Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Immune cell related signature predicts prognosis in esophageal squamous cell carcinoma based on single-cell and bulk-RNA sequencing

Background

It has been reported that tumor immune microenvironment performs a vital role in tumor progress. However, acting mechanism of immune cell related genes (IRGs) in esophageal squamous cell carcinoma (ESCC) is uncertain.

Methods

TCGA-ESCC, GSE23400, GSE26886, GSE75241, and GSE196756 datasets were gained via public databases. First, differentially expressed genes (DEGs) between ESCC and control samples from GSE23400, GSE26886, and GSE75241 were screened out by differential expression analysis, and overlapping DEGs were identified. Single-cell transcriptome data of GSE196756 were applied to explore immune cells that might be involved in regulation of ESCC. Then, weighted gene co-expression network analysis was applied to screen IRGs. Next, differentially expressed IRGs (DE-IRGs) were identified by overlapping IRGs and DEGs, and were incorporated into univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox to acquire prognosis-related genes, and ESCC samples were grouped into high-/low-risk groups on the basis of median risk score. Finally, the role of prognosis model in immunotherapy was analyzed.

Results

Totally 248 DEGs were yielded by overlapping 3,915 DEGs in GSE26886, 459 DEGs in GSE23400, and 1,641 DEGs in GSE75241. Single-cell analysis found that B cells, dendritic cells, monocytes, neutrophils, natural killer cells, and T cells were involved in ESCC development. Besides, MEred, MEblack, MEpink, MEblue and MEbrown modules were considered as key modules because of their highest correlations with immune cell subtypes. A total of 154 DE-IRGs were yielded by taking intersection of DEGs and genes in key modules. Moreover, CTSC, ALOX12, and RMND5B were identified as prognosis-related genes in ESCC. Obviously, Exclusion and TIDE scores were notably lower in high-risk group than in the other one, indicating that high-risk group was more responsive to immunotherapy.

Conclusions

Through bioinformatic analysis, we identified a prognosis model consisting of IRGs (CTSC, ALOX12, and RMND5B) in ESCC, providing new ideas for studies related to treatment and prognosis of ESCC.

Risk assessment model based on nucleotide metabolism-related genes highlights SLC27A2 as a potential therapeutic target in breast cancer

PURPOSE

Breast cancer (BC) is the most prevalent malignant tumor worldwide among women, with the highest incidence rate. The mechanisms underlying nucleotide metabolism on biological functions in BC remain incompletely elucidated. MATERIALS AND METHODS: We harnessed differentially expressed nucleotide metabolism-related genes from The Cancer Genome Atlas-BRCA, constructing a prognostic risk model through univariate Cox regression and LASSO regression analyses. A validation set and the GSE7390 dataset were used to validate the risk model. Clinical relevance, survival and prognosis, immune infiltration, functional enrichment, and drug sensitivity analyses were conducted.

RESULTS

Our findings identified four signature genes (DCTPP1, IFNG, SLC27A2, and MYH3) as nucleotide metabolism-related prognostic genes. Subsequently, patients were stratified into high- and low-risk groups, revealing the risk model's independence as a prognostic factor. Nomogram calibration underscored superior prediction accuracy. Gene Set Variation Analysis (GSVA) uncovered activated pathways in low-risk cohorts and mobilized pathways in high-risk cohorts. Distinctions in immune cells were noted between risk cohorts. Subsequent experiments validated that reducing SLC27A2 expression in BC cell lines or using the SLC27A2 inhibitor, Lipofermata, effectively inhibited tumor growth.

CONCLUSIONS

We pinpointed four nucleotide metabolism-related prognostic genes, demonstrating promising accuracy as a risk prediction tool for patients with BC. SLC27A2 appears to be a potential therapeutic target for BC among these genes.

Endoplasmic reticulum stress-related gene expression causes the progression of dilated cardiomyopathy by inducing apoptosis

Background: Previous studies have shown that endoplasmic reticulum stress (ERS) -induced apoptosis is involved in the pathogenesis of dilated cardiomyopathy (DCM). However, the molecular mechanism involved has not been fully characterized. Results: In total, eight genes were obtained at the intersection of 1,068 differentially expressed genes (DEGs) from differential expression analysis between DCM and healthy control (HC) samples, 320 module genes from weighted gene co-expression network analysis (WGCNA), and 2,009 endoplasmic reticulum stress (ERGs). These eight genes were found to be associated with immunity and angiogenesis. Four of these genes were related to apoptosis. The upregulation of MX1 may represent an autocompensatory response to DCM caused by a virus that inhibits viral RNA and DNA synthesis, while acting as an autoimmune antigen and inducing apoptosis. The upregulation of TESPA1 would lead to the dysfunction of calcium release from the endoplasmic reticulum. The upregulation of THBS4 would affect macrophage differentiation and apoptosis, consistent with inflammation and fibrosis of cardiomyocytes in DCM. The downregulation of MYH6 would lead to dysfunction of the sarcomere, further explaining cardiac remodeling in DCM. Moreover, the expression of genes affecting the immune micro-environment was significantly altered, including TGF-β family member. Analysis of the co-expression and competitive endogenous RNA (ceRNA) network identified XIST, which competitively binds seven target microRNAs (miRNAs) and regulates MX1 and THBS4 expression. Finally, bisphenol A and valproic acid were found to target MX1, MYH6, and THBS4. Conclusion: We have identified four ERS-related genes (MX1, MYH6, TESPA1, and THBS4) that are dysregulated in DCM and related to apoptosis. This finding should help deepen understanding of the role of endoplasmic reticulum stress-induced apoptosis in the development of DCM.

Cyclin-dependent kinase inhibitors enhance programmed cell death protein 1 immune checkpoint blockade efficacy in triple-negative breast cancer by affecting the immune microenvironment

BACKGROUND

Clinical studies on programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors for treating triple-negative breast cancer (TNBC) have shown unsatisfactory efficacy due to low tumor-infiltrating lymphocyte (TIL) levels. Inhibitors targeting cyclin-dependent kinase (CDK) proteins can affect the immune microenvironment, increase TIL levels, and promote antitumor immunity, thus providing a new direction for TNBC treatment strategies.

METHODS

The authors tested three CDK inhibitors on the TNBC cell lines MDA-MB-231 and 4T1 and validated their antitumor effects and impact on the immune microenvironment using multiple detection methods. They verified the efficacy and immune-related mechanisms of different combination therapy experiments in a 4T1 cell-transplanted BALB/c mouse model.

RESULTS

Treatment with CDK inhibitors for 72 hours inhibited cell proliferation, clone formation, migration, and cell-cycle arrest and induced apoptosis in human breast cancer MDA-MB-231 cells and mouse breast cancer 4T1 cells. CDK inhibitors suppressed DNA methylation by downregulating DNMT1, DNMT3a, and DNMT3b expression. These three inhibitors promoted the secretion of various chemokines, enhanced tumor cell antigen presentation, and increased PD-L1 expression. CDK inhibitors improved the efficacy of immunotherapy in animal models and increased TIL levels.

CONCLUSIONS

Combination therapy with CDK and PD-L1 immune checkpoint inhibitors affects the immune microenvironment, promotes antitumor immunity, and improves the efficacy of immunotherapy for TNBC.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

Comprehensive analyses and experimental verification of NETs and an EMT gene signature for prognostic prediction, immunotherapy, and chemotherapy in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy with high mortality and poor prognosis. Neutrophil extracellular traps (NETs) and the epithelial-mesenchymal transition (EMT) significantly influence on the progression of various cancers. However, the underlying relevance of NETs- and EMT-associated genes on the outcomes of patients with PAAD remains to be elucidated. Transcriptome RNA sequencing data, together with clinical information and single-cell sequencing data of PAAD were collected from public databases. In the TCGA-PAAD cohort, ssGSEA was used to calculate NET and EMT scores. WGCNA was used to determine the key gene modules. A risk model with eight NET- and EMT-related genes (NERGs) was established using LASSO and multivariate Cox regression analysis. Patients in the reduced risk (RR) group showed better prognostic values compared with those in the elevated risk (ER) group. The prognostic model exhibited reliable and robust prediction when validated using an external database. The distributions of risk genes were explored in a single-cell sequencing data set. Immune infiltration, immune cycle, and immune checkpoints were compared between the RR and ER groups. Moreover, potential chemotherapeutic drugs were examined. DCBLD2 was identified as a key gene in PAAD cell lines by qRT-PCR, and was highly expressed in PAAD tissues. GSEA demonstrated that DCBLD2 induced the EMT. Transwell assays and western blotting showed that cell invasion and EMT induction were significantly reduced after DCBLD2 knockdown. Collectively, we constructed a prognosis model based on a NET and EMT gene signature, providing a valuable perspective for the prognostic evaluation and management of PAAD patient.

Subtype recognition and identification of a prognosis model characterized by antibody-dependent cell phagocytosis-related genes in breast cancer

BACKGROUND

Breast cancer (BC) is a heterogeneous tumor with a variety of etiology and clinical features. Antibody-dependent cell phagocytosis (ADCP) is the last step of immune checkpoint inhibition (ICI), and macrophages detect and recognize tumor cells, then destroy and engulf tumor cells. Despite the large number, negative regulators that inhibit phagocytic activity are still a key obstacle to the full efficacy of ICI.

PATIENTS AND METHODS

An ADCP-related risk score prognostic model for risk stratification as well as prognosis prediction was established in the Cancer Genome Atlas (TCGA) cohort. The predictive value of ADCP risk score in prognosis and immunotherapy was also further validated in the TCGA along with International Cancer Genome Consortium cohorts. To promote the clinical application of the risk score, a nomogram was established, with its effectiveness verified by different methods.

RESULTS

In this study, the genes collected from previous studies were defined as ADCP-related genes. In BC patients, two ADCP-related subtypes were identified. The immune characteristics and prognostic stratification were significant different between them.

CONCLUSIONS

We identified two subtypes associated with ADCP gene expression in breast cancer. They have significant differences in immune cells, molecular functions, HLA family genes, immune scores, stromal scores, and inflammatory gene expression, which have important guiding significance for the selection of clinical treatment methods. At the same time, we constructed a risk model based on ADCP, and the risk score can be used as a good indicator of prognosis, providing potential therapeutic advantages for chemotherapy and immunotherapy, thus helping the clinical decision-making of BC patients.

Elucidation of the mechanism of action of ailanthone in the treatment of colorectal cancer: integration of network pharmacology, bioinformatics analysis and experimental validation

Background: Ailanthone, a small compound derived from the bark of Ailanthus altissima (Mill.) Swingle, has several anti-tumour properties. However, the activity and mechanism of ailanthone in colorectal cancer (CRC) remain to be investigated. This study aims to comprehensively investigate the mechanism of ailanthone in the treatment of CRC by employing a combination of network pharmacology, bioinformatics analysis, and molecular biological technique. Methods: The druggability of ailanthone was examined, and its targets were identified using relevant databases. The RNA sequencing data of individuals with CRC obtained from the Cancer Genome Atlas (TCGA) database were analyzed. Utilizing the R programming language, an in-depth investigation of differentially expressed genes was carried out, and the potential target of ailanthone for anti-CRC was found. Through the integration of protein-protein interaction (PPI) network analysis, GO and KEGG enrichment studies to search for the key pathway of the action of Ailanthone. Then, by employing molecular docking verification, flow cytometry, Transwell assays, and Immunofluorescence to corroborate these discoveries. Results: Data regarding pharmacokinetic parameters and 137 target genes for ailanthone were obtained. Leveraging The Cancer Genome Atlas database, information regarding 2,551 differentially expressed genes was extracted. Subsequent analyses, encompassing protein-protein interaction network analysis, survival analysis, functional enrichment analysis, and molecular docking verification, revealed the PI3K/AKT signaling pathway as pivotal mediators of ailanthone against CRC. Additionally, the in vitro experiments indicated that ailanthone substantially affects the cell cycle, induces apoptosis in CRC cells (HCT116 and SW620 cells), and impedes the migration and invasion capabilities of these cells. Immunofluorescence staining showed that ailanthone significantly inhibited the phosphorylation of AKT protein and suppressed the activation of the PI3K/AKT signaling pathway, thereby inhibiting the proliferation and metastasis of CRC cells. Conclusion: Therefore, our findings indicate that Ailanthone exerts anti-CRC effects primarily by inhibiting the activation of the PI3K/AKT pathway. Additionally, we propose that Ailanthone holds potential as a therapeutic agent for the treatment of human CRC.

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

BACKGROUND

Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis.

METHOD

The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model's predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA.

RESULT

We obtained 5 key RiskScore model genes for patient's risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction.

CONCLUSIONS

We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Identification and study of cuproptosis-related genes in prognostic model of multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a highly heterogeneous disease. Cuproptosis is a novel mode of death that is closely associated with several diseases, such as hepatocellular carcinoma. However, its role in MM is unknown.

METHODS

MM transcriptomic and clinical data were obtained from UCSC Xena and gene expression omnibus (GEO) databases. Following MM samples were divided into different subtypes based on the cuproptosis genes, the differentially expressed genes (DEGs) among different subtypes, namely, candidate cuproptosis related genes were analyzed by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression to construct a cuproptosis-related risk model. After the independent prognostic analysis was performed, a nomogram was constructed. Finally, Functional enrichment analysis and immune infiltration analysis were performed in the high- and low-risk groups, potential therapeutic agents were then predicted.

RESULTS

The 784 MM samples in UCSC Xena cohorts were divided into three different subtypes, and 4 out of 346 candidate cuproptosis related genes, namely CDKN2A, BCL3, KCNA3 and TTC14 were used to construct a risk model. Risk score was considered a reliable independent prognostic factor for MM patients. It was investigated that the pathway of cell cycle was significantly enriched in the high-risk group. In addition, immune score, ESTIMATE score and cytolytic activity were significantly different between different risk groups, as well as 13 immune cells such as memory B cells. Nine drugs were predicted in our study.

CONCLUSION

A cuproptosis-related prognostic model was constructed, which may have a potential guiding role in the treatment of MM.

Identification of three lncRNA-related prognostic signatures in gastric cancer by integrated multi-omics analysis

Aims: The systematic identification of molecular features correlated with the clinical status of gastric cancer (GC) in patients is significant, although such investigation remains insufficient. Methods: GC subtyping based on RNA sequencing, copy number variation and DNA methylation data were derived from The Cancer Genome Atlas program. Prognostics lncRNA biomarkers for GC were identified by univariate Cox, LASSO and SVM-RFE analysis. Results: Three molecular subtypes with significant survival discrepancies, and their specific DEmRNAs and DElncRNAs were identified. Three reliable prognostic-associated lncRNA, including LINC00670, LINC00452 and LINC00160, were selected for GC. Conclusion: Our findings expanded the understanding on the regulatory network of lncRNAs in GC, providing potential targets for prognosis and treatment of GC patients.

Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma

Background

Dysregulation of lipid metabolism is common in cancer. However, the molecular mechanism underlying lipid metabolism in esophageal squamous cell carcinoma (ESCC) and its effect on patient prognosis are not well understood. The objective of our study was to construct a lipid metabolism-related prognostic model to improve prognosis prediction in ESCC.

Methods

We downloaded the mRNA expression profiles and corresponding survival data of patients with ESCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We performed differential expression analysis to identify differentially expressed lipid metabolism-related genes (DELMGs). We used Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to establish a risk model in the GEO cohort and used data of patients with ESCC from the TCGA cohort for validation. We also explored the relationship between the risk model and the immune microenvironment via infiltrated immune cells and immune checkpoints.

Results

The result showed that 132 unique DELMGs distinguished patients with ESCC from the controls. We identified four genes (ACAA1, ACOT11, B4GALNT1, and DDHD1) as prognostic gene expression signatures to construct a risk model. Patients were classified into high- and low-risk groups as per the signature-based risk score. We used the receiver operating characteristic (ROC) curve and the Kaplan-Meier (KM) survival analysis to validate the predictive performance of the 4-gene signature in both the training and validation sets. Infiltrated immune cells and immune checkpoints indicated a difference in the immune status between the two risk groups.

Conclusion

The results of our study indicated that a prognostic model based on the 4-gene signature related to lipid metabolism was useful for the prediction of prognosis in patients with ESCC.

HLA-DPA1 overexpression inhibits cancer progression, reduces resistance to cisplatin, and correlates with increased immune infiltration in lung adenocarcinoma

PURPOSE

Human Leukocyte Antigen-DP alpha 1 (HLA-DPA1) is a critical gene in antigen-presenting cells and plays a significant role in immune regulation. The objective of this study was to comprehensively analyze the roles of HLA-DPA1 and its association with lung adenocarcinoma (LUAD).

METHODS

We utilized bioinformatics and conducted a meta-analysis to examine the roles of HLA-DPA1 expression on the progression and immunity of LUAD. We also performed CCK-8, wound healing, and Transwell assays to validate the functions of HLA-DPA1 in LUAD.

RESULTS

HLA-DPA1 expression is downregulated in LUAD tissues and is associated with gender, race, age, smoking history, clinical stage, histological type, lymph node metastasis, and prognosis of patients with LUAD. HLA-DPA1 is involved in immune responses, leukocyte cell-cell adhesion, and antigen processing and presentation. Overexpression of HLA-DPA1 inhibits cancer cell proliferation, migration, and invasion while promoting cell sensitivity to cisplatin in A549 and A549/DDP cells. Additionally, overexpression of HLA-DPA1 correlates with tumor purity, stromal, immune, and ESTIMATE scores, the abundance of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, dendritic cells, and neutrophils), and immune cell markers (programmed cell death 1, cytotoxic T-lymphocyte-associated protein 4, and cluster of differentiation 8A).

CONCLUSIONS

Decreased HLA-DPA1 expression is associated with poor prognosis and immune infiltration in LUAD while HLA-DPA1 overexpression inhibits cancer cell proliferation and progression. Therefore, HLA-DPA1 shows potential as a prognostic biomarker and a therapeutic target for LUAD.

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer

Introduction

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

Methods

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

Results

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably CTLA4, LAG3, PDCD1, and CD274, according to an analysis of immune processes. The core gene C5aR1 in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of C5aR1 had lower 10-year overall survival (OS) rates.

Conclusion

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Role of NF-κB pathway in kidney renal clear cell carcinoma and its potential therapeutic implications

Kidney renal clear cell carcinoma (KIRC), a common malignant tumor of the urinary system, is the most aggressive renal tumor subtype. Since the discovery of nuclear factor kappa B (NF-κB) in 1986, many studies have demonstrated abnormal NF-κB signaling is associated with the development of various cancers, including kidney renal clear cell carcinoma. In this study, the relationship between NF-κB and kidney renal clear cell carcinoma was confirmed using bioinformatics analysis. First, we explored the differential expression of copy number variation (CNV), single nucleotide variant (SNV), and messenger RNA (mRNA) in NF-κB-related genes in different types of cancer, as well as the impact on cancer prognosis and sensitivity to common chemotherapy drugs. Then, we divided the mRNA expression levels of NF-κB-related genes in KIRC patients into three groups through GSVA cluster analysis and explored the correlation between the NF-κB pathway and clinical data of KIRC patients, classical cancer-related genes, common anticancer drug responsiveness, and immune cell infiltration. Finally, 11 tumor-related genes were screened using least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. In addition, we used the UALCAN and HPA databases to verify the protein levels of three key NF-κB-related genes (CHUK, IKGGB, and IKBKG) in KIRC. In conclusion, our study established a prognostic survival model based on NF-κB-related genes, which can be used to predict the prognosis of patients with KIRC.

[Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes]

BACKGROUND

Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration.

METHODS

The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups.

RESULTS

A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy.

CONCLUSIONS

The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.

Cuproptosis-related lncRNAs are correlated with tumour metabolism and immune microenvironment and predict prognosis in pancreatic cancer patients

Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis-related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low-risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high-risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high-risk group were more immunosuppressive than that in the low-risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment.

Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer

Purpose: CRC is a malignant tumor seriously threatening human health. Quercetin and kaempferol are representative components of traditional Chinese medicine (TCM). Previous studies have shown that both quercetin and kaempferol have antitumor pharmacological effects, nevertheless, the underlying mechanism of action remains unclear. To explore the synergy and mechanism of quercetin and kaempferol in colorectal cancer. Methods: In this study, network pharmacology, and bioinformatics are used to obtain the intersection of drug targets and disease genes. Training gene sets were acquired from the TCGA database, acquired prognostic-related genes by univariate Cox, multivariate Cox, and Lasso-Cox regression models, and validated in the GEO dataset. We also made predictions of the immune function of the samples and used molecular docking to map a model for binding two components to prognostic genes. Results: Through Lasso-Cox regression analysis, we obtained three models of drug target genes. This model predicts the combined role of quercetin and kaempferol in the treatment and prognosis of CRC. Prognostic genes are correlated with immune checkpoints and immune infiltration and play an adjuvant role in the immunotherapy of CRC. Conclusion: Core genes are regulated by quercetin and kaempferol to improve the patient's immune system and thus assist in the treatment of CRC.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Identification of fatty acid-related subtypes, the establishment of a prognostic signature, and immune infiltration characteristics in lung adenocarcinoma

Abnormal fatty acid (FA) metabolism can change the inflammatory microenvironment and promote tumor progression and metastasis, however, the potential association between FA-related genes (FARGs) and lung adenocarcinoma (LUAD) is still unclear. In this study, we described the genetic and transcriptomic changes of FARGs in LUAD patients and identified two different FA subtypes, which were significantly correlated with overall survival and tumor microenvironment infiltrating cells in LUAD patients. In addition, the FA score was also constructed through the LASSO Cox to evaluate the FA dysfunction of each patient. Multivariate Cox analysis proved that the FA score was an independent predictor and created the FA score integrated nomogram, which offered a quantitative tool for clinical practice. The performance of the FA score has been substantiated in numerous datasets for its commendable accuracy in estimating overall survival in LUAD patients. The groups with high and low FA scores exhibited different mutation spectrums, copy number variations, enrichment pathways, and immune status. Noteworthy differences between the two groups in terms of immunophenoscore and Tumor Immune Dysfunction and Exclusion were observed, suggesting that the group with a low FA score was more responsive to immunotherapy, and similar results were also confirmed in the immunotherapy cohort. In addition, seven potential chemotherapeutic drugs related to FA score targeting were predicted. Ultimately, we ascertained that the attenuation of KRT6A expression impeded the proliferation, migration, and invasion of LUAD cell lines. In summary, this research offers novel biomarkers to facilitate prognostic forecasting and clinical supervision for individuals afflicted with LUAD.

Clinical significance of nonerythrocytic spectrin Beta 1 (SPTBN1) in human kidney renal clear cell carcinoma and uveal melanoma: a study based on Pan-Cancer Analysis

BACKGROUND

Nonerythrocytic spectrin beta 1 (SPTBN1) is an important cytoskeletal protein that involves in normal cell growth and development via regulating TGFβ/Smad signaling pathway, and is aberrantly expressed in various cancer types. But, the exact role of SPTBN1 in pan-cancer is still unclear. This report aimed to display expression patterns and prognostic landscapes of SPTBN1 in human cancers, and further assess its prognostic/therapeutic value and immunological role in kidney renal carcinoma (KIRC) and uveal melanoma (UVM).

METHODS

We firstly analyzed expression patterns and prognostic landscapes of SPTBN1 in human cancers using various databases and web-based tools. The relationships between SPTBN1 expression and survival/tumor immunity in KIRC and UVM were further investigated via R packages and TIMER 2.0 platform. The therapeutic roles of SPTBN1 in KIRC and UVM were also explored via R software. Following this, the prognostic value and cancer immunological role of SPTBN1 in KIRC and UVM were validated in our cancer patients and GEO database.

RESULTS

Overall, cancer tissue had a lower expression level of SPTBN1 frequently in pan-cancer, compared with those in adjacent nontumor one. SPTBN1 expression often showed a different effect on survival in pan-cancer; upregulation of SPTBN1 was protective to the survival of KIRC individuals, which was contrary from what was found in UVM patients. In KIRC, there were significant negative associations between SPTBN1 expression and pro-tumor immune cell infiltration, including Treg cell, Th2 cell, monocyte and M2-macrophage, and expression of immune modulator genes, such as tumor necrosis factor superfamily member 9 (TNFSF9); while, in UVM, these correlations exhibited opposite patterns. The following survival and expression correlation analysis in our cancer cohorts and GEO database confirmed these previous findings. Moreover, we also found that SPTBN1 was potentially involved in the resistance of immunotherapy in KIRC, and the enhance of anti-cancer targeted treatment in UVM.

CONCLUSIONS

The current study presented compelling evidence that SPTBN1 might be a novel prognostic and therapy-related biomarker in KIRC and UVM, shedding new light on anti-cancer strategy.

Identification of a novel circRNA-miRNA-mRNA regulatory axis in hepatocellular carcinoma based on bioinformatics analysis

In recent years, circular RNAs (circRNAs) have been found to play an essential regulatory role in hepatocellular carcinoma (HCC) through various mechanisms, particularly the endogenous competitive RNA (ceRNA) mechanism. Therefore, it is significant to explore the circRNAs in hepatoma. In this study, we constructed the ceRNA and survival network using Cytoscape. We also used R, Perl software, and multiple online databases and platforms, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to perform overall survival, immune cell infiltration, immune checkpoints, pathway activity, and anticancer drug sensitivity analysis of the genes. Finally, the receiver operator characteristic curve (ROC) analysis was performed to identify the diagnosis value of the genes. KEGG analysis revealed the T cell receptor signaling pathway as the main enrichment pathway. A total of 29 genes related to survival and prognosis were screened out. The findings suggest that ZNF544, WDR76, ACTG1, RASSF3, E2F3, ASRGL1, and POGK are associated with multilevel immune cell infiltration. Additionally, immune checkpoint analysis screened out the ACTG1, E2F3, RASSF3, and WDR76. It was also revealed that the WDR76, E2F3, ASRGL1, and POGK mainly activated the cell cycle and DNA damage response (DDR) pathway. The results suggest that the sensitivity toward trametinib, refametinib (RDEA119), and selumetinib correlates to the expression of WDR76. ROC analysis showed that the area under the curve (AUC) of all genes in the regulatory axis was greater than 0.7. The identified hsa_circ_0000417/hsa_circ_0002688/hsa_circ_0001387--hsa-miR-199a-5p--WDR76 regulatory axis may provide new insights into the progression, clinical diagnosis, and treatment of HCC.

Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning

Idiopathic pulmonary hypertension (IPAH) is a condition that affects various tissues and organs and the metabolic and inflammatory systems. The most prevalent metabolic condition is metabolic syndrome (MS), which involves insulin resistance, dyslipidemia, and obesity. There may be a connection between IPAH and MS, based on a plethora of studies, although the underlying pathogenesis remains unclear. Through various bioinformatics analyses and machine learning algorithms, we identified 11 immune- and metabolism-related potential diagnostic genes (EVI5L, RNASE2, PARP10, TMEM131, TNFRSF1B, BSDC1, ACOT2, SAC3D1, SLA2, P4HB, and PHF1) for the diagnosis of IPAH and MS, and we herein supply a nomogram for the diagnosis of IPAH in MS patients. Additionally, we discovered IPAH's aberrant immune cells and discuss them here.

Risk factors for additional postoperative adjuvant therapy in patients with locally advanced cervical cancer and construction of a risk model

OBJECTIVE

To investigate the influencing factors of postoperative adjuvant therapy for stage IB1-IIA2 cervical cancer, and establish a nomogram model to predict the risk of postoperative adjuvant therapy for locally advanced cervical cancer (LACC).

METHODS

A retrospective analysis was conducted on 144 patients with stage IB1-IIA2 cervical squamous cell carcinoma treated in Wuhan No.1 Hospital from June 2015 to January 2017, and their clinical data were analyzed. The clinical application value of the nomogram risk model was evaluated by receiver operating characteristic curve (ROC).

RESULTS

Through logistic regression analysis, we found that squamous cell carcinoma antigen (SCC-Ag), International Federation of Gynecology and Obstetrics (FIGO) stage ≥ IIA1, and laparoscopic surgery were independent influencing factors for additional adjuvant therapy after laparoscopic surgery. The nomogram model for predicting the risk of postoperative adjuvant therapy for cervical cancer constructed according to the selected variables had good predictive performance (with C-index of 0.798) and conformity. The area under the curve of established model in predicting 1-, 3- and 5-year survival time was 0.730, 0.810 and 0.830, respectively, indicating that the model has good performance.

CONCLUSION

History of diabetes, tumor size, FIGO stage ≥ IIA1, and SCC-Ag >1.5 are independent influencing factors for additional adjuvant therapy after laparoscopic surgery of LACC patients. In addition, the constructed risk model is effective in predicting the postoperative risk of additional adjuvant therapy, which is expected to provide a reference for clinical treatment selection.

A novel prognostic signature for lung adenocarcinoma based on cuproptosis-related lncRNAs: A Review

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease with complex pathogenesis, high mortality, and poor prognosis. Cuproptosis is a new type of programmed cell death triggered by copper accumulation that may play an important role in cancer. LncRNAs are becoming valuable prognostic factors in cancer patients. The effect of cuproptosis-related lncRNAs (CRlncRNAs) on LUAD has not been clarified. Based on the Cancer Genome Atlas database, CRlncRNAs were screened by co-expression analysis of cuproptosis- related genes and lncRNAs. Using CRlncRNAs, Cox and LASSO regression analyses constructed a risk prognostic model. The predictive efficacy of the model was assessed and validated using survival analysis, receiver operating characteristic curve, univariate and multifactor Cox regression analysis, and principal component analysis. A nomogram was constructed and calibration curves were applied to enhance the predictive efficacy of the model. Tumor Mutational Burden analysis and chemotherapeutic drug sensitivity prediction were performed to assess the clinical feasibility of the risk model. The novel prognostic signature consisted of 5 potentially high-risk CRlncRNAs, MAP3K20-AS1, CRIM1-DT, AC006213.3, AC008035.1, and NR2F2-AS1, and 5 potentially protective CRlncRNAs, AC090948.1, AL356481.1, AC011477.2, AL031600.2, and AC026355.2, which had accurate and robust predictive power for LUAD patients. Collectively, the novel prognostic signature constructed based on CRlncRNAs can effectively assess and predict the prognosis of patients and provide a new perspective for the diagnosis and treatment of LUAD.

Construction of ceRNA network and key gene screening in cervical squamous intraepithelial lesions

BACKGROUND

This study aimed to construct an endogenous competition network for cervical squamous intraepithelial lesions using differential gene screening.

METHODS

GSE149763 was used to screen differentially expressed long non-coding RNAs (lncRNAs) and mRNAs to predict correlated microRNAs (miRNAs). The correlated miRNAs and GSE105409 were used to screen differentially expressed miRNAs for differential co-expression analysis, and the co-expressed differentially expressed miRNAs were used to predict correlated mRNAs. Differentially expressed mRNAs, miRNAs, and lncRNAs were visualized, and differential gene screening, enrichment, and pathway analysis were performed.

RESULTS

The ceRNA network of cervical squamous intraepithelial was successfully established and a potential differentially expressed network was identified. The key genes were VEGFA and FOS, and the key pathway was the MAPK signaling pathway.

CONCLUSIONS

The differential expression and potential effects of the lncRNA BACH1-IT1/miR-140-5p/VEGFA axis, key genes, VEGFA and FOS, and MAPK signaling in CIN were clarified, and the occurrence and potential effects of CIN were further clarified. The underlying molecular mechanism provides a certain degree of reference for subsequent treatments and experimental research.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Background

Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.

Methods

From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.

Results

Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.

Conclusion

To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

Identification of PLAUR-related ceRNA and immune prognostic signature for kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) represents one of the most fatal cancers, usually showing malignant progression and a high tumor recurrence rate. The urokinase-type plasminogen activator receptor (PLAUR) plays a critical role in the initiation and progression of several cancers, including KIRC. However, the function and mechanism of PLAUR in patients with KIRC are still unclear and require further investigation. In the present study, we first explored the expression profile and prognostic values of PLAUR in pan-cancer based on The Cancer Genome Atlas and Genotype-Tissue Expression databases. PLAUR was upregulated in multiple cancers and was significantly associated with poor overall survival and disease-free survival only in patients with KIRC. Subsequently, the PVT1/SNHG15-hsa-miR-532-3p axis was identified as the most potential upstream regulatory network of PLAUR in KIRC. In addition, PLAUR expression was closely associated with tumor-infiltrating immune cells, tumor immunity biomarkers, and immunomodulator expression. Furthermore, we constructed a multiple-gene risk prediction signature according to the PLAUR-related immunomodulators (PRIs). A prognostic nomogram was then developed to predict the 1-, 3-, and 5-year survival probabilities of individuals. In conclusion, our study identified the PVT1/SNHG15-hsa-miR-532-3p-PLAUR axis and a prognostic signature of PRIs, which could be a reference for future clinical research.

Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma

Backgrounds

Uterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.

Methods

Consensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.

Results

All UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.

Conclusion

We successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) is widely acknowledged as a malignant tumor with rapid progression, high recurrence rate, and poor prognosis. At present, there is a paucity of reliable biomarkers at the clinical level to guide the management of HCC and improve patient outcomes. Our research is aimed at assessing the prognostic value of MAD2L1 in HCC.

Methods

Four datasets, GSE121248, GSE101685, GSE85598, and GSE62232, were selected from the GEO database to analyze differentially expressed genes (DEGs) between HCC and normal liver tissues. After functional analysis, we constructed a protein-protein interaction network (PPI) for DEGs and identified core genes in this network with high connectivity with other genes. We assessed the relationship between core genes and the pathogenesis and prognosis of HCC. Finally, we explored the gene regulatory signaling mechanisms involved in HCC pathogenesis.

Results

145 DEGs were screened from the intersection of the four GEO datasets. MAD2L1 was associated with most genes according to the PPI network and was selected as a candidate gene for further study. Survival analysis suggested that high MAD2L1 expression in HCC correlated with a worse prognosis. In addition, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC) findings suggested that the expression of MAD2L1 was abnormally increased in HCC tissues and cells compared to paraneoplastic tissues and normal hepatocytes.

Conclusion

We found that high MAD2L1 expression in HCC was significantly associated with overall patient survival and clinical features. We also explored the potential biological properties of this gene.

Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.