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

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours

BACKGROUND

Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, which increases the difficulty of surgery and indicates a poor prognosis. However, the molecular mechanism related to invasiveness remains to be further studied. This study is to screen and identify the characteristic biomarkers of invasive NFPitNETs.

METHODS

Based on the data of 73 NFPitNETs microarray chips in the GSE169498 dataset, this study used weighted gene co-expression network (WGCNA), differential expression analysis, protein-protein interaction (PPI) network analysis and various machine learning methods (XGBOOST, LASSO regression, random forest, support vector machine) to screen candidate biomarkers for invasive NFPitNET. Then, using gene set enrichment analysis (GSEA) to explore the differences in biological activities and signaling pathways between invasive NFPitNET and non-invasive NFPitNET. Single-sample GSEA (ssGSEA) was used to analyze key biomarkers-related signaling pathways. Finally, the expression and function of the key biomarkers were verified by q-RT PCR, immunohistochemical (IHC) experiments and in vitro experiments.

RESULTS

Combined with WGCNA and differential expression analysis, 128 high-expression and 85 low-expression candidate biomarkers were preliminarily obtained. PPI analysis and four machine learning algorithms further identified GALR1, PENK and HOXD9. The receiver operating characteristic (ROC) curve results showed that the three biomarkers had good predictive ability of invasiveness. After combining the validation set data, GALR1 and PENK were the final key biomarkers. Finally, PCR and IHC results verified the decreased expression of GALR1 and PENK in invasive NFPitNET and promotes proliferation and invasive ablity of pituitary tumor cells.

CONCLUSION

This study confirmed that the reduced expression of GALR1 and PENK is an important molecular feature of invasive NFPitNETs, which may play an important role in inhibiting the development of NFPitNET.

Construction of a prognostic risk model for clear cell renal cell carcinomas based on centrosome amplification-related genes

Clear cell renal cell carcinoma (ccRCC) is the urological malignancy with the highest incidence, centrosome amplification-associated genes (CARGs) have been suggested to be associated with carcinogenesis, but their roles in ccRCC are still incompletely understood. This study utilizes bioinformatics to explore the role of CARGs in the pathogenesis of ccRCC and to establish a prognostic model for ccRCC related to CARGs. Based on publicly available ccRCC datasets, 2312 differentially expressed genes (DEGs) were identified (control vs. ccRCC). Disease samples were classified into high and low scoring groups based on CARG scores and analysed for differences to obtain 345 DEGs associated with CARG scores (S-DEGs). 137 candidate genes were obtained by taking the intersection of DEGs and S-DEGs. Six prognostic genes (PCP4, SLN, PI3, PROX1, VAT1L, and KLK2) were then screened by univariate Cox, LASSO, and multifactorial Cox regression. These genes exhibit a high degree of enrichment in ribosome-associated pathways. Both risk score and age were independent prognostic factors, and the Nomogram constructed based on them had a good predictive performance (AUC > 0.7). In addition, immunological analyses identified 6 different immune cells and 23 immune checkpoints between the high- and low-risk groups, whereas mutational analyses identified frequent VHL mutations in both high- and low-risk groups. Finally, 93 potentially sensitive drugs were identified. In conclusion, this study identified six CARGs as prognostic genes for ccRCC and established a risk model with predictive value. These findings provide insights for prognostic prediction of ccRCC, optimisation of clinical management and development of targeted therapeutic strategies.

Prognostic Value of Ferroptosis-Immunity-Related Signature Genes in Cervical Cancer Radiotherapy Resistance and Risk Modeling

Introduction

The aim of this study was to clarify the genome of ferroptosis in the genes involved in radiotherapy resistance and regulation of tumor immune microenvironment by multigene analysis of cervical cancer (CC) patients.

Methods

Different radiation sensitivity samples from CC patients were collected for RNA sequencing. Differentially expressed genes (DEGs) between the RNA dataset and the GSE9750 dataset were considered as radiotherapy-DEGs. The intersection genes of radiotherapy-DEGs with ferroptosis-related genes (FRGs) and the intersection genes of radiotherapy-DEGs with immune-related genes (IRGs) were labeled as FRGs-IRGs-DEGs (FIGs). A risk model was established by prognostic genes selected from FIGs by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis. The results were further validated using samples from CC tissue samples.

Results

The 329 DEGs related to CC radiotherapy were identified. LSAAO analysis was utilized to identify five prognostic genes (CALCRL, UCHL1, GNRH1, ACVRL1, and MUC1) from six candidate prognosis genes and construct a risk model. The risk model demonstrated favorable effectiveness in predicting outcomes at 1, 3, and 5 years, as evidenced by ROC curves. Univariate and multivariate Cox regression analysis demonstrated that CALCRL, GNRH1, and MUC1 were independent prognostic factors. The results of functional similarity analysis showed that CALCRL, UCHL1, ACVRL1 and MUC1 had high average functional similarity. The results of PCR and IHC showed the same trend with the results above.

Discussion

A novel prognostic model related to ferroptosis and immune microenvironment in CC radiotherapy was developed and validated, providing valuable guidance for personalized anti-cancer therapy.

Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing

Background

Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While N 6-methyladenosine (m6A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.

Methods

Transcriptomic datasets of sepsis, along with m6A-related genes (m6A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m6A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m6A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.

Results

DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4+ memory cells, and CD16+ and CD14+ monocytes. The expression of DPP4 was further validated in clinical samples.

Conclusions

DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.

Constructing and validating a novel prognostic risk score model for rectal cancer based on four immune-related genes

Background

Immunotherapy is playing an increasing role in the treatment of various cancers. However, its application in rectal cancer is very limited as only microsatellite-unstable bowel cancers with defective mismatch repair are found to benefit. The majority of rectal cancers belong to the microsatellite-stable phenotype. Therefore, the aim of this study is to explore immune-related genes within the tumor microenvironment of rectal cancer, with the objective of discovering novel biomarkers and therapeutic targets for rectal cancer, and to establish a new prognostic prediction model for rectal cancer based on these immune-related genes.

Methods

The data in The Cancer Genome Atlas (TCGA) database were processed using the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to obtain differently expressed genes (DEGs). Then the DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Gene and Genomes (KEGG), Reactome function enrichment analysis, and protein-protein interaction (PPI) analysis to screen the core genes, which were utilized to compute the risk scores of individual patients. Finally, combining risk scores and clinical characteristics, a new prognostic prediction model was established by univariate and multivariate Cox analyses, and the prognostic model was validated by the Gene Expression Omnibus (GEO) database.

Results

The study finally identified four core genes (CYBB, CCR4, FOXP3, and CD80), and immune cell infiltration analyses of the four core genes showed that their expression levels were positively correlated with the distribution of various immune cells. The 4-gene risk score categorized rectal cancer patients into high-risk and low-risk groups, and the results showed that the low-risk group had a stronger correlation with the immune response and had a better prognosis. A prognostic model was developed by integrating risk scores and clinical characteristics and showed a strong predictive effect.

Conclusions

In patients with rectal cancer, CYBB, CCR4, FOXP3, and CD80 are immune-related core genes, and low expression of each gene is associated with poor clinical prognosis. The risk score obtained on their basis is independent prognostic factors for rectal cancer, suggesting that the four core genes may provide a foundation for the development of new prognostic biomarkers for rectal cancer and the study of immunotherapy.

Mitochondrial permeability transition drives the expression, identification and validation of necrosis-related genes in prognostic risk models of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor, and the current treatment methods exhibit various limitations. In recent years, the role of mitochondrial permeability transition-driven necrosis-related genes (MPT-DNRGs) in the pathogenesis and progression of severe diseases, particularly tumors, has garnered significant attention. This study aimed to identify new targets and concepts for MPT-DNRG-targeted therapy in HCC.

Methods

In this study, we utilized HCC-related datasets and MPT-DNRGs to identify differentially expressed genes (DEGs) between HCC patients and control groups. By conducting a cross-analysis of the results of DEGs and MPT-DNRGs, we screened candidate genes. Subsequently, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis methods were employed to identify prognostic genes, which were used to construct a risk model and calculate individual risk scores for HCC patients. Additionally, we performed univariate and multivariate Cox regression analyses to identify independent prognostic factors and constructed a column chart based on these factors to predict the survival probability of HCC patients. Furthermore, gene set enrichment analysis (GSEA), the immune microenvironment, chemotherapy drugs, and the expression of prognostic genes between the two groups were analyzed. Finally, the expression of these prognostic genes was further confirmed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology.

Results

In this study, we identified 8,515 DEGs between HCC and control samples. By performing intersection analysis between DEGs and MPT-DNRGs, we pinpointed 15 candidate genes. Subsequently, through univariate Cox regression and LASSO regression analysis, we identified six genes (LMNB2, LMNB1, BAK1, CASP7, LMNA, and AKT1) that were significantly associated with overall survival (OS) in patients. Based on the median risk score, we categorized HCC patients into high-risk and low-risk groups. Kaplan-Meier (KM) survival analysis results demonstrated a significant difference in OS between the two groups, which was further validated through additional assessment. Furthermore, we constructed a nomogram to predict the survival probability of HCC patients. Moreover, GSEA revealed a crucial correlation between these genes and HCC, and highlighted a close association between risk scores and regulatory T cells. We also identified four chemotherapy drugs related to HCC. Finally, in both the training and validation cohorts, LMNB2, LMNB1, and LMNA exhibited high expression levels in tumor samples. Further validation using RT-qPCR confirmed that the expression of all prognostic genes was significantly higher in HCC group compared to the control group.

Conclusions

This study explored six prognostic genes (LMNB2, LMNB1, BAK1, CASP7, LMNA and AKT1) associated with MPT-DNRGs in HCC, which provides a reference for further research on HCC.

A prognostic model of lung adenocarcinoma constructed based on circadian rhythm genes and its potential clinical significance

Background

Lung adenocarcinoma (LUAD) is a common pathological category of lung cancer. Circadian rhythm (CR) disruption has been demonstrated to impact on lung tumorigenesis in mouse models. The aim of this study was to mine genes relevant to CR in LUAD and construct a corresponding risk model.

Methods

CRRGs from GSEA-MsigDB were filtered by overlapping DEGs in LUAD and NC specimens, two clusters with survival and clinical discrepancies, and CRRGs. Cox regression analysis (univariate and multivariate) was used to establish a CR-relevant risk model, which was validated in both the training and validation sets. Differences in immune infiltration, immunotherapy, and drug sensitivity between subgroups were explored. Prognostic gene expression was tested in clinical cancer and paracancer tissue samples using RT-qPCR.

Results

A grand total of two prognostic genes (CDK1 and HLA-DMA) related to CR were screened. The AUC values of a CR-relevant risk model in predicting 1/3/5-years survival in LUAD patients were greater than 0.6, indicating that the efficiency of the model was decent. Then, the results of CIBERSORT demonstrated noticeable differences in the tumor microenvironment between CR-relevant high- and low-risk subgroups. In addition, the CR-relevant risk score could be performed to estimate the effectiveness of immunotherapy in LUAD patients. The sensitivity of three common drugs (homoharringtonine, lapatinib, and palbociclib) in LUAD could be evaluated by the CR-relevant risk model. Ultimately, the experimental results confirmed that the expression trends of CDK1 and HLA-DMA in our collected clinical samples were in line with the expression trends in the TCGA-LUAD dataset.

Conclusion

In conclusion, a CR-relevant risk model based on CDK1 and HLA-DMA was constructed by using bioinformatics analysis, which might supply a new insight into the improved prognosis of LUAD.

Unveiling prognostic value of JAK/STAT signaling pathway related genes in colorectal cancer: a study of Mendelian randomization analysis

BACKGROUND

Colorectal cancer (CRC) ranks among the frequently occurring malignant neoplasms affecting the gastrointestinal tract. This study aimed to explore JAK-STAT signaling pathway related genes in CRC and establish a new prognostic model.

METHODS

The data set used in this study is from a public database. JAK-STAT-differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). Prognostic genes were selected from JAK-STAT-DEGs through Mendelian randomization (MR), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) analyses. The expressions of prognostic genes were verified by RT-qPCR. Then, a risk model was built and validated by the GSE39582. Independent prognostic factors were screened underlying risk scores and different clinical indicators, resulting in the construction of a nomogram. Additionally, immune infiltration, immune scores and immune checkpoint inhibitors analyses and gene set enrichment analysis (GSEA) were carried out.

RESULTS

The 3,668 JAK-STAT-DEGs were obtained by intersection of 5826 CRC-DEGs and 9766 JAK-STAT key module genes. Five prognostic genes were selected (ANK3, F5, FAM50B, KLHL35, MPP2), and their expressions were significantly different between CRC and control groups. A risk model was constructed according to prognostic genes and verified by GSE39582. In addition, the nomogram exhibited superior predictive accuracy for CRC. Furthermore, immune analysis results indicated a notable positive correlation between risk score and the scores of immune (R = 0.486), stromal (R = 0.309), and ESTIMATE (R = 0.422). Immune checkpoint inhibitor ADORA2A (Cor = 0.483263) exhibited the strongest positive correlation with risk score. And MPP2 exhibited the most potent activating influence on the cell cycle pathway, whereas ANK3 demonstrated the most significant inhibitory effect within the apoptosis pathway.

CONCLUSIONS

A new JAK-STAT related CRC prognostic model was constructed and validated, which possessed an underlying predictive potential for CRC patients' prognosis and could potentially enhance tailored guidance for immunotherapy.

Analysis and exploration of regulatory mechanisms and potential prognostic biomarkers in squamous cell carcinoma of the lung by expression profiling

Background

Lung cancer is the most common malignant tumor in China. In 2016, more than 800,000 new cases of lung cancer were diagnosed in China. Squamous cell carcinoma of the lung, a type of non-small cell lung cancer (NSCLC), accounts for 25-30% of all lung cancer cases, and has an overall 5-year survival rate of about 32.53%, lower than adenocarcinoma for which there have been far more therapeutic advances in the last few decades. The purpose of this study was to explore the mechanisms of the disease and to identify potential prognostic biomarkers.

Methods

This study analyzed lung squamous cell carcinoma of the lung tissues and paraneoplastic tissues to identify differentially expressed genes (DEGs). We conducted a Gene Set Enrichment Analysis and prognostic analysis by constructing competing endogenous RNA (ceRNA) networks; we performed a correlation analysis of the target genes and verified the targeting relationship of the ceRNA by cellular assays. We assessed the effects of the target genes on tumor cell proliferation, invasion and apoptosis by Cell Counting Kit-8 (CCK-8) assays, invasion assays, and caspase 3/7 assays, respectively.

Results

We identified 4,039 downregulated genes and 1,924 upregulated genes. The p53 pathway, cell-cycle pathway and mismatch-repair (MMR) pathway were activated, while the mitogen-activated protein kinase pathway was inhibited. Two ceRNA networks centered on the long non-coding RNAs (lncRNAs) MAGI2-AS3 and LINC01089 were constructed. MAGI2-AS3 was found to regulate five messenger RNAs (mRNAs) (i.e., MBNL2, ATP5L, FAM103A1, MDH1, and STXBP1) through three microRNAs (miRNAs), whereas LINC01089 was found to regulate six mRNAs (i.e., ZFP36L2, APBB2, PDLIM3, MYADM, PHF5A, and SLC26A9) through two miRNAs. The expression of these lncRNAs and mRNAs was significantly associated with prognosis (P<0.05). A significant correlation was also found between the expression of MAGI2-AS3 and MBNL2 (R=0.51), and both signatures were also significantly associated with prognosis. We also found that MAGI2-AS3 and MBNL2 had a regulatory relationship at the cellular level, for example, high expression of MBNL2 was noted to inhibit cancer cell proliferation and migration yet promote apoptosis.

Conclusions

MAGI-AS3 and MBNL2 are both differentially expressed in squamous cell carcinoma of the lung and are potential prognostic markers. A significant association was also found between MAGI2-AS3 and the expression of MBNL2 (R=0.51). High expression of MBNL2 inhibits cancer cell proliferation and migration, yet promotes cancer cell apoptosis.

A nomograph model for predicting the risk of diabetes nephropathy

OBJECTIVE

Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.

METHODS

First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods. A nomogram model was constructed for the risk prediction of DKD. Finally, the predictive performance, predictive value, calibration, and clinical effectiveness of the model were evaluated through the receipt of ROC curves, Brier score values, calibration curves (CC), and decision curves (DCA). In addition, we will visualize it.

RESULTS

A total of 4371 participants were selected and included in this study. Patients were randomly divided into a training set (n = 3066 people) and a validation set (n = 1305 people) in a 7:3 ratio. Using machine learning algorithms and drawing Venn diagrams, five variables significantly correlated with DKD risk were identified, namely Age, Hba1c, ALB, Scr, and TP. The area under the ROC curve (AUC) of the training set evaluation index for this model is 0.735, the net benefit rate of DCA is 2%-90%, and the Brier score is 0.172. The area under the ROC curve of the validation set (AUC) is 0.717, and the DCA curve shows a good net benefit rate. The Brier score is 0.177, and the calibration curve results of the validation set and training set are almost consistent.

CONCLUSION

The DKD risk nomogram model constructed in this study has good predictive performance, which helps to evaluate the risk of DKD as early as possible in clinical practice and formulate relevant intervention and treatment measures. The visual result can be used by doctors or individuals to estimate the probability of DKD risk, as a reference to help make better treatment decisions.

Unveiling and validating biomarkers related to the IL-10 family in chronic sinusitis with nasal polyps: insights from transcriptomics and single-cell RNA sequencing analysis

Introduction

Extensive efforts have been made to explore members of the IL-10 family as potential therapeutic strategies for various diseases; however, their biological role in chronic rhinosinusitis with nasal polyps (CRSwNP) remains underexplored.

Methods

Gene expression datasets GSE136825, GSE179265, and GSE196169 were retrieved from the Gene Expression Omnibus (GEO) for analysis. Candidate genes were identified by intersecting differentially expressed genes (DEGs) between the CRSwNP and control groups (DEGsall) with those between the high- and low-score groups within the CRSwNP cohort (DEGsNP). Biomarker selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the Boruta algorithm. Further refinement of biomarkers was carried out using receiver operating characteristic (ROC) analysis, with genes demonstrating an area under the curve (AUC) greater than 0.7 being considered significant. Genes exhibiting consistent expression trends and significant differences across both GSE136825 and GSE179265 were selected as potential biomarkers. Cell-type annotation was performed on GSE196169, and the expression profiles of the biomarkers across various cell types were analyzed. A competing endogenous RNA (ceRNA) network and a biomarker-drug interaction network were also established. Additionally, the mRNALocater database was utilized to determine the cellular localization of the identified biomarkers.

Results

The intersection of 1817 DEGsall and 24 DEGsNP yielded 15 candidate genes. Further filtering through LASSO, SVM-RFE, and Boruta led to the identification of seven candidate biomarkers: PRB3, KRT16, MUC6, SPAG4, FGFBP1, NR4A1, and GSTA2. Six of these genes demonstrated strong diagnostic performance in GSE179265, while four biomarkers, showing both significant differences and consistent expression trends, were validated in both GSE179265 and GSE136825. Single-cell sequencing analysis of GSE196169 revealed seven distinct cell types, including endothelial cells, with the biomarkers predominantly expressed in epithelial cells. The ceRNA network comprised nine nodes and eleven edges, with only FGFBP1 exhibiting a complete lncRNA-miRNA-mRNA interaction.

Discussion

This study identifies several novel biomarkers and their associated drugs for CRSwNP therapy, as well as potential therapeutic targets, such as spiperone and arnenous acid, identified through molecular docking. Ultimately, this work underscores the identification of four IL-10 family-related biomarkers, providing a theoretical foundation for future clinical research in CRSwNP.

Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy

AIMS

To explore tyrosine metabolism-related characteristics in liver hepatocellular carcinoma (LIHC) and to establish a risk signature for the prognostic prediction of LIHC. Novel prognostic signatures contribute to the mining of novel biomarkers, which are essential for the construction of a precision medicine system for LIHC and the improvement of survival.

BACKGROUND

Tyrosine metabolism plays a critical role in the initiation and development of LIHC. Based on the tyrosine metabolism-related characteristics in LIHC, this study developed a risk signature to improve the prognostic prediction of patients with LIHC.

OBJECTIVE

To investigate the correlation between tyrosine metabolism and progression of LIHC and to develop a tyrosine metabolism-related prognostic model.

METHODS

Gene expression and clinicopathological information of LIHC were obtained from The Cancer Genome Atlas (TCGA) database. Distinct subtypes of LIHC were classified by performing consensus cluster analysis on the tyrosine metabolism-related genes. Univariate and Lasso Cox regression were used to develop a RiskScore prognosis model. Kaplan-Meier (KM) survival analysis with log-rank test and area under the curve (AUC) of receiver operating characteristic (ROC) were employed in the prognostic evaluation and prediction validation. Immune infiltration, tyrosine metabolism score, and pathway enrichment were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, a nomogram model was developed with the RiskScore and other clinicopathological features.

RESULTS

Based on the tyrosine metabolism genes in the TCGA cohort, we identified 3 tyrosine metabolism-related subtypes showing significant prognostic differences. Four candidate genes selected from the common differentially expressed genes (DEGs) between the 3 subtypes were used to develop a RiskScore model, which could effectively divide LIHC patients into high- and lowrisk groups. In both the training and validation sets, high-risk patients tended to have worse overall survival, less active immunotherapy response, higher immune infiltration and clinical grade, and higher oxidative, fatty, and xenobiotic metabolism pathways. Multivariate analysis confirmed that the RiskScore was an independent indicator for the prognosis of LIHC. The results from pan-- cancer analysis also supported that the RiskScore had a strong prognostic performance in other cancers. The nomogram demonstrated that the RiskScore contributed the most to the prediction of LIHC prognosis.

CONCLUSION

Our study developed a tyrosine metabolism-related risk model that performed well in survival prediction, showing the potential to serve as an independent prognostic predictor for LIHC treatment.

Identification and validation of a novel prognostic model based on anoikis‑related genes in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematological cancer prevalent worldwide. Anoikis-related genes (ARGs) are crucial in the progression of cancer and metastasis of tumors. However, their role in AML needs to be clarified. In the present study, differential analysis was performed on data from The Cancer Genome Atlas database to identify differentially expressed ARGs (DE-ARGs). Subsequently, a prognostic model for patients with AML was constructed using univariate Cox, Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses. This model was based on four key DE-ARGs [lectin galactoside-binding soluble 1 (LGALS1), integrin subunit α 4 (ITGA4), hepatocyte growth factor (HGF) and Ras homolog gene family member C (RHOC)]. Independent prognostic factors for AML included prior treatment, age, risk scores and diagnosis. A nomogram was constructed based on these factors to aid clinical decision-making. Furthermore, bone marrow samples were collected from individuals diagnosed with AML and healthy donors to validate the expression of the identified ARGs using reverse transcription-quantitative PCR. The mRNA levels of LGALS1 and RHOC were significantly higher, while those of ITGA4 and HGF were significantly lower in patients with AML than in healthy donors (all P<0.05). The results of the present study expands the understanding of the function of ARGs in AML, providing a new theoretical basis for the treatment of AML.

Identification and Validation of a Novel Prognostic Signature of Gastric Cancer Based on Seven Complement System-Related Genes: An Integrated Analysis

The complement system (CS) is linked to the progression of gastric cancer (GC), which has a high mortality rate, though its mechanisms in GC remain unclear. This study aims to identify CS-related prognostic genes with causal links to GC, and to investigate their mechanisms. The intersection between differentially expressed genes (DEGs) obtained from the TCGA-STAD dataset and CS-related genes (CRGs) was defined as differentially expressed CRGs (DCRGs). Prognostic genes with a causal association with GC (pCDCRGs) were sequentially identified via Mendelian randomization (MR) analysis and Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, followed by expression analysis. A gene signature and a nomogram were then established based on pCDCRGs and independent prognostic factors. Subsequent analyses focused on functional enrichment, immune relevance, drug sensitivity, gene interactions, and molecular regulatory networks. Eventually, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate expression of pCDCRGs. DCRGs were obtained from the intersection of 8,418 DEGs and 241 CRGs. Among 12 DCRGs with causal association (CDCRGs) with GC, 7 genes were identified as pCDCRGs, including FANCG, FANCF, F2R, C4BPA, SERPINF2, PROC, and CD59. Notably, CD59 was markedly highly expressed in the normal group, whereas the other genes were markedly highly expressed in the GC group. Afterward, an accurate pCDCRG signature was developed. Risk score, age, and stage were recognized as independent risk factors, and the constructed nomogram demonstrated strong predictive accuracy. Additionally, analyses indicated that these 7 pCDCRGs may influence GC by affecting pathways such as complement and coagulation cascades, immune cell infiltration, immune characteristics, immunotherapy responses, and drug sensitivity. These effects may be linked to gene interactions and the regulatory roles of lncRNAs like RMRP and miRNAs such as hsa-mir-613. RT-qPCR showed C4BPA, PROC, F2R, and SERPINF2 were markedly up-regulated, whereas CD59 was markedly down-regulated in GC tissues. This study identified seven complement system-related prognostic genes with causal links to GC, based on which we developed a highly predictive 7-pCDCRG signature, providing valuable insights for clinical prognostic prediction and immunotherapy in GC patients.

Identification of the shared gene signatures in retinoblastoma and osteosarcoma by machine learning

Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively. RNA sequences and patient information for OS and RB were obtained from the University of California Santa Cruz (UCSC) Xena and Gene Expression Omnibus databases. When RB and OS were first identified, a common gene expression profile was discovered. Weighted Gene Co-expression Network Analysis (WGCNA) revealed co-expression networks associated with OS after immunotyping patients. To evaluate the genes shared by RB and OS, univariate and multivariate Cox regression analysis were then carried out. Three machine learning methods were used to pick key genes, and risk models were created and verified. Next, medications that target independent prognostic genes were found using the Cellminer database. The comparison of differential gene expression between OS and RB revealed 1216 genes, primarily linked to the activation and proliferation of immune cells. WGCNA identified 12 modules related to OS immunotyping, with the grey module showing a strong correlation with the immune-inflamed phenotype. This module intersected with differential genes from RB, producing 65 RB-associated OS Immune-inflamed Genes (ROIGs). Analysis identified 6 hub genes for model construction through univariate Cox regression and three machine learning techniques. A risk model based on these hub genes was established, demonstrating significant prognostic value for OS. Genes shared between OS and RB contribute to the progression of both cancers through multiple pathways. The ROIGs risk score model independently predicts the overall survival of OS patients. Additionally, this study highlights genes with potential as therapeutic targets or biomarkers for clinical use.

In Vitro and Vivo Experiments Revealing Astragalin Inhibited Lung Adenocarcinoma Development via LINC00582/miR-140-3P/PDPK1

This study aimed to explore the mechanism of the development of lung adenocarcinoma (LUAD) treated by astragalin. Transcriptome sequencing was performed to obtain the gene profile of LUAD treated by astragalin. Combining with bioinformatics analysis including differential gene screening, function enrichment analysis (gene ontology and KEGG), and ceRNA construction, we obtained the novel mechanism of lncRNA mediated miRNA/mRNA axis. Then, the cell experiments were performed to examine the role of lncRNA in cell proliferation, migration and invasion, and apoptosis for LUAD treated with astragalin. Moreover, the tumor formation in nude mice was carried out to detect the ceRNA mechanism in LUAD treated by astragalin in vivo. The lncRNA mediated ceRNA network was obtained, that is, LINC00852 LINC00582/miR-140-3p/PDPK1 played an important role in LUAD treated by astragalin. Function experiments indicated that si-LINC00852 inhibited LUAD cell proliferation, migration and invasion, and promoted cell apoptosis via miR-140-3p/PDPK1 (p < 0.05, p < 0.01). The animal experiments further confirmed that si-LINC00852 inhibited tumor growth through miR-140-3p/PDPK1 in vivo. Conversely, this study provides comprehensive insights into the diagnostic and therapeutic implications of LINC00582 in LUAD, LINC00582 mediated miR-140-3p/PDPK1 axis was the novel drug target of astragalin for treating LUAD.

Integrative analysis of homologous recombination repair patterns unveils prognostic signatures and immunotherapeutic insights in breast cancer

Globally, breast cancer (BC) is the leading cause of female death and morbidity. Homologous recombination repair (HRR) is critical in BC. However, the prognostic role and immunotherapy response of HRR in BC remains to be clarified. Firstly, we identified HRR types in BC samples from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset (GSE42568) based on 65 HRR genes (HRRGs). A differentially expressed gene (DEG) list for different HRR types was generated. Then, the influences of gene sets composed of these DEGs on biological pathways and BC prognosis were explored. Next, we identified gene clusters based on gene sets composed of DEGs. Genes associated with prognosis for DEGs were identified using univariate Cox regression. Finally, the HRR score was constructed based on genes associated with prognosis. We analyzed how HRR score correlates with tumor mutation burden (TMB), immune cell infiltration (ICI), and immunotherapy response. Three HRR clusters were discovered. HRR subtype A demonstrated decreased infiltration and a high number of immunosuppressive cells with a poor prognosis. DEGs among various HRR types were predominantly enriched in cell cycle and genomic stability-related pathways. The prognostic model based on sixteen DEGs accurately predicted BC prognosis. The HRRGs were differentially expressed in three DEG clusters. TMB, ICI, and immunotherapy responses differed significantly between the high and low HRR groups (HSG, LSG). The HSG was distinguished by a high degree of ICI and low TMB. LSG had a better response to anti-PD-1 or anti-PD-1 and anti-CTLA4 combination therapy. This work revealed that HRR patterns would contribute to predicting prognosis and immunotherapy response in BC, which may benefit patients.

Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis

Background

Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis.

Methods

Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers.

Results

A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1, CD36, MICB, and PRDX6. Predictive nomograms were generated based on these biomarkers. ANXA1, CD36, and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin.

Conclusion

This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research.

Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma

Introduction

Lung cancer is a leading cause of cancer-related deaths worldwide. Black/African American (B/AA) populations, in particular, exhibit the highest incidence and mortality rates of lung adenocarcinoma (LUAD) in the United States.

Methods

This study aims to explore gene expression patterns linked to LUAD in B/AA and case-matched white patients, with the goal of developing predictive models for prognosis. Leveraging RNA sequencing data from The Cancer Genome Atlas (TCGA) database, genes and pathways associated with overall survival (OS) were identified.

Results

The OS-associated genes in B/AA patients were distinct from those in white patients, showing predominant enrichment in immune-related pathways. Furthermore, mRNA co-expression network analysis revealed that OS-associated genes in B/AA patients had higher levels of interaction with various pathways, including those related to immunity, cell-ECM interaction, and specific intracellular signaling pathways. Notably, a potential B/AA-specific biomarker, C9orf64, demonstrated significant correlations with genes involved in immune response. Unsupervised machine learning algorithms stratified B/AA patients into groups with distinct survival outcomes, while supervised algorithms demonstrated a higher accuracy in predicting survival for B/AA LUAD patients compared to white patients.

Discussion

In total, this study explored OS-associated genes and pathways specific for B/AA LUAD patients. Further validation and clinical application of these findings are warranted to address disparities and improve outcomes in diverse patient populations.

Development of oxidative stress- and ferroptosis-related prognostic signature in gastric cancer and identification of CDH19 as a novel biomarker

BACKGROUND

Ferroptosis is a unique mode of cell death that is iron-dependent and associated with oxidative stress and lipid peroxidation. Oxidative stress and ferroptosis are essential mechanisms leading to metabolic abnormalities in cells and have been popular areas in cancer research.

METHODS

Initially, 76 oxidative stress and ferroptosis-related genes (OFRGs) were acquired by intersecting the gene sets from oxidative stress and ferroptosis. Afterwards, optimal OFRGs were screened using PPI networks, and individuals were separated into two OFRG subtypes (K = 2). Subsequently, we successfully constructed and verified a prognostic signature comprising SLC7A2, Cadherin 19 (CDH19), and CCN1. To further uncover potential biomarkers of gastric cancer (GC), we examined the expression level of CDH19, investigated the effects of knocking down CDH19 on the biological behavior of GC cells, and explored whether CDH19 is involved in ferroptosis and oxidative stress processes.

RESULTS

According to the findings, individuals in the low-risk scoring group have less infiltration of immune suppressive cells, fewer occurrences of immune escape and dysfunction, greater efficacy in chemotherapy and immunotherapy, and better survival outcomes. The qRT-PCR assay indicated that CDH19 expression was significantly higher in GC cells. Through experiments, we demonstrated that knocking down CDH19 can affect the transcription levels of ACSL4 and GPX4, increase intracellular iron ion concentration and accumulation of reactive oxygen species (ROS), and inhibit the proliferation and migration of GC cells.

CONCLUSION

We developed an OFRG-related signature to predict the prognosis and treatment responsiveness of individuals with GC and identified CDH19 as a possible therapeutic target for GC.

Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing

BACKGROUND

Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis.

METHODS

Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients.

RESULTS

ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength.

CONCLUSION

This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.

Exploring a specific type of tissue-resident natural killer cell involved in the anti-tumor and immunotherapy response in human papillomavirus-positive head and neck squamous cell carcinoma using scRNA-seq

Background

Human papillomavirus (HPV)-positive head and neck squamous cell carcinoma (HNSCC) is an increasingly common malignancy. We aimed to explore the immune heterogeneity of natural killer (NK) cells in HPV-positive HNSCC.

Methods

Single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing datasets of HPV-positive HNSCC data were obtained from the Gene Expression Omnibus (GEO) database. "Seurat", "harmony", and "SingleR" were used to perform the scRNA-seq analysis. Subsequently, the "cellphonedb" package was used for the cell crosstalk analysis, and the "clusterProfiler" package was used for the hallmark pathway enrichment analysis. Finally, the "gene set variation analysis" ("GSVA") package was used for the immune cell infiltration, Tumor Immune Dysfunction and Exclusion (TIDE), and risk-score analyses.

Results

A total of 30,562 cells were classified into 9 cell clusters that comprised 6 main cell types [i.e., T cells, natural killer T (NKT) cells, NK cells, B cells, plasma cells, and macrophages]. The NK cells were then further clustered into 3 tissue-resident NK (trNK0-2) and 2 tumor-associated NK (taNK0-1) cell types. The trNK0 cell type, which exhibited inhibitory cancer hallmark activity, appeared to exert potential anti-tumor effects via trNK0-macrophage crosstalk. The trNK score could serve as an independent and valuable prognostic classifier, as the patients with high-trNK scores had better outcomes, immune-infiltration levels, and immunotherapy effects.

Conclusions

Using an scRNA-seq analysis, we identified a specific type of tissue-resident NK cell (i.e., trNK-0) that was involved in the anti-tumor and immunotherapy response in HPV-positive HNSCC.

Identifying the prognostic significance of mitophagy-associated genes in multiple myeloma: a novel risk model construction

Multiple myeloma (MM) is a highly heterogeneous hematological malignancy that is currently incurable. Individualized therapeutic approaches based on accurate risk assessment are essential for improving the prognosis of MM patients. Nevertheless, current prognostic models for MM exhibit certain limitations and prognosis heterogeneity still an unresolved issue. Recent studies have highlighted the pivotal involvement of mitochondrial autophagy in the development and drug sensitivity of MM. This study seeks to conduct an integrative analysis of the prognostic significance and immune microenvironment of mitophagy-related signature in MM, with the aim of constructing a novel predictive risk model. GSE4581 and GSE47552 datasets were acquired from the Gene Expression Omnibus database. MM-differentially expressed genes (DEGs) were identified by limma between MM samples and normal samples in GSE47552. Mitophagy key module genes were obtained by weighted gene co-expression network analysis in the Cancer Genome Atlas (TCGA)-MM dataset. Mitophagy DEGs were identified by the overlap genes between MM-DEGs and mitophagy key module genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, and a risk model was subsequently constructed based on these prognostic genes. Subsequently, the MM samples were stratified into high- and low-risk groups based on their median risk scores. The validity of the risk model was further evaluated using the GSE4581 dataset. Moreover, a nomogram was developed using the independent prognostic factors identified from the risk score and various clinical indicators. Additionally, analyses were conducted on immune infiltration, immune scores, immune checkpoint, and chemotherapy drug sensitivity. The 17 mitophagy DEGs were obtained by intersection of 803 MM-DEGs and 1084 mitophagy key module genes. Five prognostic genes (CDC6, PRIM1, SNRPB, TOP2A, and ZNF486) were selected via LASSO and univariate cox regression analyses. The predictive performance of the risk model, which was constructed based on the five prognostic genes, demonstrated favorable results in both TCGA-MM and GSE4581 datasets as indicated by the receiver operating characteristic (ROC) curves. In addition, calibration curve, ROC curve, and decision curve analysis curve corroborated that the nomogram exhibited superior predictive accuracy for MM. Furthermore, immune analysis results indicated a significant difference in stromal scores of two risk groups categorized on median risk scores. And four immune checkpoints (CD274, CTLA4, LAG3, and PDCD1LG2) showed significant differences in different risk groups. The analysis of chemotherapy drug sensitivity revealed that etoposide and doxorubicin, which target TOP2A, exhibited superior treatment outcomes in the high-risk group. A novel prognostic model for MM was developed and validated, demonstrating significant potential in predicting patient outcomes and providing valuable guidance for personalized immunotherapy counseling.

Analysis of risk factors affecting the prognosis of angiosarcoma patients: a retrospective study

This study aimed to identify prognostic factors influencing the survival of angiosarcoma patients and to explore the relationship between peripheral blood indicators and patient prognosis. A retrospective analysis was conducted on the clinical data collected from 105 angiosarcoma patients treated at China-Japan Union Hospital of Jilin University from January 2004 to April 2019, with an additional 50 patients included as external validation cohort. The median survival time for the study cohort was 1395 days, with 66.7% of patients (n=70) dying during the follow-up period. Significant differences were observed between the survival and death groups in age (P=0.022), primary tumor site (P=0.013), tumor size (P=0.008), and metastasis (P=0.018). Analysis of peripheral blood indicators showed that white blood cell (WBC) (P=0.006), platelet (PLT) (P=0.019), platelet-to-lymphocyte ratio (PLR) (P<0.001), and systemic immune-inflammation index (SII) (P=0.036) were significantly lower in the survival group, while lymphocyte (LYM) (P<0.001), albumin (ALB) (P<0.001), and prognostic nutritional index (PIN) (P<0.001) were significantly higher in the survival group. Multivariate Cox regression analysis identified SII (P=0.049, HR=0.551, 95% CI: 0.304-0.998), primary tumor site (P=0.001, HR=0.405, 95% CI: 0.235-0.699), metastasis (P=0.029, HR=1.864, 95% CI: 1.066-3.26), and chemotherapy (P=0.004, HR=0.434, 95% CI: 0.245-0.768) as independent prognostic factors affecting patients' 5-year survival. A nomogram model constructed based on these factors demonstrated high accuracy and stability in predicting 1-year, 3-year, and 5-year survival rates, with area under the curve (AUC) values of 0.836, 0.837, and 0.803, respectively, as validated by calibration curves and receiver operating characteristic (ROC) analysis. External validation further confirmed the model's reliability. Additionally, significant interactions were found between SII and primary tumor site (P=0.005) as well as chemotherapy (P=0.045). In conclusion, SII, primary tumor site, metastasis, and chemotherapy are crucial prognostic factors for angiosarcoma, and the developed nomogram provides a reliable tool for predicting survival outcomes.

Identification of key genes and diagnostic model associated with circadian rhythms and Parkinson's disease by bioinformatics analysis

Background

Circadian rhythm disruption is typical in Parkinson's disease (PD) early stage, and it plays an important role in the prognosis of the treatment effect in the advanced stage of PD. There is growing evidence that circadian rhythm genes can influence development of PD. Therefore, this study explored specific regulatory mechanism of circadian genes (C-genes) in PD through bioinformatic approaches.

Methods

Differentially expressed genes (DEGs) between PD and control samples were identified from GSE22491 using differential expression analysis. The key model showing the highest correlation with PD was derived through WGCNA analysis. Then, DEGs, 1,288 C-genes and genes in key module were overlapped for yielding differentially expressed C-genes (DECGs), and they were analyzed for LASSO and SVM-RFE for yielding critical genes. Meanwhile, from GSE22491 and GSE100054, receiver operating characteristic (ROC) was implemented on critical genes to identify biomarkers, and Gene Set Enrichment Analysis (GSEA) was applied for the purpose of exploring pathways involved in biomarkers. Eventually, immune infiltrative analysis was applied for understanding effect of biomarkers on immune microenvironment, and therapeutic drugs which could affect biomarkers expressions were also predicted. Finally, we verified the expression of the genes by q-PCR.

Results

Totally 634 DEGs were yielded between PD and control samples, and MEgreen module had the highest correlation with PD, thus it was defined as key model. Four critical genes (AK3, RTN3, CYP4F2, and LEPR) were identified after performing LASSO and SVM-RFE on 18 DECGs. Through ROC analysis, AK3, RTN3, and LEPR were identified as biomarkers due to their excellent ability to distinguish PD from control samples. Besides, biomarkers were associated with Parkinson's disease and other functional pathways.

Conclusion

Through bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.

To Develop Biomarkers for Diabetic Nephropathy Based on Genes Related to Fibrosis and Propionate Metabolism and Their Functional Validation

Propionate metabolism is important in the development of diabetes, and fibrosis plays an important role in diabetic nephropathy (DN). However, there are no studies on biomarkers related to fibrosis and propionate metabolism in DN. Hence, the current research is aimed at evaluating biomarkers associated with fibrosis and propionate metabolism and to explore their effect on DN progression. The GSE96804 (DN : control = 41 : 20) and GSE104948 (DN : control = 7 : 18) DN-related datasets and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were acquired from the public database. First, DN differentially expressed genes (DN-DEGs) between the DN and control samples were sifted out via differential expression analysis. The PMRG scores of the DN samples were calculated based on PMRGs. The samples were divided into the high and low PMRG score groups according to the median scores. The PM-DEGs between the two groups were screened out. Second, the intersection of DN-DEGs, PM-DEGs, and FRGs was taken to yield intersected genes. Random forest (RF) and recursive feature elimination (RFE) analyses of the intersected genes were performed to sift out biomarkers. Then, single gene set enrichment analysis was conducted. Finally, immunoinfiltrative analysis was performed, and the transcription factor (TF)-microRNA (miRNA)-mRNA regulatory network and the drug-gene interaction network were constructed. There were 2633 DN-DEGs between the DN and control samples and 515 PM-DEGs between the high and low PMRG score groups. In total, 10 intersected genes were gained after taking the intersection of DN-DEGs, PM-DEGs, and FRGs. Seven biomarkers, namely, SLC37A4, ACOX2, GPD1, angiotensin-converting enzyme 2 (ACE2), SLC9A3, AGT, and PLG, were acquired via RF and RFE analyses, and they were found to be involved in various mechanisms such as glomerulus development, fatty acid metabolism, and peroxisome. The seven biomarkers were positively correlated with neutrophils. Moreover, 8 TFs, 60 miRNAs, and 7 mRNAs formed the TF-miRNA-mRNA regulatory network, including USF1-hsa-mir-1296-5p-AGT and HIF1A-hsa-mir-449a-5p-ACE2. The drug-gene network contained UROKINASE-PLG, ATENOLOL-AGT, and other interaction relationship pairs. Via bioinformatic analyses, the risk of fibrosis and propionate metabolism-related biomarkers in DN were explored, thereby providing novel ideas for research related to DN diagnosis and treatment.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach

Background

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC.

Method

This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis.

Results

Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways.

Conclusions

This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) stands as the most prevalent and treatment-resistant malignant tumour, characterized by a dismal prognosis. Croton acylation (CA) has recently gained attention as a critical factor in cancer pathogenesis. This study sought to rapidly identify prognostic features of HCC linked to CA. Differential analysis was conducted between tumour tissues and adjacent non-tumour tissues in the TCGA-LIHC and GSE76427 datasets to uncover differentially expressed genes (DEG1 and DEG2). The intersection of DEG1 and DEG2 highlighted DEGs with consistent expression patterns. Single-sample gene set enrichment analysis scores were calculated for 18 lysine crotonylation-related genes (LCRGs) identified in prior research, showing significant differences between tumour and normal groups. Subsequently, weighted gene co-expression network analysis was employed to identify key module genes correlated with the LCRG score. Candidate genes were identified by overlapping consistently expressed DEGs with key module genes. Prognostic features were identified, and risk scores were determined via regression analysis. Patients were categorized into risk groups based on the optimal cutoff value. Gene set enrichment analysis (GSEA) and immunoassays were also performed. The prognostic features were further validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 88 candidate genes were identified from 1179 consistently expressed DEGs and 4200 key module genes. Seven prognostic features were subsequently identified: TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21. GSEA revealed that DEGs between risk groups were primarily associated with chylomicron metabolism, among other pathways. Additionally, activated CD4+ T cells demonstrated the strongest positive correlation with risk scores, and most immune checkpoints showed significant differences between risk groups, with ASXL1 exhibiting the strongest correlation with risk scores. The Tumour Immune Dysfunction and Exclusion score was notably higher in the high-risk group. Moreover, in both the TCGA-LIHC and ICGC-LIRI-JP datasets, the expression of other prognostic features was elevated in tumour tissues, with the exception of CHST9. RT-qPCR confirmed the increased expression of TMCO3, RAP2A, ITGAV, ZFYVE26, and HMGN4. This study establishes a risk model for HCC based on seven crotonylation-associated prognostic features, offering a theoretical framework for the diagnosis and treatment of HCC.

Identifying and validating necroptosis-associated features to predict clinical outcome and immunotherapy response in patients with glioblastoma

BACKGROUND

Necroptosis is a type of programmed cell death involved in the pathogenesis of cancers. This work developed a prognostic glioblastoma (GBM) model based on necroptosis-related genes.

METHODS

RNA-Seq data were collected from the TCGA database. The "WGCNA" method was used to identify co-expression modules, based on which GO and KEGG analyses were conducted. A protein-protein interaction (PPI) network was compiled. The number of key prognostic genes was reduced applying COX regression and least absolute shrinkage and selection operator (LASSO) analysis to build a RiskScore model. Differences in immune microenvironments were assessed using CIBERSORT, ESTIMATE, MCP-count, and TIMER databases. The potential impact of key prognostic genes on GBM was validated by cellular experiments.

RESULTS

GBM patients in the higher necroptosis score group had higher immune scores and worse survival. The Brown module, which was closely related to the necroptosis score, was considered as a key gene module. Three key genes (GZMB, PLAUR, SOCS3) were obtained by performing regression analysis on the five clusters. The RiskScore model was significantly, positively, correlated with necroptosis score. Low-risk patients could benefit from immunotherapy, while high-risk patients may be more suitable to take multiple chemotherapy drugs. The nomogram showed strong performance in survival prediction. GZMB, PLAUR, and SOCS3 played key roles in GBM development. Among them, high-expressed GZMB was related to the invasive and migratory abilities of GBM cells.

CONCLUSIONS

A genetic signature associated with necroptosis was developed, and we constructed a RiskScore model to provide reference for predicting clinical outcomes and immunotherapy responses of patients with GBM.

Identification of Potential Key Genes for the Comorbidity of Myasthenia Gravis With Thymoma by Integrated Bioinformatics Analysis and Machine Learning

Background

Thymoma is a key risk factor for myasthenia gravis (MG). The purpose of our study was to investigate the potential key genes responsible for MG patients with thymoma.

Methods

We obtained MG and thymoma dataset from GEO database. Differentially expressed genes (DEGs) were determined and functional enrichment analyses were conducted by R packages. Weighted gene co-expression network analysis (WGCNA) was used to screen out the crucial module genes related to thymoma. Candidate genes were obtained by integrating DEGs of MG and module genes. Subsequently, we identified several candidate key genes by machine learning for diagnosing MG patients with thymoma. The nomogram and receiver operating characteristics (ROC) curves were applied to assess the diagnostic value of candidate key genes. Finally, we investigated the infiltration of immunocytes and analyzed the relationship among key genes and immune cells.

Results

We obtained 337 DEGs in MG dataset and 2150 DEGs in thymoma dataset. Biological function analyses indicated that DEGs of MG and thymoma were enriched in many common pathways. Black module (containing 207 genes) analyzed by WGCNA was considered as the most correlated with thymoma. Then, 12 candidate genes were identified by intersecting with MG DEGs and thymoma module genes as potential causes of thymoma-associated MG pathogenesis. Furthermore, five candidate key genes (JAM3, MS4A4A, MS4A6A, EGR1, and FOS) were screened out through integrating least absolute shrinkage and selection operator (LASSO) regression and Random forest (RF). The nomogram and ROC curves (area under the curve from 0.833 to 0.929) suggested all five candidate key genes had high diagnostic values. Finally, we found that five key genes and immune cell infiltrations presented varying degrees of correlation.

Conclusions

Our study identified five key potential pathogenic genes that predisposed thymoma to the development of MG, which provided potential diagnostic biomarkers and promising therapeutic targets for MG patients with thymoma.

Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma

INTRODUCTION

Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients.

METHODS

We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

RESULTS

A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line.

CONCLUSION

This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PURPOSE

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.

METHODS

The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.

RESULTS

At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).

CONCLUSION

The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Unraveling the causal links and novel molecular classification of Crohn's disease in breast Cancer: a two-sample mendelian randomization and transcriptome analysis with prognostic modeling

BACKGROUND

Crohn's disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversial.

METHODS

We used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes.

RESULTS

MR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. Using these genes, a new molecular classification of BC patients was proposed.

CONCLUSIONS

CD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.

From bioinformatics to clinical applications: a novel prognostic model of cuproptosis-related genes based on single-cell RNA sequencing data in hepatocellular carcinoma

OBJECTIVE AND METHODS

To ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database.

RESULTS

Thirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels.

CONCLUSION

A PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

Clinical Significance and Molecular Annotation for PD-L1 Negative Advanced Non-Small Cell Lung Cancer with Sensitivity to Responsive to Dual PD-1/CTLA-4 Blockade

Background

Immunotherapy has become the standard treatment for driving gene-negative advanced non-small cell lung cancer (NSCLC). However, compared to PD-L1-positive patients, the efficacy of Anti-PD-(L)1 monotherapy is suboptimal in PD-L1-negative advanced NSCLC. In this study, we aim to analyze the optimal immunotherapy approach for PD-L1-negative NSCLC patients and develop a new nomogram to enhance the clinical predictability of immunotherapy for NSCLC patients.

Methods

In this study, we retrieved clinical information and genomic data from cBioPortal for NSCLC patients undergoing immunotherapy. Cox regression analyses were utilized to screen the clinical information and genomic data that related to survival. The prognostic-relate genes function was studied by comprehensive bioinformatics analyses. The Kaplan-Meier plot method was employed for survival analysis.

Results

A total of 199 PD-L1-negative NSCLC patients were included in this study. Among them, 165 patients received Anti-PD-(L)1 monotherapy, while 34 patients received Anti-PD-(L)1+Anti-CTLA-4 combination therapy. The Anti-PD-(L)1+Anti-CTLA-4 combination therapy demonstrated significantly higher PFS compared to the Anti-PD-(L)1 monotherapy. The mutation status of KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 were found to correlate with PFS. Utilizing the clinicopathological parameters and genomic data of the patients, a novel nomogram was developed to predict the prognosis of Anti-PD-(L)1+Anti-CTLA-4 combination therapy.

Conclusion

Our study revealed that KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 mutation could serve as predictive biomarkers for patients with Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our systematic nomogram demonstrates significant potential in predicting the prognosis for NSCLC patients with responsive to dual PD-1/CTLA-4 blockade.

Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus

Background

Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear.

Methods

We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group.

Conclusion

Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.

EXPLORING THE PROGNOSTIC NECROPTOSIS-RELATED GENES AND UNDERLYING MECHANISM IN SEPSIS USING BIOINFORMATICS

ABSTRACT

Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers ( ATRX , TSC1 , CD40 , BACH2 , BCL2 , and LEF1 ) with survival and diagnostic significance through Kaplan-Meier (KM) and receiver operating characteristic (ROC) analyses. Afterward, the ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. Moreover, we examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. The molecular regulatory network displayed that hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified paclitaxel, docetaxel, and rasagiline as potential BCL2-targeting sepsis treatments. Finally, real-time quantitative PCR confirmed ATRX, TSC1, and LEF1 downregulation in sepsis samples, contrasting CD40's increased expression in CTL samples. In conclusion, ATRX , TSC1 , CD40 , BACH2 , BCL2 , and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

Construction of a mitophagy-related prognostic signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma

Background

Mitophagy is the selective degradation of mitochondria by autophagy. It becomes increasingly clear that mitophagy pathways are important for cancer cells to adapt to their high-energy needs. However, which genes associated with mitophagy could be used to prognosis cancer is unknown.

Methods

We created a clinical prognostic model using mitophagy-related genes (MRGs) in lung adenocarcinoma (LUAD) patients for the first time, and we employed bioinformatics methods to search for biomarkers that affect the progression and prognosis of LUAD. Transcriptome data for LUAD were obtained from The Cancer Genome Atlas (TCGA) database, and additional expression data from LUAD patients were sourced from the Gene Expression Omnibus (GEO) database. Furthermore, 25 complete MRGs were identified based on annotations from the MSigDB database.

Results

A comparison of the mitophagy scores between the groups with high and low scores was done using receiver operating characteristic (ROC) curves, which also revealed the differential gene expression patterns between the two groups. Using Kaplan-Meier analysis, two prognostic MRGs from the groups with high and low mitophagy scores were identified: TOMM40 and VDAC1. Using univariate and multivariate Cox regression, the relationship between the expression levels of these two genes and prognostic clinical features of LUAD was examined further.The prognosis of LUAD patients was shown to be significantly correlated (P < 0.05) with the expression levels of these two genes.

Conclusions

Our prognostic model would improve the prognosis of LUAD and guide clinical treatments.

A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer

Background

As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA.

Methods

The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The "GSVA" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The "limma" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay.

Results

The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells.

Conclusion

This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.

Constructing a prognostic model for colon cancer: insights from immunity-related genes

BACKGROUND

Colon cancer (CC) is a malignancy associated with significant morbidity and mortality within the gastrointestinal tract. Recurrence and metastasis are the main factors affecting the prognosis of CC patients undergoing radical surgery; consequently, we attempted to determine the impact of immunity-related genes.

RESULT

We constructed a CC risk model based on ZG16, MPC1, RBM47, SMOX, CPM and DNASE1L3. Consistently, we found that a significant association was found between the expression of most characteristic genes and tumor mutation burden (TMB), microsatellite instability (MSI) and neoantigen (NEO). Additionally, a notable decrease in RBM47 expression was observed in CC tissues compared with that in normal tissues. Moreover, RBM47 expression was correlated with clinicopathological characteristics and improved disease-free survival (DFS) and overall survival (OS) among patients with CC. Lastly, immunohistochemistry and co-immunofluorescence staining revealed a clear positive correlation between RBM47 and CXCL13 in mature tertiary lymphoid structures (TLS) region.

CONCLUSION

We conclude that RBM47 was identified as a prognostic-related gene, which was of great significance to the prognosis evaluation of patients with CC and was correlated with CXCL13 in the TLS region.

Bioinformatic analysis and experimental validation of six cuproptosis-associated genes as a prognostic signature of breast cancer

Background

Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA.

Methods

Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the 'limma' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines.

Results

Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A.

Conclusion

Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA.

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.

CCNA2 and KIF23 are molecular targets for the prognosis of adenoid cystic carcinoma

OBJECTIVE

Adenoid cystic carcinoma (ACC) is a tumor type derived from glands. However, relationship between CCNA2 and KIF23, and adenoid cystic carcinoma remains unclear.

METHODS

GSE36820 and GSE88804 profiles for ACC were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified, and Weighted Gene Co-expression Network Analysis (WGCNA) was conducted. Subsequently, the construction and analysis of protein-protein interaction (PPI) network, functional enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed. A gene expression heat map was generated to visually depict the expression difference of core genes between adenoid cystic carcinoma and normal samples. TargetScan was employed to identify miRNAs that regulated central DEGs. Western blotting (WB) was conducted for cell verification.

RESULTS

A total of 885 DEGs were identified. GO and KEGG analyses revealed their main enrichment in responses to chemical stimuli, cell proliferation, tissue development, and regulation of cell proliferation. The GO and KEGG results indicated significant enrichment in aldosterone-regulated sodium reabsorption, the cell cycle, and the PPAR signaling pathway. Notably, core genes (CCNA2 and KIF23) were found to be highly expressed in Adenoid Cystic Carcinoma samples and expressed at low levels in normal samples. WB validated the overexpression of CCNA2 and KIF23 in the Adenoid Cystic Carcinoma group, confirming the protein-level changes associated with cell cycle, metastasis, apoptosis, and inflammatory factors in Adenoid Cystic Carcinoma groups with gene overexpression and knockout.

CONCLUSIONS

CCNA2 and KIF23 exhibit high expression levels in ACC, suggesting their potential role as molecular targets for this malignancy.

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.