. Knocking down BRD9 led to the inhibition of lung and colon cancer development, likely via the Wnt/β-catenin signalling pathway.

CONCLUSIONS

These pan-cancer study revealed the diagnostic and prognostic potential, along with the biological mechanism of BRD9 as a novel therapeutic target in human tumours.

SKP2 Knockout in Rb1/p53-Deficient Mouse Models of Osteosarcoma Induces Immune Infiltration and Drives a Transcriptional Program with a Favorable Prognosis

Osteosarcoma is an aggressive bone malignancy with a poor prognosis. One putative proto-oncogene in osteosarcoma is SKP2, encoding a substrate recognition factor of the SCF E3 ubiquitin ligase. We previously demonstrated that Skp2 knockout in murine osteosarcoma improved survival and delayed tumorigenesis. Here, we performed RNA sequencing (RNA-seq) on tumors from a transgenic osteosarcoma mouse model with conditional Trp53 and Rb1 knockouts in the osteoblast lineage ("DKO": Osx1-Cre;Rb1lox/lox;p53lox/lox) and a triple-knockout model with additional Skp2 germline knockout ("TKO": Osx1-Cre;Rb1lox/lox;p53lox/lox;Skp2-/-), followed by qPCR and immunohistochemistry validation. To investigate the clinical implications of our results, we analyzed a human osteosarcoma patient cohort ("NCI-TARGET OS") with RNA-seq and clinical data. We found large differences in gene expression after SKP2 knockout. Surprisingly, we observed increased expression of genes related to immune microenvironment infiltration in TKO tumors, especially the signature genes for macrophages and to a lesser extent, T cells, B cells, and vascular cells. We also uncovered a set of relevant transcription factors that may mediate these changes. In osteosarcoma patient cohorts, high expression of genes upregulated in TKO was correlated with favorable overall survival, which was largely explained by the macrophage gene signatures. This relationship was further supported by our finding that SKP2 expression was negatively correlated with macrophage infiltration in the NCI-TARGET osteosarcoma and the TCGA Sarcoma cohorts. Overall, our findings indicate that SKP2 may mediate immune exclusion from the osteosarcoma tumor microenvironment, suggesting that SKP2 modulation in osteosarcoma may induce antitumor immune activation.

Immune Cell Infiltration Types as Biomarkers for the Recurrence Diagnosis and Prognosis of Bladder Cancer

This study aimed to investigate the role of infiltrating immune cell types in diagnosing and predicting bladder cancer recurrence. This study mainly applied some algorithms, including Estimate the Proportion of Immune and Cancer Cells (EPIC), support vector machine-recursive feature elimination (SVM-RFE), random forest out-of-bag (RF-OOB) and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. We found six immune infiltrating cell types significantly associated with recurrence prognosis and two independent clinical prognostic factors. Infiltrating immune cell types (IICTs) based on the prognostic immune risk score (pIRS) models may provide significant biomarkers for the diagnosis and prognostic prediction of bladder cancer recurrence.

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia

BACKGROUND

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis.

METHODS

The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool.

RESULTS

Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p.

CONCLUSIONS

The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Prediction and verification of the prognostic biomarker SLC2A2 and its association with immune infiltration in gastric cancer

Gastric cancer (GC) is the fifth most common cause of cancer-associated deaths; however, its treatment options are limited. Despite clinical improvements, chemotherapy resistance and metastasis are major challenges in improving the prognosis and quality of life of patients with GC. Therefore, effective prognostic biomarkers and targets associated with immunological interventions need to be identified. Solute carrier family 2 member 2 (SLC2A2) may serve a role in tumor development and invasion. The present study aimed to evaluate SLC2A2 as a prospective prognostic marker and chemotherapeutic target for GC. SLC2A2 expression in several types of cancer and GC was analyzed using online databases, and the effects of SLC2A2 expression on survival prognosis in GC were investigated. Clinicopathological parameters were examined to explore the association between SLC2A2 expression and overall survival (OS). Associations between SLC2A2 expression and immune infiltration, immune checkpoints and IC50 were estimated using quantification of the tumor immune contexture from human RNA-seq data, the Tumor Immune Estimation Resource 2.0 database and the Genomics of Drug Sensitivity in Cancer database. Differential SLC2A2 expression and the predictive value were validated using the Human Protein Atlas, Gene Expression Omnibus, immunohistochemistry and reverse transcription-quantitative PCR. SLC2A2 expression was downregulated in most types of tumor but upregulated in GC. Functional enrichment analysis revealed an association between SLC2A2 expression and lipid metabolism and the tumor immune microenvironment. According to Gene Ontology term functional enrichment analysis, SLC2A2-related differentially expressed genes were enriched predominantly in 'chylomicron assembly', 'plasma lipoprotein particle assembly', 'high-density lipoprotein particle', 'chylomicron', 'triglyceride-rich plasma lipoprotein particle', 'very-low-density lipoprotein particle'. 'intermembrane lipid transfer activity', 'lipoprotein particle receptor binding', 'cholesterol transporter activity' and 'intermembrane cholesterol transfer activity'. In addition, 'cholesterol metabolism', and 'fat digestion and absorption' were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Patients with GC with high SLC2A2 expression had higher levels of neutrophil and M2 macrophage infiltration and a significant inverse correlation was observed between SLC2A2 expression and MYC targets, tumor mutation burden, microsatellite instability and immune checkpoints. Furthermore, patients with high SLC2A2 expression had worse prognosis, including OS, disease-specific survival and progression-free interval. Multivariate regression analysis demonstrated that SLC2A2 could independently prognosticate GC and the nomogram model showed favorable performance for survival prediction. SLC2A2 may be a prospective prognostic marker for GC. The prediction model may improve the prognosis of patients with GC in clinical practice, and SLC2A2 may serve as a novel therapeutic target to provide immunotherapy plans for GC.

Role of prognostic gene DKK1 in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common squamous cell carcinomas of the head and neck. The morbidity and mortality rates of OSCC have increased in recent years. However, the pathogenesis of this disease remains unknown. The present study aimed to identify predictive biomarkers and therapeutic targets for OSCC. Bioinformatics screening of differentially expressed genes in OSCC was performed based on data from The Cancer Genome Atlas and Gene Expression Omnibus databases. Dickkopf Wnt signaling pathway inhibitor 1 (DKK1) was identified to be associated with survival, tumor immunity and DNA repair in OSCC. Furthermore, the effects of DKK1 were evaluated by the knockdown of DKK1 in two OSCC cell lines. The proliferation, clonogenicity, migration and invasion of the cells were assessed in vitro using Cell Counting Kit-8, colony formation, wound healing and Transwell assays, respectively, and were found to be inhibited by DKK1 knockdown. The present study suggests that DKK1 may be used in the prognosis of patients with OSCC and that targeting DKK1 is a potential strategy for OSCC therapy.

T-cell exhaustion prediction algorithm in tumor microenvironment for evaluating prognostic stratification and immunotherapy effect of esophageal cancer

Esophageal cancer (EC) is a common digestive malignancy that ranks sixth in cancer deaths, with a 5-year survival rate of 15%-25%. As a result, reliable prognostic biomarkers are required to accurately predict the prognosis of EC. T-cell exhaustion (TEX) is associated with poorer prognosis and immune infiltration in EC. In this study, nine risk genes were finally screened to constitute the prognostic model using least absolute shrinkage and selection operator analysis. Patients were divided into two groups based on the expression of the TEX-related genes: high-risk group and low-risk group. The expression of TEX-related genes differed significantly between the two groups. The findings revealed that the risk model developed was highly related to the clinical prognosis and amount of immune cell infiltration in EC patients. It was also significantly correlated with the therapeutic sensitivity of multiple chemotherapeutic agents in EC patients. Subsequently, we successfully constructed drug-resistant cell lines KYSE480/CDDP-R and KYSE180/CDDP-R to verify the correlation between PD-1 and drug resistance in EC. Then, we examined the mRNA and protein expression levels of PD-1 in parental and drug-resistant cells using qPCR and WB. It was found that the expression level of PD-1 was significantly increased in the plasma red of drug-resistant cells. Next, we knocked down PD-1 in drug-resistant cells and found that the resistance of EC cells to CDDP was significantly reduced. And the proportion of apoptotic cells in cells treated with 6 μM CDDP for 24 h was significantly in increase. The TEX-based risk model achieved good prediction results for prognosis prediction in EC patients. And it was also significantly associated with the level of immune cell infiltration and drug therapy sensitivity of EC patients. Additionally, the downregulation of PD-1 may be associated with increased drug sensitivity in EC and enhanced T-cell infiltration. The high-risk group had lower TIDE scores, indicating that the high-risk group benefits more after receiving immunotherapy. Thus, the TEX-based risk model can be used as a novel tumor prognostic biomarker.

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Corrigendum: Identification and validation of immune-related methylation clusters for predicting immune activity and prognosis in breast cancer

[This corrects the article DOI: 10.3389/fimmu.2021.704557.].

Identification of C4BPA as biomarker associated with immune infiltration and prognosis in breast cancer

Background

C4BPA is a gene that encodes the C4BP protein α chain and is involved in the complement system. C4BPA is regarded as a new biomarker for cancer, especially for non-small cell lung cancer and ovarian cancer. However, its role in breast cancer (BC) has not yet been determined.

Methods

In this research, we used a bioinformatics approach to assess the prognostic significance of C4BPA in BC. Utilizing a variety of databases and analysis tools, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), R, STRING, and the Kaplan-Meier plotter, we specifically assessed the connection between C4BPA and BC.

Results

C4BPA expression was markedly decreased in BC tissues compared to its expression in normal breast tissues (P<0.05). Additionally, a receiver operating characteristic (ROC) curve revealed that C4BPA has a significant capacity for prognostication and diagnostics. Additionally, C4BPA expression was linked to some immune infiltrating cells' functionality, according to gene set enrichment analysis (GSEA) and immune infiltration analysis. Low C4BPA expression was additionally related to poor progression-free interval (PFI) and overall survival (OS), according to the Kaplan-Meier method. We also found that C4BPA expression was independently connected to PFI and OS through Cox regression analysis. Finally, prognostic analysis of the various subgroups of breast invasive carcinoma (BRCA/BIC) in TCGA showed that patients with low C4BPA expression might have worse PFI and OS in patients with Luminal A compared to other BC subtypes.

Conclusions

In conclusion, these results revealed that C4BPA could potentially act as a diagnostic biomarker for BC patients indicating unfavorable prognoses and offers valuable knowledge for creating therapeutics and prognostic indicators.

Identification of gut microbes-related molecular subtypes and their biomarkers in colorectal cancer

The role of gut microbes (GM) and their metabolites in colorectal cancer (CRC) development has attracted increasing attention. Several studies have identified specific microorganisms that are closely associated with CRC occurrence and progression, as well as key genes associated with gut microorganisms. However, the extent to which gut microbes-related genes can serve as biomarkers for CRC progression or prognosis is still poorly understood. This study used a bioinformatics-based approach to synthetically analyze the large amount of available data stored in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through this analysis, this study identified two distinct CRC molecular subtypes associated with GM, as well as CRC markers related to GM. In addition, these new subtypes exhibit significantly different survival outcomes and are characterized by distinct immune landscapes and biological functions. Gut microbes-related biomarkers (GMRBs), IL7 and BCL10, were identified and found to have independent prognostic value and predictability for immunotherapeutic response in CRC patients. In addition, a systematic collection and review of prior research literature on GM and CRC provided additional evidence to support these findings. In conclusion, this paper provides new insights into the underlying pathological mechanisms by which GM promotes the development of CRC and suggests potentially viable solutions for individualized prevention, screening, and treatment of CRC.

Identification of immune-related gene signatures for chronic obstructive pulmonary disease with metabolic syndrome: evidence from integrated bulk and single-cell RNA sequencing data

Chronic obstructive pulmonary disease (COPD) is closely related to innate and adaptive inflammatory immune responses. It is increasingly becoming evident that metabolic syndrome (MetS) affects a significant portion of COPD patients. Through this investigation, we identify shared immune-related candidate biological markers. The Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to reveal the co-expression modules linked to COPD and MetS. The commonly expressed genes in the COPD and MetS were utilized to conduct an enrichment analysis. We adopted machine-learning to screen and validate hub genes. We also assessed the relationship between hub genes and immune cell infiltration in COPD and MetS, respectively. Moreover, associations across hub genes and metabolic pathways were also explored. Finally, we chose a single-cell RNA sequencing (scRNA-seq) dataset to investigate the hub genes and shared mechanisms at the level of the cells. We also applied cell trajectory analysis and cell-cell communication analysis to focus on the vital immune cell we were interested in. As a result, we selected and validated 13 shared hub genes for COPD and MetS. The enrichment analysis and immune infiltration analysis illustrated strong associations between hub genes and immunology. Additionally, we applied metabolic pathway enrichment analysis, indicating the significant role of reactive oxygen species (ROS) in COPD with MetS. Through scRNA-seq analysis, we found that ROS might accumulate the most in the alveolar macrophages. In conclusion, the 13 hub genes related to the immune response and metabolism may serve as diagnostic biomarkers and treatment targets of COPD with MetS.

UBE2C is a diagnosis and therapeutic biomarker involved in immune infiltration of cancers including lung adenocarcinoma

The mechanism of action of UBE2C in lung adenocarcinoma (LUAD) and its significance in cancer diagnosis, targeted therapy and immunotherapy, even in pan-cancer, are still unclear. Several large public databases and online analysis tools were used for big data mining analysis. RNA interference technology, CCK8 assay, flow cytometry and apoptosis detection, and western blot were used for in vitro experiments. UBE2C was found to be overexpressed in various of tumors, including LUAD, and its expression level was found to be significantly related to gender, weight, tumor stage, grade and prognosis in LUAD. Downregulation of UBE2C expression induced proliferation suppression and G2/M phase arrest and cell apoptosis in LUAD cells and suppressed LUAD cell growth through inhibiting the Akt-mTOR signaling pathway. Expression level of UBE2C was negatively correlated with B cells and CD4+ T cell, and also with immune checkpoint genes in LUAD. Pan-cancer assay shown that UBE2C was significantly overexpressed in 28 cancers and was correlated with Ki-67 index in many cancers. Overexpression of UBE2C in BRCA, LUAD and MESO indicated worse Overall Survival (OS). UBE2C expression levels were positively associated with immunocyte infiltration, immune regulatory genes, immune checkpoints, TMB, MSI and MMRs in some cancers. Additionally, Single-cell functional analysis showed that UBE2C was positively correlated with cell cycle, proliferation, DNA damage, EMT, DNA repair, invasion and differentiation in some cancers. These findings suggested that UBE2C could be regarded as a latent diagnosis and prognostic biomarker and a new target for immunological therapy of cancers including LUAD.

Association of cuproptosis-related signature with the prognosis of patients with head and neck squamous cell carcinoma

Patients with head and neck squamous cell carcinoma (HNSCC) have a poor prognosis because of their high recurrence and metastasis rates. Cuproptosis is a novel type of copper-dependent cell death that differs from apoptosis, necroptosis, and cytosolic scorch death. We designed and validated an individualized cuproptosis-related gene (CRG) signature for risk evaluation and prognostic prediction in HNSCC patients. Ninety differentially expressed CRGs were found in HNSCC. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the functional involvement of CRGs in the Cancer Genome Atlas (TCGA) HNSCC cohort. A CRG signature was created using 10 genes after univariate and multivariate analysis. Kaplan Meier (KM) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group. Multivariate regression analysis identified risk scores based on prognostic characteristics as independent prognostic indicators of HNSCC. Moreover, risk models are related to tumor mutational burden (TMB), tumor-infiltrating immune cells (TICs), immune checkpoints, clinical characteristics, and antitumor drug susceptibility. Furthermore, we found that CuCl2 treatment promoted cuproptosis in HNSCC cells, and that the expression levels of cuproptosis-related genes were altered by different doses of CuCl2. In summary, understanding the detailed molecular mechanisms of cuproptosis and its impact on overall survival (OS), and identifying potential therapeutic targets for HNSCC will provide potential insights for treatment.Communicated by Ramaswamy H. Sarma.

Pan-cancer and single-cell analysis reveal THRAP3 as a prognostic and immunological biomarker for multiple cancer types

Background: Thyroid hormone receptor-associated protein 3 (THRAP3) is of great significance in DNA damage response, pre-mRNA processing, and nuclear export. However, the biological activities of THRAP3 in pan-cancer remain unexplored. We aimed to conduct a comprehensive analysis of THRAP3 and validate its expression levels in lung cancer. Methods: A pan-cancer analysis was conducted to study the correlation of THRAP3 expression with clinical outcome and the tumor microenvironment based on the available bioinformatics databases. The protein levels of THRAP3 were explored in lung cancer by immunohistochemistry (IHC) analysis. Single-cell sequencing (ScRNA-seq) analysis was employed to investigate the proportions of each cell type in lung adenocarcinoma (LUAD) and adjacent normal tissues, along with the expression levels of THRAP3 within each cell type. Results: THRAP3 is upregulated in multiple cancer types but exhibits low expression in lung squamous cell carcinoma (LUSC). immunohistochemistry results showed that THRAP3 is a lowly expression in LUAD and LUSC. THRAP3 elevation had a poor prognosis in kidney renal clear cell carcinoma and a prolonged survival time in kidney chromophobe, brain lower-grade glioma and skin cutaneous melanoma, as indicated by the KM curve. Single-cell analysis confirmed that the proportions of T/B cells, macrophages, and fibroblasts were significantly elevated in LUAD tissues, and THRAP3 is specifically overexpressed in mast cells. Conclusion: Our findings uncover that THRAP3 is a promising prognostic biomarker and immunotherapeutic target in multiple cancers, but in LUAD and LUSC, it may be a protective gene.

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Background

Calcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.

Methods

Three microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.

Results

We identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.

Conclusion

Three pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A, DNMT3B) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.

Screening and experimental validation of diagnostic gene in ulcerative colitis with anti-TNF-α therapy

In clinical practice, the diagnosis of ulcerative colitis (UC) mainly relies on a comprehensive analysis of a series of signs and symptoms of patients. The current biomarkers for diagnosis of UC and prognostic prediction of anti-TNF-α therapy are inaccurate. The present study aimed to perform an integrative analysis of gene expression profiles in patients with UC. A total of seven datasets from the GEO database that met our strict inclusion criteria were included. After identifying differentially expressed genes (DEGs) between UC patients and healthy individuals, the diagnostic and prognostic utility of the DEGs were then analyzed via least absolute shrinkage and selection operator and support-vector machine recursive feature elimination. Subgroup analyses of the treated and untreated groups, as well as the treatment-response group and non-response group, were also performed. Furthermore, the relationship between the expressions of UC-related genes and infiltration of immune cells in the course of treatment was also investigated. Immunohistochemical (IHC) assay was used to verify the gene expression in inflamed UC tissues. When considering all the applied methods, DUOX2, PI3, S100P, MMP7, and S100A8 had priority to be defined as the characteristic genes among DEGs. The area under curve (AUC) of the five genes, which were all consistently over-expressed, based on an external validation dataset, were all above 0.94 for UC diagnosis. Four of the five genes (DUOX2, PI3, MMP7, and S100A8) were down-regulated between treatment-responsive and nonresponsive patients. A significant difference was also observed concerning the infiltration of immune cells, including macrophage and neutrophil, between the two groups (treatment responsive and nonresponsive). The changes in the expression of DUOX2 and MMP7 based on the IHC assay were highly consistent with the results obtained in the current study. This confirmed the mild to moderate diagnostic and predictive value of DUOX2 and MMP7 in patients with UC. The conducted analyses showed that the expression profile of the five identified biomarkers accurately detects UC, whereas four of the five genes evidently predicted the response to anti-TNF-α therapy.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Bioinformatics and Network Pharmacology Explore the Role of Immune Cells in the Occurrence of Anti-Vascular Endothelial Growth Factor (VEGF) Resistance in Patients with Neovascular Age-Related Macular Degeneration(nAMD) and the Application of Complementary Medicine Treatment

PURPOSE

This study explores the immune cells' role in anti-VEGF resistance in nAMD patients, and the potential of Zi-Yin-Jiang-Huo-Tang (ZYJHT), a Traditional Chinese Medicine formula, as complementary therapy.

METHODS

Aqueous humor proteomics data from 10 nAMD patients with anti-VEGF resistance and 10 nAMD patients without anti-VEGF resistance were analyzed, investigating immune cells's role in anti-VEGF resistance and its underlying mechanism. Network pharmacology methods are employed to analyze the active ingredients in ZYJHT that contribute to therapeutic effects and their mechanisms. Real-time PCR (polymerase chain reaction) was used to detect changes in the expression of SOD1 (superoxide dismutase 1) after treatment with compounds targeting SOD1 in ARPE-19 cells.

RESULTS

nAMD patients with anti-VEGF resistance showed enhancement of biological processes linked to the positive regulation of immune function, along with decreased cellular resistance to oxidative stress. Infiltration of B cells memory, plasma cells, CD8+and γδ-T cells were higher in nAMD patients with anti-VEGF resistance. SOD1 was identified as a hub gene in the occurrence of anti-VEGF resistance and a core therapeutic target of ZYJHT, negatively correlated with B and T cell infiltration. Compounds diosgenin, naringenin, and liquiritin in ZYJHT can bind to SOD1 and upregulating SOD1 expression in ARPE-19 cells.

Exploring Immune-Related Gene Profiling and Infiltration of Immune Cells in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Cervical cancer is a widespread malignancy among women, leading to a substantial global health impact. Despite extensive research, our understanding of the basic molecules and pathogenic processes of cervical squamous cell carcinoma is still insufficient. This investigation aims to uncover immune-related genes linked to CESC and delineate their functions. Leveraging data from the GEO and ImmPort databases, a total of 22 immune-related genes were identified. Multiple tools, including DAVID, the human protein atlas, STRING, GeneMANIA, and TCGA, were employed to delve into the expression and roles of these immune genes in CESC, alongside their connections to the disease's pathological features. Through RT-PCR, the study confirmed notable disparities in CXCL8 and CXCL10 mRNA expression between CESC and normal cervical tissue. The TCGA dataset's immune-related information reinforced the association of CXCL8 and CXCL10 with immune infiltration in CESC. This research sheds light on the potential of CXCL8 and CXCL10 as promising therapeutic targets and essential prognostic factors for individuals diagnosed with CESC.

New Insights into Roles of IL-7R Gene as a Therapeutic Target Following Intracerebral Hemorrhage

Background

Spontaneous intracerebral hemorrhage (ICH) is a subtype of stroke leading to high rates of morbidity and mortality in adults. Recent studies showed that immune and inflammatory responses might play essential roles in secondary brain injury. The purpose of this article was to provide a reference for further therapeutic strategies for ICH patients.

Methods

GSE206971 and GSE216607 datasets from the gene expression omnibus (GEO) database were used to screen the highly immune-related differentally expressed genes (IRDEGs). We used the CIBERSORT algorithm to assess the level of immune signatures infiltration and got the possible function of IRDEGs which was analyzed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Protein-protein interaction (PPI) networks and six hub genes were identified in the Cytoscape plug-in. GSVA algorithm was performed to evaluate the potential pathways of six hub genes in ICH samples. The expression level of IL-7R chosen from six hub genes was further validated by Western blotting. The cell models of ICH were established for the research of IL-7/IL-7R signaling way.

Results

A total of six hub genes (ITGAX, ITGAM, CCR2, CD28, SELL, and IL-7R) were identified. IL-7R was highly expressed in the mice ICH group, as shown by immunoblotting. Next, we constructed ICH cell models in RAW264.7 cells and BV2 cells. After treatment with IL-7, iNOS expression (M1 marker) was greatly inhibited while Arg-1(M2 marker) was enhanced, and it might function via the JAK3/STAT5 signaling pathway.

Conclusion

The hypothesis is proposed that the IL-7/IL-7R signaling pathway might regulate the inflammatory process following ICH by regulating microglia polarization. Our study is limited and requires more in-depth experimental confirmation.

SLC7A8 overexpression inhibits the growth and metastasis of lung adenocarcinoma and is correlated with a dismal prognosis

BACKGROUND

Overexpression of solute carrier family 7 member 8 (SLC7A8) has been shown to relate to the survival time and tumor progression in cancer patients. However, the role of SLC7A8 in lung adenocarcinoma (LUAD) is still obscure.

METHOD

The relationships between SLC7A8 expression in LUAD tissues and clinical values as well as immune infiltration were explored through bioinformatics. The functions and pathways of SLC7A8 in LUAD were investigated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, Western blotting, and other methods.

RESULTS

We found that the expression of SLC7A8 was decreased significantly in LUAD tissues compared with normal tissues, which was related to the dismal survival time and disease progression. Moreover, it carried diagnostic value in LUAD and was a risk factor for dismal prognosis. Receiver operating characteristic curve analysis indicated that the expression level of SLC7A8 carried significant diagnostic value in LUAD. Overexpression of SLC7A8 inhibited the proliferation, invasion, and migration of LUAD cells, likely through a mechanism involving the cell cycle. SLC7A8 expression in LUAD was significantly correlated with the infiltration of immune cells, especially B cells, interstitial dendritic cells, mast cells, CD56 bright cells, natural killer cells, plasmacytoid dendritic cells, T follicular helper cells, T helper 2 and 17 cells, and immune factors.

CONCLUSION

The downregulation of SLC7A8 was related to a dismal prognosis and immune cell infiltration in LUAD. Increasing the expression of SLC7A8 inhibited the growth and migration of LUAD cells, thereby improving the prognosis of patients.

Prognostic biomarker DARS2 correlated with immune infiltrates in bladder tumor

Background

DARS2 is a pivotal member of the Aminoacyl-tRNA synthetases family that is critical for regulating protein translation. However, the biological role of DARS2 in bladder cancer remains elusive.

Methods

We analyzed the correlation between DARS2 expression and prognosis, tumor stage, and immune infiltration in bladder cancer using The Cancer Genome Atlas (TCGA) database. We validated findings in clinical samples from The First Affiliated Hospital of Nanchang University and explored the biological functions of DARS2 using cell and animal models.

Results

We found DARS2 to be upregulated in bladder cancer, associated with tumor progression and poor prognosis. Immune infiltration analysis suggested that DARS2 may facilitate immune evasion by modulating PD-L1. Cell and animal experiments validated that DARS2 knockdown and overexpress can inhibit or increase cancer cell proliferation, metastasis, tumorigenesis, immune escape, and PD-L1 levels.

Conclusions

Our study reveals DARS2 as a potential prognostic biomarker and immunotherapy target in BLCA.

Prediction significance of autophagy-related genes in survival probability and drug resistance in diffuse large B-cell lymphoma

BACKGROUND/AIMS

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.

METHODS

Utilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.

RESULTS

DLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8+ T cell, CD4+ memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.

CONCLUSIONS

This study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.

The Role of GNMT and MMP12 Expression in Determining TACE Efficacy: Validation at Transcription and Protein Levels

Purpose

Transarterial chemoembolization (TACE) represents a significant therapeutic modality for hepatocellular carcinoma (HCC). We aimed to develop a gene signature to accurately predict patient TACE response and explore the underlying mechanisms.

Methods

Three independent datasets were utilized, including GSE104580, GSE14520 and external validation from the Cancer Hospital Chinese Academy of Medical Sciences. GSE104580 was randomly partitioned into a training set and a validation set, whereas GSE14520 was categorized into a resection group and a TACE group. Logistic regression was used to develop a TACE effectiveness model. Immunohistochemistry is utilized to confirm the protein expression trends of the signature genes. Immune infiltration and functional enrichment analyses were conducted to investigate the potential underlying mechanisms.

Results

A 2-gene signature consisting of glycine N-methyltransferase (GNMT) and matrix metalloproteinase-12 (MMP12) was constructed, and based on this, all the patients were assigned TACE effectiveness scores and categorized into high effectiveness (HE) and low effectiveness (LE) groups. The HE group exhibited a better prognosis than the LE group in the various cohorts (p < 0.05). In the external validation set, immunohistochemistry confirmed the expression of the signature genes exhibiting an upregulated trend of GNMT in the HE group and MMP12 in the LE group, the LE group also exhibited a poorer prognosis [for overall survival (OS), HE group: 881 days vs LE group: 273 days (p < 0.05), and for progression-free survival (PFS), HE group: 458 days vs LE group: 136 days (p < 0.05)]. Multivariate analysis in all the datasets identified LE status as an independent risk factor for OS, disease-free survival (DFS) and PFS. The infiltration level of M0 macrophages and activated mast cells in the LE group was significantly higher than in the HE group. The hypoxia signaling pathway and glycolysis pathway were significantly enriched in the LE group.

Conclusion

The loss of GNMT and the overexpression of MMP12 may be critical factors influencing TACE efficacy.

Increased ACSL6 Expression Predicts a Favorable Prognosis in Triple-negative Breast Cancer

BACKGROUND

Long-chain acyl-coenzyme A synthases (ACSLs) are responsible for the catalysis of fatty acids into their corresponding fatty acyl-CoAs. The dysregulation of ACSLs has been increasingly recognized in cancer patients. However, the function of ACSL6 in triple-negative breast cancer (TNBC) is still completely unknown.

METHODS

In this study, immunohistochemistry was applied to detect ACSL6 protein expression using a TNBC tissue microarray. Additionally, the mRNA levels of ACSL6 in human normal tissues and pancancer tissues were analyzed using Genotype Tissue Expression (GTEx) datasets and The Cancer Genome Atlas (TCGA) database. The correlations between the levels of ACSL6 expression and clinical characteristics were analyzed. The survival analysis of ACSL6 in TNBC was carried out using the Kaplan‒Meier Plotter online tool. Associations of ACSL6 with immune infiltration analyses were conducted using the ESTIMATE, CIBERSORT, and TISIDB databases. The relationship between ACSL6 and sensitivity to drugs was analyzed from Genomics of Drug Sensitivity in Cancer (GDSC).

RESULTS

The results indicated a significant increase in ACSL6 expression in TNBC tissues compared to adjacent normal tissues. However, high ACSL6 expression was significantly associated with favorable survival outcomes in TNBC patients. Enrichment analysis revealed that coexpressed genes of ACSL6 were significantly enriched in various immunity processes. ACSL6 was positively correlated with the infiltration of memory CD4 T cells, while a negative correlation was found between ACSL6 and M2 macrophages and resting dendritic cells. Further analysis revealed that high levels of ACSL6 correlated with increased survival outcomes in cancer patients who received immunotherapy.

CONCLUSION

Altogether, the current findings highlight the potential value of ACSL6 as a diagnostic and prognostic marker in the treatment of TNBC.

Identification of CD8+ T cell-related biomarkers and immune infiltration characteristic of rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease, which do not respond well to current treatment partially. Therefore, further in-depth elucidation of the molecular mechanism and pathogenesis of RA is urgently needed for the diagnosis, personalized therapy and drug development. Herein, we collected 111 RA samples from Gene Expression Omnibus (GEO) database, and conducted differentially expressed genes and GESA analysis. Abnormal activation and imbalance of immune cells in RA were observed. WGCNA was utilized to explore the gene modules and CD8+ T cell-related genes (CRGs) were chosen for KEGG and GO analysis. Besides, to explore biomarkers of RA in depth, machine learning algorithms and bioinformatics analysis were used, and we identified GDF15, IGLC1, and IGHM as diagnostic markers of RA, which was confirmed by clinical samples. Next, ssGSEA algorithms were adopted to investigate the differences in immune infiltration of 23 immune cell subsets between RA and healthy control group. Finally, optimal classification analysis based on consensus clustering combined with ssGSEA algorithms were conducted. GDF15 was revealed that to be positively correlated with mast cells and type 2 T helper cells, but negatively correlated with most other immune cells. On the other hand, IGHM and IGLC1 were negatively correlated with CD56dim natural killer cells, while positively associated with other immune cells. Finally, RA samples in subtype A exhibited a higher immune infiltration status. This study could provide guidance for individualized treatment of RA patients and provide new targets for drug design.