Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation

MMPs-related risk model identification and SAA1 promotes clear cell renal cell carcinoma migration via ERK-AP1-MMPs axis

Matrix Metalloproteinases (MMPs) have been demonstrated to be essential in facilitating the migration and metastasis of clear cell renal cell carcinoma (ccRCC). However, the ability of the MMP family to predict clinical outcomes and guide optimal therapeutic strategies for ccRCC patients remains incompletely understood. In this investigation, we initially conducted a thorough examination of the MMP family in pan-cancer. Notably, MMPs exhibited distinctive significance in ccRCC. Following this, we undertook an extensive analysis to evaluate the clinical value of MMPs and potential mechanisms by which MMPs contribute to the progression of ccRCC. A novel stratification method and prognostic model were developed based on MMPs in order to enhance the accuracy of prognosis prediction for ccRCC patients and facilitate personalized treatment. By conducting multi-omics analysis and transcriptional regulation analysis, it was hypothesized that SAA1 plays a crucial role in promoting ccRCC migration through MMPs. Subsequently, in vitro experiments confirmed that SAA1 regulates ccRCC cell migration via the ERK-AP1-MMPs axis. In conclusion, our study has explored the potential value of the MMP family as prognostic markers for ccRCC and as guides for medication regimens. Additionally, we have identified SAA1 as a crucial factor in the migration of ccRCC.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Identification of molecular characteristics of FUT8 and alteration of core fucosylation in kidney renal clear cell cancer

BACKGROUND

Kidney renal clear cell cancer (KIRC) is a type of urological cancer that occurs worldwide. Core fucosylation (CF), as the most common post-translational modification, is involved in the tumorigenesis.

METHODS

The alterations of CF-related genes were summarized in pan-cancer. The "ConsensusClusterPlus" package was utilized to identify two CF-related KIRC subtypes. The "ssgsea" function was chosen to estimate the CF score, signaling pathways and cell deaths. Multiple algorithms were applied to assess immune responses. The "oncoPredict" was utilized to estimate the drug sensitivity. The IHC and subgroup analysis was performed to reveal the molecular features of FUT8. Single-cell RNA sequencing (scRNA-seq) data were scrutinized to evaluate the CF state.

RESULTS

In pan-cancer, there was a noticeable alteration in the expression of CF-related genes. In KIRC, two CF-related subtypes (i.e., C1, C2) were obtained. In comparison to C2, C1 exhibited a higher CF score and correlated with poorer overall survival. Additionally, the TME of C2 demonstrated increased activity in neutrophils, macrophages, myeloid dendritic cells, and B cells, alongside a higher presence of silent mast cells, NK cells, and endothelial cells. Compared to normal samples, higher expression of FUT8 is observed in KIRC. The mutation of SETD2 was more frequent in low-FUT8 samples while the mutation of DNAH9 was more frequent in high-FUT8 samples. scRNA-seq analyses revealed that the CF score was predominantly higher in endothelial cells and fibroblast cells.

CONCLUSIONS

Two CF-related subtypes with distinct prognosis and TME were identified in KIRC. FUT8 exhibited elevated expression in KIRC samples.

Effects and mechanisms of Porphyromonas gingivalis outer membrane vesicles induced cardiovascular injury

BACKGROUND

The outer membrane vesicles (OMVs) derived from Porphyromonas gingivalis (P. gingivalis) have long been acknowledged for their crucial role in the initiation of periodontitis. However, the implications of P. gingivalis OMVs in the context of cardiovascular disease (CVD) remain incompletely understood. This study aimed to clarify both the impact and the underlying mechanisms through which P. gingivalis OMVs contribute to the propagation of distal cardiovascular inflammation and trauma.

METHODS

In this study, various concentrations (0, 1.25, 2.5, and 4.5 µg/µL) of P. gingivalis OMVs were microinjected into the common cardinal vein of zebrafish larvae at 48 h post-fertilization (hpf) to assess changes in cardiovascular injury and inflammatory response. Zebrafish larvae from both the PBS and the 2.5 µg/µL injection cohorts were harvested at 30 h post-injection (hpi) for transcriptional analysis. Real-time quantitative PCR (RT-qPCR) was employed to evaluate relative gene expression.

RESULTS

These findings demonstrated that P. gingivalis OMVs induced pericardial enlargement in zebrafish larvae, caused vascular damage, increased neutrophil counts, and activated inflammatory pathways. Transcriptomic analysis further revealed the involvement of the immune response and the extracellular matrix (ECM)-receptor interaction signaling pathway in this process.

CONCLUSION

This study illuminated potential mechanisms through which P. gingivalis OMVs contribute to CVD. It accentuated their involvement in distal cardiovascular inflammation and emphasizes the need for further research to comprehensively grasp the connection between periodontitis and CVD.

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

Oxidative stress and autophagy-mediated immune patterns and tumor microenvironment infiltration characterization in gastric cancer

Recent years have seen a sharp rise in the amount of research on the connection between oxidative stress, autophagy, and cancer cells. However, the significant functions of oxidative stress and autophagy-related genes (OARGs) in gastric cancer (GC) are yet to be investigated integrally. Therefore, it will be a new and promising concept to search for novel OARG-related biomarkers to predict the prognosis and treatment response of GC. First, we assessed changes in prognosis and tumor microenvironment (TME) characteristics across the various oxidative stress and autophagy-related modification patterns based on a detailed analysis of 17 OARGs with prognostic significance of 808 GC samples. We identified three distinct OARG alteration patterns which displayed unique biological characteristics and immune cell infiltration features. Using principal component analysis methods, the OARGscore was developed to evaluate the OARG modification patterns of certain tumors. The negative connection between OARGscore and immune cells was statistically significant. Increased survival, a higher incidence of mutations, and a better response to immunotherapy were all predicted to be related to patients' high-OARGscore. In addition, the candidate chemotherapeutic drugs were predicted using the oncoPredict program. The low-OARGscore group was predicted to benefit more from Ribociclib, Alisertib, Niraparib, Epirubicin, Olaparib, and Axitinib, while patients in the high-OARGscore group were predicted to benefit more from Afatinib, Oxaliplatin, Paclitaxel, 5-Fluorouracil, Dabrafenib and Lapatinib. Our findings offer a specific method for predicting a patient's prognosis and susceptibility to immunotherapy, as well as a promising insight of oxidative stress and autophagy in GC.

Identification and characterization of interferon-γ signaling-based personalized heterogeneity and therapeutic strategies in patients with pancreatic cancer

Background

Interferon-γ (IFN-γ) is a key cytokine with diverse biological functions, including antiviral defense, antitumor activity, immune regulation, and modulation of cellular processes. Nonetheless, its role in pancreatic cancer (PC) therapy remains debated. Therefore, it is worthwhile to explore the role of Interferon-γ related genes (IFN-γGs) in the progression of PC development.

Methodology

Transcriptomic data from 930 PC were sourced from TCGA, GEO, ICGC, and ArrayExpress, and 93 IFN-γGs were obtained from the MSigDB. We researched the characteristics of IFN-γGs in pan-cancer. Subsequently, the cohort of 930 PC was stratified into two distinct subgroups using the NMF algorithm. We then examined disparities in the activation of cancer-associated pathways within these subpopulations through GSVA analysis. We scrutinized immune infiltration in both subsets and probed classical molecular target drug sensitivity variations. Finally, we devised and validated a novel IFN-γ related prediction model using LASSO and Cox regression analyses. Furthermore, we conducted RT-qPCR and immunohistochemistry assays to validate the expression of seven target genes included in the prediction model.

Results

We demonstrated the CNV, SNV, methylation, expression levels, and prognostic characteristics of IFN-γGs in pan-cancers. Notably, Cluster 2 demonstrated superior prognostic outcomes and heightened immune cell infiltration compared to Clusters 1. We also assessed the IC50 values of classical molecular targeted drugs to establish links between IFN-γGs expression levels and drug responsiveness. Additionally, by applying our prediction model, we segregated PC patients into high-risk and low-risk groups, identifying potential benefits of cisplatin, docetaxel, pazopanib, midostaurin, epothilone.B, thapsigargin, bryostatin.1, and AICAR for high-risk PC patients, and metformin, roscovitine, salubrinal, and cyclopamine for those in the low-risk group. The expression levels of these model genes were further verified through HPA website data and qRT-PCR assays in PC cell lines and tissues.

Conclusion

This study unveils IFN-γGs related molecular subsets in pancreatic cancer for the first time, shedding light on the pivotal role of IFN-γGs in the progression of PC. Furthermore, we establish an IFN-γGs related prognostic model for predicting the survival of PC, offering a theoretical foundation for exploring the precise mechanisms of IFN-γGs in PC.

Identification of the methotrexate resistance-related diagnostic markers in osteosarcoma via adaptive total variation netNMF and multi-omics datasets

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.

Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients

BACKGROUND

Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction.

METHODS

Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments.

RESULTS

Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation.

CONCLUSION

This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients.

Prognostic value of lactate dehydrogenase in patients with uveal melanoma treated with immune checkpoint inhibition

OBJECTIVE

We performed the meta-analysis to explore the predictive value of lactate dehydrogenase (LDH) levels in uveal melanoma (UM) patients receiving immune checkpoint inhibitors (ICIs).

METHODS

Eligible articles were obtained through EMBASE, PubMed, Google Scholar, and the Cochrane Library, until March 23, 2023. The clinical outcomes evaluated in this study encompassed overall survival (OS) and progression-free survival (PFS).

RESULTS

This meta-analysis comprised eight studies with a combined total of 383 patients. The results showed that patients with high LDH levels had noticeably worse OS (HR: 3.445, 95% CI: 2.504-4.740, p < 0.001) and PFS (HR: 1.720, 95% CI: 1.429-2.070, p < 0.001). Subgroup analysis confirmed that the upper limit of normal was the ideal cut-off value for LDH. In multivariate analysis, we also found that high LDH levels significantly predicted shorter OS (HR: 3.405, 95% CI: 1.827-6.348, p < 0.001) and PFS (HR: 2.519, 95% CI: 1.557-4.076, p < 0.001) in UM patients. The sensitivity analysis and publication bias test supported the reliability of our results.

CONCLUSIONS

In UM patients treated with ICIs, the LDH levels were reliable indicators of prognosis.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer

Oxidative stress (OS), which impacts lipid metabolic reprogramming, can affect the biological activities of cancer cells. How oxidative stress and phospholipid metabolism (OSPM) influence the prognosis of pancreatic cancer (PC) needs to be elucidated. The metabolic data of 35 pancreatic tumor samples, 34 para-carcinoma samples, and 31 normal pancreatic tissues were obtained from the previously published literature. Pan-cancer samples were obtained from The Cancer Genome Atlas (TCGA). And the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), ArrayExpress, and the Genotype-Tissue Expression (GTEx) databases were searched for more PC and normal pancreatic samples. The metabolites in PC were compared with normal and para-carcinoma tissues. The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. Based on this signature, PC samples were divided into high- and low-risk subgroups. The dysregulations of the tumor immune microenvironment were further investigated. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to investigate the expression of genes in the signature in PC and normal tissues. The protein levels of these genes were further demonstrated. In PC, metabolomic studies revealed the alteration of PM, while transcriptomic studies showed different expressions of OSPM-related genes. Then 930 PC samples were divided into three subtypes with different prognoses, and an OSPM-related signature including eight OSPM-related genes (i.e., SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, ECT2, SLC22A3, and FGD6) was developed. High- and low-risk subgroups divided by the signature showed different prognoses, expression levels of immune checkpoint genes, immune cell infiltration, and tumor microenvironment. The risk score was negatively correlated with the proportion of TIL, pDC, Mast cell, and T cell co-stimulation. The expression levels of genes in the signature were verified in PC and normal samples. The protein levels of SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, and SLC22A3 showed up-regulation in PC samples compared with normal tissues. After integrating metabolomics and transcriptomics data, the alterations in OSPM in PC were investigated, and an OSPM-related signature was developed, which was helpful for the prognostic assessment and individualized treatment for PC.

Identification and immunological characterization of lipid metabolism-related molecular clusters in nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is now the major contributor to chronic liver disease. Disorders of lipid metabolism are a major element in the emergence of NAFLD. This research intended to explore lipid metabolism-related clusters in NAFLD and establish a prediction biomarker.

METHODS

The expression mode of lipid metabolism-related genes (LMRGs) and immune characteristics in NAFLD were examined. The "ConsensusClusterPlus" package was utilized to investigate the lipid metabolism-related subgroup. The WGCNA was utilized to determine hub genes and perform functional enrichment analysis. After that, a model was constructed by machine learning techniques. To validate the predictive effectiveness, receiver operating characteristic curves, nomograms, decision curve analysis (DCA), and test sets were used. Lastly, gene set variation analysis (GSVA) was utilized to investigate the biological role of biomarkers in NAFLD.

RESULTS

Dysregulated LMRGs and immunological responses were identified between NAFLD and normal samples. Two LMRG-related clusters were identified in NAFLD. Immune infiltration analysis revealed that C2 had much more immune infiltration. GSVA also showed that these two subtypes have distinctly different biological features. Thirty cluster-specific genes were identified by two WGCNAs. Functional enrichment analysis indicated that cluster-specific genes are primarily engaged in adipogenesis, signalling by interleukins, and the JAK-STAT signalling pathway. Comparing several models, the random forest model exhibited good discrimination performance. Importantly, the final five-gene random forest model showed excellent predictive power in two test sets. In addition, the nomogram and DCA confirmed the precision of the model for NAFLD prediction. GSVA revealed that model genes were down-regulated in several immune and inflammatory-related routes. This suggests that these genes may inhibit the progression of NAFLD by inhibiting these pathways.

CONCLUSIONS

This research thoroughly emphasized the complex relationship between LMRGs and NAFLD and established a five-gene biomarker to evaluate the risk of the lipid metabolism phenotype and the pathologic results of NAFLD.

Multi-omics analysis of LAMB3 as a potential immunological and biomarker in pan-cancer

Laminin Subunit Beta 3 (LAMB3) is a transcription factor and participates in the coding of laminin. It plays an important role in cell proliferation, adhesion, and transfer by regulating various target genes and signaling pathways. However, the role of LAMB3 in human pan-cancer immunology and prognosis is still poorly understood. The TCGA, GTEx, CCLE, and HPA databases were utilized for the analysis of LAMB mRNA and protein expression. The expression of LAMB3 in various immune and molecular subtypes of human cancer was examined using the TISIDB database. The prognostic significance of LAMB3 in various cancers and clinical subtypes was investigated using Kaplan-Meier and Cox regression analysis. The relationship between LAMB3 expression, various immune cell infiltration, immune checkpoints, tumor mutational load, microsatellite instability, and DNA methylation was examined using the TCGA database. Clinical samples of four lung cancer cell lines and eight lung cancer cases were collected to confirm the expression of mRNA in lung cancer. In 17 cancers, the mRNA for LAMB3 is expressed differently and has good diagnostic and prognostic value in 22 cancers. Cox regression and Nomogram analysis show that LAMB3 is an independent risk factor for 15 cancers. LAMB3 is implicated in a variety of tumorigenesis and immune-related signaling pathways, according to GO, KEGG, and GSEA results. LAMB3 expression was associated with TMB in 33 cancer types and MSI in 32 cancer types, while in lung cancer LAMB3 expression was strongly associated with immune cell infiltration and negatively correlated with all seven methylated CpG islands. Cellular experiments demonstrated that LAMB3 promotes malignant behavior of tumor cells. Preliminary mechanistic exploration revealed its close association with PD-L1, CTLA4, cell stemness gene CD133 and β-catenin-related signaling pathways. Based on these findings, it appears that LAMB3 could be a potential therapeutic target for immunotherapy and tumor prognosis. Our findings reveal an important role for LAMB3 in different cancer types.

PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

Background

PANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis.

Methods

We downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model.

Results

We obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8⁺ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient's survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues.

Conclusion

We first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis.

Identification of lipid metabolism-related biomarkers for diagnosis and molecular classification of atherosclerosis

BACKGROUND

Atherosclerosis is now the main cause of cardiac-cerebral vascular diseases around the world. Disturbances in lipid metabolism have an essential role in the development and progression of atherosclerosis. Thus, we aimed to investigate lipid metabolism-related molecular clusters and develop a diagnostic model for atherosclerosis.

METHODS

First, we used the GSE100927 and GSE43292 datasets to screen differentially expressed lipid metabolism-related genes (LMRGs). Subsequent enrichment analysis of these key genes was performed using the Metascape database. Using 101 atherosclerosis samples, we investigated the LMRG-based molecular clusters and the corresponding immune cell infiltration. After that, a diagnostic model for atherosclerosis was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a series of bioinformatics techniques, including CIBERSORT, gene set variation analysis, and single-cell data analysis, were used to analyze the potential mechanisms of the model genes in atherosclerosis.

RESULTS

A total of 29 LMRGs were found to be differentially expressed between atherosclerosis and normal samples. Functional and DisGeNET enrichment analyses indicated that 29 LMRGs are primarily engaged in cholesterol and lipid metabolism, the PPAR signaling pathway, and regulation of the inflammatory response and are also closely associated with atherosclerotic lesions. Two LMRG-related molecular clusters with significant biological functional differences are defined in atherosclerosis. A three-gene diagnostic model containing ADCY7, SCD, and CD36 was subsequently constructed. Receiver operating characteristic curves, decision curves, and an external validation dataset showed that our model exhibits good predictive performance. In addition, three model genes were found to be closely associated with immune cell infiltration, especially macrophage infiltration.

CONCLUSIONS

Our study comprehensively highlighted the intricate association between lipid metabolism and atherosclerosis and created a three-gene model for future clinical diagnosis.

Determination and characterization of molecular heterogeneity and precision medicine strategies of patients with pancreatic cancer and pancreatic neuroendocrine tumor based on oxidative stress and mitochondrial dysfunction-related genes

Background

Mitochondria are significant both for cellular energy production and reactive oxygen/nitrogen species formation. However, the significant functions of mitochondrial genes related to oxidative stress (MTGs-OS) in pancreatic cancer (PC) and pancreatic neuroendocrine tumor (PNET) are yet to be investigated integrally. Therefore, in pan-cancer, particularly PC and PNET, a thorough assessment of the MTGs-OS is required.

Methods

Expression patterns, prognostic significance, mutation data, methylation rates, and pathway-regulation interactions were studied to comprehensively elucidate the involvement of MTGs-OS in pan-cancer. Next, we separated the 930 PC and 226 PNET patients into 3 clusters according to MTGs-OS expression and MTGs-OS scores. LASSO regression analysis was utilized to construct a novel prognostic model for PC. qRT-PCR(Quantitative real-time PCR) experiments were performed to verify the expression levels of model genes.

Results

The subtype associated with the poorest prognosis and lowerest MTGs-OS scores was Cluster 3, which could demonstrate the vital function of MTGs-OS for the pathophysiological processes of PC. The three clusters displayed distinct variations in the expression of conventional cancer-associated genes and the infiltration of immune cells. Similar molecular heterogeneity was observed in patients with PNET. PNET patients with S1 and S2 subtypes also showed distinct MTGs-OS scores. Given the important function of MTGs-OS in PC, a novel and robust MTGs-related prognostic signature (MTGs-RPS) was established and identified for predicting clinical outcomes for PC accurately. Patients with PC were separated into the training, internal validation, and external validation datasets at random; the expression profile of MTGs-OS was used to classify patients into high-risk (poor prognosis) or low-risk (good prognosis) categories. The variations in the tumor immune microenvironment may account for the better prognoses observed in high-risk individuals relative to low-risk ones.

Conclusions

Overall, our study for the first time identified and validated eleven MTGs-OS remarkably linked to the progression of PC and PNET, and elaborated the biological function and prognostic value of MTGs-OS. Most importantly, we established a novel protocol for the prognostic evaluation and individualized treatment for patients with PC.

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

The tumour microenvironment (TME) is vital to tumour development and influences the immunotherapy response. Abnormal nucleotide metabolism (NM) not only promotes tumour cell proliferation but also inhibits immune responses in the TME. Therefore, this study aimed to determine whether the combined signatures of NM and the TME could better predict the prognosis and treatment response in gastric cancer (GC). 97 NM-related genes and 22 TME cells were evaluated in TCGA-STAD samples, and predictive NM and TME characteristics were determined. Subsequent correlation analysis and single-cell data analysis illustrated a link between NM scores and TME cells. Thereafter, NM and TME characteristics were combined to construct an NM-TME classifier. Patients in the NMlow/TMEhigh group exhibited better clinical outcomes and treatment responses, which could be attributed to the differences in immune cell infiltration, immune checkpoint genes, tumour somatic mutations, immunophenoscore, immunotherapy response rate and proteomap. Additionally, the NMhigh/TMElow group benefited more from Imatinib, Midostaurin and Linsitinib, while patients in the NMlow/TMEhigh group benefited more from Paclitaxel, Methotrexate and Camptothecin. Finally, a highly reliable nomogram was developed. In conclusion, the NM-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic responses, which may offer novel strategies for strategizing patients with optimal therapies.

Identification of the cell cycle characteristics of non-small cell lung cancer and its relationship with tumor immune microenvironment, cell death pathways, and metabolic reprogramming

Background

The genes related to the cell cycle progression could be considered the key factors in human cancers. However, the genes involved in cell cycle regulation in non-small cell lung cancer (NSCLC) have not yet been reported. Therefore, it is necessary to evaluate the genes related to the cell cycle in all types of cancers, especially NSCLC.

Methods

This study constituted the first pan-cancer landscape of cell cycle signaling. Cluster analysis based on cell cycle signaling was conducted to identify the potential molecular heterogeneity of NSCLC. Further, the discrepancies in the tumor immune microenvironment, metabolic remodeling, and cell death among the three clusters were investigated. Immunohistochemistry was performed to validate the protein levels of the ZWINT gene and examine its relationship with the clinical characteristics. Bioinformatics analyses and experimental validation of the ZWINT gene were also conducted.

Results

First, pan-cancer analysis provided an overview of cell cycle signaling and highlighted its crucial role in cancer. A majority of cell cycle regulators play risk roles in lung adenocarcinoma (LUAD); however, some cell cycle genes play protective roles in lung squamous cell carcinoma (LUSC). Cluster analysis revealed three potential subtypes for patients with NSCLC. LUAD patients with high cell cycle activities were associated with worse prognosis; while, LUSC patients with high cell cycle activities were associated with a longer survival time. Moreover, the above three subtypes of NSCLC exhibited distinct immune microenvironments, metabolic remodeling, and cell death pathways. ZWINT, a member of the cell signaling pathway, was observed to be significantly associated with the prognosis of LUAD patients. A series of experiments verified the higher expression levels of ZWINT in NSCLC compared to those in paracancerous tissues. The activation of epithelial-mesenchymal transition (EMT) induced by ZWINT might be responsible for tumor progression.

Conclusion

This study revealed the regulatory function of the cell cycle genes in NSCLC, and the molecular classification based on cell cycle-associated genes could evaluate the different prognoses of patients with NSCLC. ZWINT expression was found to be significantly upregulated in NSCLC tissues, which might promote tumor progression via activation of the EMT pathway.

The integrated single-cell analysis developed a lactate metabolism-driven signature to improve outcomes and immunotherapy in lung adenocarcinoma

Background

It has been suggested that lactate metabolism (LM) is crucial for the development of cancer. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients.

Methods

The most significant genes for LM were identified through the use of the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis. To build risk models with superior predictive performance, cox- and lasso-regression were utilized, and these models were validated on multiple external independent datasets. We then explored the differences in the tumor microenvironment (TME), immunotherapy, mutation landscape, and enriched pathways between different risk groups. Finally, cell experiments were conducted to verify the impact of AHSA1 in LUAD.

Results

A total of 590 genes that regulate LM were identified for subsequent analysis. Using cox- and lasso-regression, we constructed a 5-gene signature that can predict the prognosis of patients with LUAD. Notably, we observed differences in TME, immune cell infiltration levels, immune checkpoint levels, and mutation landscapes between different risk groups, which could have important implications for the clinical treatment of LUAD patients.

Conclusion

Based on LMRGs, we constructed a prognostic model that can predict the efficacy of immunotherapy and provide a new direction for treating LUAD.

A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma

Background

The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear.

Methods

The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines.

Results

Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed.

Conclusion

The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

Identification and validation of neurotrophic factor-related gene signatures in glioblastoma and Parkinson's disease

Background

Glioblastoma multiforme (GBM) is the most common cancer of the central nervous system, while Parkinson's disease (PD) is a degenerative neurological condition frequently affecting the elderly. Neurotrophic factors are key factors associated with the progression of degenerative neuropathies and gliomas.

Methods

The 2601 neurotrophic factor-related genes (NFRGs) available in the Genecards portal were analyzed and 12 NFRGs with potential roles in the pathogenesis of Parkinson's disease and the prognosis of GBM were identified. LASSO regression and random forest algorithms were then used to screen the key NFRGs. The correlation of the key NFRGs with immune pathways was verified using GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was constructed using LASSO (Least absolute shrinkage and selection operator) and multivariate Cox risk regression based on the expression of the 12 NFRGs in the GBM cohort from The Cancer Genome Atlas (TCGA) database. We also investigated differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between risk groups. Finally, the accuracy of the model genes was validated using multi-omics mutation analysis, single-cell sequencing, QT-PCR, and HPA.

Results

We found that 4 NFRGs were more reliable for the diagnosis of Parkinson's disease through the use of machine learning techniques. These results were validated using two external cohorts. We also identified 7 NFRGs that were highly associated with the prognosis and diagnosis of GBM. Patients in the low-risk group had a greater overall survival (OS) than those in the high-risk group. The nomogram generated based on clinical characteristics and risk scores showed strong prognostic prediction ability. The NFRG signature was an independent prognostic predictor for GBM. The low-risk group was more likely to benefit from immunotherapy based on the degree of immune cell infiltration, expression of immune checkpoints (ICs), and predicted response to immunotherapy. In the end, 2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of Parkinson's disease and the outcome of GBM.

Conclusions

Our study revealed that 4 NFRGs are involved in the progression of PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and help clinicians to classify the GBM patients into high and low risk groups. EN1, and LOXL1 can be used as therapeutic targets for personalized immunotherapy for patients with PD and GBM.

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Background

Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.

Methods

In this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.

Results

4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.

Conclusions

4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

Establishment of a cell senescence related prognostic model for predicting prognosis in glioblastoma

Background: Glioblastoma (GBM) is highly malignant and has a worse prognosis with age, and next-generation sequencing (NGS) provides us with a huge amount of information about GBM. Materials and Methods: Through the enrichment scores of cell senescence-related pathways, we constructed a consensus matrix and mined molecular subtypes and explored the differences in pathological, immune/pathway and prognostic. Also we identified key genes related to cell senescence characteristics using least absolute shrinkage and selection operator (Lasso) regression and univariate COX regression analysis models. The use of risk factor formats to construct clinical prognostic models also explored the differences in immunotherapy/chemotherapy within the senescence-related signatures score (SRS.score) subgroups. Decision trees built with machine learning to identify the main factors affecting prognosis have further improved the prognosis model and survival prediction. Results: We obtained seven prognostic-related pathways related to cell senescence. We constructed four different molecular subtypes and found patients with subtype C1 had the worst prognosis. C4 had the highest proportion of patients with IDH mutations. 1005 differentially expressed genes (DEGs) were analyzed, and finally 194 Risk genes and 38 Protective genes were obtained. Eight key genes responsible for cell senescence were finally identified. The clinical prognosis model was established based on SRS.score, and the prognosis of patients with high SRS.score was worse. SRS.score and age were the vital risk factors for GBM patients through decision tree model mining. Conclusion: We constructed a clinical prognosis model that could provide high prediction accuracy and survival prediction ability for adjuvant treatment of patients with GBM.