Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes: a bioinformatics analysis

AIM

To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression.

METHODS

Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool.

RESULTS

Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance.

CONCLUSION

PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Higher genetically predicted triglyceride level increases the bladder cancer risk independent of LDL and HDL levels

The causal relationship between lipid levels and bladder cancer is still inconclusive currently. We aimed to reveal the causal relationship between triglycerides, HDL, and LDL and the risk of bladder cancer by univariable and multivariable Mendelian randomization (MR) analysis. The single nucleotide polymorphisms (SNPs) of exposure (triglycerides: 441,016 samples; HDL: 403,943 samples; LDL: 440,546 samples) were obtained from UK Biobank. The Genetic variation related to bladder cancer included 1554 cases and 359,640 controls. Univariable and multivariable MR methods were conducted with subsequent analysis, and smoking was regarded as a confounder. The inverse-variance weighted (IVW), MR-Egger, weighted-median method, Cochran's Q test, and MR-PRESSO were considered the main MR analysis and sensitivity analysis methods. Univariable MR analysis results suggested the triglycerides level (P = 0.011, OR = 1.001, 95% CI = 1.000-1.002) was causally associated with increased risk of bladder cancer. Multivariable MR results indicated that higher triglyceride levels could still increase the risk of bladder cancer after adjusting the effects of HDL, LDL, and smoking (P = 0.042, OR = 1.001, 95% CI = 1.000-1.002). Our findings supported that triglyceride level is causally associated with an increased risk of bladder cancer independent of LDL and HDL at the genetic level. Timely attention to changes in blood lipid levels might reduce the risk of bladder cancer.

Harnessing lipid metabolism modulation for improved immunotherapy outcomes in lung adenocarcinoma

BACKGROUND

While anti-programmed cell death protein-1 (PD-1) monotherapy has shown effectiveness in treating lung cancer, its response rate is limited to approximately 20%. Recent research suggests that abnormal lipid metabolism in patients with lung adenocarcinoma may hinder the efficacy of anti-PD-1 monotherapy.

METHODS

Here, we delved into the patterns of lipid metabolism in patients with The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and their correlation with the immune microenvironment's cellular infiltration characteristics of the tumor. Furthermore, the lipid metabolism score (LMS) system was constructed, and based on the LMS system, we further performed screening for potential agents targeting lipid metabolism. The mechanism of MK1775 was further validated using RNA sequencing, co-culture technology, and in vivo experiments.

RESULTS

We developed an LSM system and identified a potential sensitizing agent, MK1775, which targets lipid metabolism and enhances the effects of anti-PD-1 treatment. Our results demonstrate that MK1775 inhibits tumor progression by influencing lipid crosstalk between tumor cells and tumor-associated macrophages and CD8+T cells, thereby increasing the effectiveness of anti-PD-1 treatment. Further, we found that MK1775 inhibited the phosphatidylinositol 3-kinase(PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway, which on one hand downregulated FASN-mediated synthesis of fatty acids (FAs) to inhibit fatty acid oxidation of tumor-associated macrophages, and on the other hand, promoted IRF-mediated secretion of CXCL10 and CXCL11 to facilitate the infiltration of CD8+ T cells.

CONCLUSIONS

These findings emphasize the important role of lipid metabolism in shaping the complex tumor microenvironment. By manipulating the intricate intricacies of lipid metabolism within the tumor microenvironment, we can uncover and develop promising strategies to sensitize immunotherapy, potentially revolutionizing cancer treatment approaches.

ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BACKGROUND

Osteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.

METHODS

We obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.

RESULTS

We identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.

CONCLUSION

4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Identification of VRK1 as a Novel Potential Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

Research has indicated that VRK1 is essential for the tumor cell cycle. However, its prognostic and immunotherapeutic predictive significance has not been documented in hepatocellular carcinoma (HCC).

Methods

The TCGA, ICGC, and GSE14520 datasets were used to investigate VRK1 expression and its predictive significance of survival outcomes. The qRT-PCR and immunohistochemistry (IHC) were used to confirm the findings. The immunotherapeutic response of VRK1 was anticipated by the IMvigor210 cohort. Lastly, the association between immune infiltration, m6A modification, and functional enrichment of differentially expressed genes (DEGs) was investigated in connection to VRK1 expression.

Results

VRK1 expression was markedly elevated on both the mRNA and protein levels in HCC. In HCC patients, a high expression of VRK1 was linked to a poor prognosis. Furthermore, there was a substantial positive correlation seen between increased VRK1 expression and the response rate to anti-PD-L1 immunotherapy. Relationships between VRK1 and m6A-related genes as well as different immune cells were shown by correlation studies. Lastly, enrichment analysis revealed a tight relationship between VRK1 and important biological functions, including DNA replication, cell cycle control, and fatty acid metabolism.

Conclusion

Our research reveals the potential of VRK1 as a novel biomarker for prognosis and immunotherapy response in HCC patients.

Identification of Hydroxysteroid Dehydrogenase Type 1 As a Potential Bladder Tumor Marker

Background

The 17beta-hydroxysteroid dehydrogenase type 1 (HSD17B) family has been implicated in the prognosis and treatment prediction of various malignancies; however, its association with bladder cancer (BLCA) remains unclear. This study aimed to evaluate the potential of HSD17B1, as a prognostic biomarker, for the survival of patients with BLCA and to determine its effectiveness as a supplemental biomarker for BLCA.

Methods

A series of bioinformatics techniques were applied to investigate the expression of HSD17B1 in different types of cancer and its potential association with the prognosis of BLCA patients using diverse databases. The UALCAN, Human Protein Atlas, cBioPortal, Metascape, GEPIA, MethSurv, and TIMER were employed to analyze expression differences, mutation status, enrichment analysis, overall survival, methylation, and immune-infiltrating cells. The real-time reverse transcription-PCR (qRT-PCR) was implemented to detect the messenger ribonucleic acid (mRNA) expression levels of HSD17B1 in vitro.

Results

Elevated mRNA and protein levels of HSD17B1, surpassing normal levels, were observed in BLCA samples. In addition, the BLCA patients with higher mRNA expression level of HSD17B1 significantly reduced the overall survival. Also, several immune infiltrating cells, including mast cell resting CIBERSORT-ABS, have been identified as tumor-associated biomarker genes, with the potential to significantly influence the immunological environment. Finally, qRT-PCR analysis revealed a significant upregulation of HSD17B1 mRNA expression level in the cancer cells compared to the human 293T cells, which was consistent with the bioinformatics data.

Conclusion

There is a strong correlation between the elevated HSD17B1 expression and positive prognosis in patients with BLCA. Therefore, HSD17B1 can be used as a prognostic biomarker in these patients.

Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis

BACKGROUND

Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.

AIM

This study aims to identify shared ICH genes for both processes.

METHODS

ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted via CMap database.

RESULTS

64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted via CMap.

CONCLUSION

Several shared genes between mitophagy and ferroptosis potentially drive ICH progression via TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

Weighted gene co-expression network analysis and machine learning identified the lipid metabolism-related gene LGMN as a novel biomarker for keloid

The aetiology of keloid formation remains unclear, and existing treatment modalities have not definitively established a successful approach. Therefore, it is necessary to identify reliable and novel keloid biomarkers as potential targets for therapeutic interventions. In this study, we performed differential expression analysis and functional enrichment analysis on the keloid related datasets, and found that multiple metabolism-related pathways were associated with keloid formation. Subsequently, the differentially expressed genes (DEGs) were intersected with the results of weighted gene co-expression network analysis (WGCNA) and the lipid metabolism-related genes (LMGs). Then, three learning machine algorithms (SVM-RFE, LASSO and Random Forest) together identified legumain (LGMN) as the most critical LMGs. LGMN was overexpressed in keloid and had a high diagnostic performance. The protein-protein interaction (PPI) network related to LGMN was constructed by GeneMANIA database. Functional analysis of indicated PPI network was involved in multiple immune response-related biological processes. Furthermore, immune infiltration analysis was conducted using the CIBERSORT method. M2-type macrophages were highly infiltrated in keloid tissues and were found to be significantly and positively correlated with LGMN expression. Gene set variation analysis (GSVA) indicated that LGMN may be related to promoting fibroblast proliferation and inhibiting their apoptosis. Moreover, eight potential drug candidates for keloid treatment were predicted by the DSigDB database. Western blot, qRT-PCR and immunohistochemistry staining results confirmed that LGMN was highly expressed in keloid. Collectively, our findings may identify a new biomarker and therapeutic target for keloid and contribute to the understanding of the potential pathogenesis of keloid.

Targeted therapies in bladder cancer: signaling pathways, applications, and challenges

Bladder cancer (BC) is one of the most prevalent malignancies in men. Understanding molecular characteristics via studying signaling pathways has made tremendous breakthroughs in BC therapies. Thus, targeted therapies including immune checkpoint inhibitors (ICIs), antibody-drug conjugates (ADCs), and tyrosine kinase inhibitor (TKI) have markedly improved advanced BC outcomes over the last few years. However, the considerable patients still progress after a period of treatment with current therapeutic regimens. Therefore, it is crucial to guide future drug development to improve BC survival, based on the molecular characteristics of BC and clinical outcomes of existing drugs. In this perspective, we summarize the applications and benefits of these targeted drugs and highlight our understanding of mechanisms of low response rates and immune escape of ICIs, ADCs toxicity, and TKI resistance. We also discuss potential solutions to these problems. In addition, we underscore the future drug development of targeting metabolic reprogramming and cancer stem cells (CSCs) with a deep understanding of their signaling pathways features. We expect that finding biomarkers, developing novo drugs and designing clinical trials with precisely selected patients and rationalized drugs will dramatically improve the quality of life and survival of patients with advanced BC.

Prognostic clinical phenotypes associated with tumor stemness in the immune microenvironment of T-cell exhaustion for hepatocellular carcinoma

T-cell exhaustion (TEX) and high heterogeneity of cancer stem cells (CSCs) are associated with progression, metastasis, and treatment resistance in hepatocellular carcinoma (HCC). Here, we aim to characterize TEX-stemness-related genes (TEXSRGs) and screen for HCC patients who are more sensitive to immunotherapy. The immune cell abundance identifier (ImmuCellAI) was utilized to precisely evaluate the abundance of TEX and screen TEX-related genes. The stemness index (mRNAsi) of samples was analyzed through the one-class logistic regression (OCLR) algorithm. Application of the non-negative matrix decomposition algorithm (NMF) for subtype identification of HCC samples. The different subtypes were assessed for differences in prognosis, tumor microenvironment (TME) landscape, and immunotherapy treatment response. Then, the TEXSRGS-score, which can accurately forecast the survival outcome of HCC patients, was built by LASSO-Cox and multivariate Cox regression, and experimentally validated for the most important TEXSRGs. We also analyzed the expression of TEXSRGs and the infiltration of CD8+ T cells in clinical samples using qRT-PCR and immunohistochemistry (IHC). Based on 146 TEXSRGs, we found two distinct clinical phenotypes with different TEX infiltration abundance, tumor stemness index, enrichment pathways, mutational landscape, and immune cell infiltration through the non-negative matrix decomposition algorithm (NMF), which were confirmed in the ICGC dataset. Utilizing eight TEXSRGs linked to clinical outcome, we created a TEXSRGs-score model to further improve the clinical applicability. Patients can be divided into two groups with substantial differences in the characteristics of immune cell infiltration, TEX infiltration abundance, and survival outcomes. The results of qRT-PCR and IHC analysis showed that PAFAH1B3, ZIC2, and ESR1 were differentially expressed in HCC and normal tissues and that patients with high TEXSRGs-scores had higher TEX infiltration abundance and tumor stemness gene expression. Regarding immunotherapy reaction and immune cell infiltration, patients with various TEXSRGs-score levels had various clinical traits. The outcome and immunotherapy efficacy of patients with low TEXSRGs-score was favorable. In conclusion, we identified two clinical subtypes with different prognoses, TEX infiltration abundance, tumor cell stemness index, and immunotherapy response based on TEXSRGs, and developed and validated a TEXSRGs-score capable of accurately predicting survival outcomes in HCC patients by comprehensive bioinformatics analysis. We believe that the TEXSRGs-score has prospective clinical relevance for prognostic assessment and may help physicians select prospective responders in preference to current immune checkpoint inhibitors (ICIs).

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis

OBJECTIVE

The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

METHODS

The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes.

RESULTS

We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs.

CONCLUSIONS

Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

Establishing a prognostic model with ferroptosis-related long non-coding RNAs in bladder cancer

Background

Ferroptosis is a distinct form of cell death that has the potential to supersede the drug resistance that is commonly observed with current chemotherapeutic agents. As a result, ferroptosis presents a new and innovative therapeutic pathway for cancer treatment. The current understanding regarding the expression of genes associated with ferroptosis in bladder cancer (BLCA) and their prognostic implications remains unclear. Consequently, this study aimed to examine the potential prognostic value of ferroptosis-associated long non-coding RNAs (lncRNAs) in BLCA.

Methods

The Cancer Genome Atlas (TCGA) was accessed to download RNA sequencing data and clinicopathological features of BLCA while accessing the FerrDb database to download ferroptosis-associated genes. The study calculated risk scores for ferroptosis-associated lncRNAs, and subsequently divided patients with BLCA into two groups, namely high- and low-risk, on the basis of the median risk score. Moreover, Kaplan-Meier (K-M) curves, Cox regression analysis, and column plots were utilized for evaluating the risk score prognostic value. Subsequently, the involvement of ferroptosis-associated mRNA, N6-methyladenosine (m6A) mRNA status, and immune responses was investigated for BLCA prognosis.

Results

Thirty-six lncRNAs were identified to be differently expressed and linked to the prognosis of BLCA. The findings from the K-M curve analysis indicated a significant association between a high-risk lncRNA profile and poor BLCA prognosis. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.810. The risk assessment model exhibited superior performance in predicting prognosis for BLCA compared to conventional clinicopathological features.

Conclusions

Thirty-six lncRNAs were found to be linked to ferroptosis for the prognosis of patients with BLCA, and these results may provide new insights for treating BLCA.

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Aldose reductase and cancer metabolism: The master regulator in the limelight

It is strongly established that metabolic reprogramming mediates the initiation, progression, and metastasis of a variety of cancers. However, there is no common biomarker identified to link the dysregulated metabolism and cancer progression. Recent studies strongly advise the involvement of aldose reductase (AR) in cancer metabolism. AR-mediated glucose metabolism creates a Warburg-like effect and an acidic tumour microenvironment in cancer cells. Moreover, AR overexpression is associated with the impairment of mitochondria and the accumulation of free fatty acids in cancer cells. Further, AR-mediated reduction of lipid aldehydes and chemotherapeutics are involved in the activation of factors promoting proliferation and chemo-resistance. In this review, we have delineated the possible mechanisms by which AR modulates cellular metabolism for cancer proliferation and survival. An in-depth understanding of cancer metabolism and the role of AR might lead to the use of AR inhibitors as metabolic modulating agents for the therapy of cancer.

Combined analysis of bulk and single-cell RNA sequencing reveals novel natural killer cell-related prognostic biomarkers for predicting immunotherapeutic response in hepatocellular carcinoma

Introduction

Natural killer (NK) cells play an irreplaceable and important role as a subtype of innate immune cells in the contemporary setting of antitumor immunity.

Methods

We chose a total of 1,196 samples for this analysis from the public dataset’s six separate cohorts. To identify 42 NK cell marker genes, we first carried out a thorough study of single-cell RNA sequencing data from the GSE149614 cohort of hepatocellular carcinoma (HCC).

Results

Using the NK cell marker genes in the TCGA cohort, we next created a seven-gene prognostic signature, separating the patients into two categories with distinct survival patterns. This signature’s prognostic prediction ability was well verified across several validation cohorts. Patients with high scores had higher TIDE scores but lower immune cell infiltration percentages. Importantly, low-scoring patients had superior immunotherapy response and prognosis than high-scoring patients in an independent immunotherapy cohort (IMvigor210). Finally, we used CD56 and TUBA1B antibodies for immunohistochemical labeling of HCC tissue sections, and we discovered a lower number of CD56+ cells in the HCC tissue sections with high TUBA1B expression.

Discussion

In summary, our research created a unique prognostic profile based on NK cell marker genes that may accurately predict how well immunotherapy would work for HCC patients.

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302-0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299-0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.

An Analysis Regarding the Association Between Proteasome (PSM) and Hepatocellular Carcinoma (HCC)

Background

The Proteasome (PSM) is a large multi-catalytic protease complex consisting of a 20S core particle and a 19S regulatory particle whose main function is to accept and degrade ubiquitinated substrates, are now considered as one of the potential regulators of tumor proliferation, and stemness maintenance. However, to date, studies on the relationship between PSM and hepatocellular carcinoma (HCC) are limited.

Methods

This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with PSM. A series of experiments in vivo and in vitro were performed to explore the function of the 26S proteasome non-ATPase regulatory subunit 13 (PSMD13) in HCC.

Results

HCC patients can be divided into two clusters. Cluster 1 (C1) patients having a significantly worse prognosis than Cluster (C2). Two subtypes had significant differences in proliferation-related signaling. In particular, the frequency of TP53 mutation was significantly higher in C1 than in C2. In addition, PSM-associated genes were highly consistent with the expression of DNA repair-related signatures, suggesting a potential link between PSM and genomic instability. We also found that downregulation of PSMD13 expression significantly inhibited stemness of tumor cells and impaired the Epithelial mesenchymal transition (EMT) process. Finally, the correlation between the PSMD13 and Ki67 was found to be strong.

Conclusion

PSM is a valid predictor of prognosis and therapeutic response in patients with HCC disease. Furthermore, PSMD13 may be a potential therapeutic target.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

Identification and Validation of Molecular Subtype and Prognostic Signature for Bladder Cancer Based on Neutrophil Extracellular Traps

Neutrophil extracellular traps (NETs) could promote tumor growth and distant metastases. Molecular subtypes of bladder cancer were identified with consensus cluster analysis. A NETs-related prognostic signature was constructed with LASSO cox regression analysis. As a result, we identified three subtypes of bladder cancer, which had a distinct difference in prognosis, immune microenvironment, TIDE score, mRNAsi score and IC50 score. We also developed a prognostic signature based on 5 NETs-related genes, which had a good performance in clinical outcome prediction of bladder cancer. These results may provide more data about the vital role of NETs in bladder cancer.

A novel fatty acid metabolism-related gene prognostic signature and candidate drugs for patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Fatty acid metabolism (FAM) is associated with the development and treatment of HCC. This study aimed to build a FAM-related gene model to assess the prognosis of HCC and provide guidance for individual treatment. RNA-sequencing data of patients with HCC from The Cancer Genome Atlas and Gene Expression Omnibus database (GSE14520) were extracted as the training and validation sets, respectively. A FAM-related gene predictive signature was built, and the performance of prognostic model was assessed. The immune infiltration and drug sensitivity were also evaluated. Quantitative real-time polymerase chain reaction and western blot were performed to evaluate the levels of the model genes. A 12-gene FAM-related risk signature was constructed; patients with a higher risk score had poorer prognosis than those with a lower risk score. Risk score was shown as an independent risk factor for overall survival of HCC, and the signature was further confirmed as an effective and accurate model. A nomogram was constructed, and it exhibited the good performance in the prognostic prediction. In addition, the immune cell infiltration and sensitivity to chemotherapy drugs were correlated with different risk levels. Finally, quantitative real-time polymerase chain reaction and western blot proved the changes of above genes. Differential expression of FAM-related genes can be used to predict response to immunotherapy and chemotherapy, and improve the clinical prognosis evaluation of patients with HCC, which provides new clues for further experimental exploration and verification on FAM-related genes in HCC.

Serum metabolites as early detection markers of non-muscle invasive bladder cancer in Chinese patients

Background

Biomarkers of different stages and grades of bladder cancer (BC) are important in clinical work. The objective of our study was to investigate new biomarkers of early-stage BC with liquid chromatography-high resolution mass spectrometry (LC-HRMS) using serum samples.

Methods

A total of 215 cases were included in our study, including 109 healthy adults as the control group and 106 non-muscle invasive bladder cancer (NMIBC) patients as the NMIBC group. Serum samples were collected from BC patients in the early stage, called NMIBC, and healthy people before surgery. We used LC-HRMS to distinguish the NMIBC group from the control group and the low-grade NMIBC group from the control group.

Results

An apparent difference between the NMIBC group and the control group was visualized by unsupervised principal component analysis (PCA). Metabolite panels for 16-hydroxy-10-oxohexadecanoic acid, PGF2a ethanolamide, sulfoglycolithocholate, and threoninyl-alanine were used to distinguish the two groups. The area under the curve (AUC) of the panels was 0.985, and the sensitivity and specificity were 98.63% and 98.59%, respectively. To distinguish the low-grade NMIBC group from the control group, serum metabolic profiling differences between the low-grade NMIBC group and control group samples were also analyzed. Metabolite panels of L-octanoylcarnitine, PGF2a ethanolamide, and threoninyl-alanine showed good discrimination performance. The AUC of the panels was 0.999, and the sensitivity and specificity were 97.8% and 100%, respectively.

Conclusion

Metabolomics analysis of serum samples can distinguish the NMIBC group from the control group, particularly the early-stage low-grade NMIBC group.

Prognostic Lysosome-Related Biomarkers for Predicting Drug Candidates in Hepatocellular Carcinoma: An Insilco Analysis

Background

Lysosomes play an important role in enhancing tumorigenesis and chemoresistance in hepatocellular carcinoma (HCC). Therefore, a detailed analysis of the role of lysosome-related genes could improve the poor prognosis of HCC patients.

Methods

Lysosome-associated genes were downloaded from the GO and Genome Enrichment Analysis (GSEA) databases. After analyzing lysosome-associated differentially expressed genes (DEGs) between the TCGA and GTEx cohorts, we used univariate Cox regression, LASSO-Cox regression, stepwise Cox regression, and multivariate Cox regression analyses to build a predictive risk model. The ICGC cohort was used as a test cohort for the prognostic signature's validation. It was also assessed how significantly the signature affected the tumor microenvironment (TME) and sensitivity to immune checkpoint inhibitors. To investigate the expression of this signature in clinical samples, qRT-PCR and immunohistochemistry (IHC) were carried out in 50 normal tissues and 59 HCC tissues.

Results

A total of 894 lysosome-associated genes were obtained. After identifying 113 lysosome-associated DEGs, we constructed a five-gene prognostic signature (RRAGD, AP1M2, CRHBP, NCSTN, and SLCO4C1) that can be effectively applied to the prognostic classification of HCC patients in TCGA and ICGC cohorts. Additionally, we discovered that this signature can affect the proportion of macrophage infiltration in TME. We also evaluated several tumor-sensitive medicines that affect this signature. Finally, we discovered that HCC tissues had lower amounts of CRHBP compared to normal tissues by the qRT-PCR and IHC assay.

Conclusion

We developed and validated a predictive signature of five lysosome-related genes for HCC patients and verified the downregulation of CRHBP expression in clinical samples, which may provide fresh perspectives for customized immunotherapy.

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

Background

Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP.

Methods

In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms.

Results

The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP.

Conclusion

Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.

NUPR1 imparts oncogenic potential in bladder cancer

BACKGROUND

NUPR1, or p8, is a small chromatin protein that plays a central role in the resistance to treatment and progression of cancer. Nevertheless, the molecular mechanism of NUPR1 in bladder cancer (BLCA) remains unclear.

METHODS

We used online databases and immunohistochemistry (IHC) to explore the expression of NUPR1 in BLCA tissues and controls. Lentivirus-mediated small interfering ribonucleic acid (siRNA) was used to knockdown the expression of NUPR1 in two human BLCA cell lines. We used an in vivo experiment to investigate the effect of NUPR1 knockdown on the growth of BLCA. Moreover, an in silico analysis was conducted to assess the differential expression profile after NUPR1 interference. The CIBERSORT algorithm was utilized to evaluate the effects of tumor-infiltrating immune cells among BLCA patients.

RESULTS

The expression of NUPR1 in BLCA tissues was significantly higher than in the control. NUPR1 expression was also positively correlated with the stage of BLCA. After lentivirus-mediated interference, the expression of NUPR1 was significantly down-regulated in BLCA cell lines. The cell cycle was blocked in G1 phase and the cell proportion of S phase was decreased in both two cell lines. Moreover, in vivo experiment revealed that the tumor growth of BLCA can be delayed by inhibiting the expression of NUPR1. Both in silico analysis and functional experiments revealed that NUPR1 was correlated with epithelial-mesenchymal transition (EMT). We also revealed that macrophages were the most related immune cells associated with the expression of NUPR1 in BLCA.

CONCLUSIONS

This study suggests that NUPR1 plays a carcinogenic role in BLCA. NUPR1 lentivirus-mediated interference could interfere with cycle progression of the BLCA cell, resulting in cell cycle arrest in the G1-phase. The carcinogenic effect of NUPR1 in BLCA is likely achieved through EMT. NUPR1 is correlated with the M0-type macrophage markers CD68 and CD11b-integrin.

A novel stemness-hypoxia-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma

Background

The specific differentiation potential, unlimited proliferation, and self-renewal capacity of cancer stem cells (CSCs) are closely related to the occurrence, recurrence, and drug resistance of hepatocellular carcinoma (HCC), as well as hypoxia. Therefore, an in-depth analysis of the relationship between HCC stemness, oxygenation status, and the effectiveness of immunotherapy is necessary to improve the poor prognosis of HCC patients.

Methods

The weighted gene co-expression network analysis (WGCNA) was utilized to find hypoxia-related genes, and the stemness index (mRNAsi) was evaluated using the one-class logistic regression (OCLR) technique. Based on stemness-hypoxia-related genes (SHRGs), population subgroup categorization using NMF cluster analysis was carried out. The relationship between SHRGs and survival outcomes was determined using univariate Cox regression. The LASSO-Cox regression strategy was performed to investigate the quality and establish the classifier associated with prognosis. The main effect of risk scores on the tumor microenvironment (TME) and its response to immune checkpoint drugs was also examined. Finally, qRT-PCR was performed to explore the expression and prognostic value of the signature in clinical samples.

Results

After identifying tumor stemness- and hypoxia-related genes through a series of bioinformatics analyses, we constructed a prognostic stratification model based on these SHRGs, which can be effectively applied to the prognostic classification of HCC patients and the prediction of immune checkpoint inhibitors (ICIs) efficacy. Independent validation of the model in the ICGC cohort yielded good results. In addition, we also constructed hypoxic cell models in Herp3B and Huh7 cells to verify the expression of genes in the prognostic model and found that C7, CLEC1B, and CXCL6 were not only related to the tumor stemness but also related to hypoxia. Finally, we found that the constructed signature had a good prognostic value in the clinical sample.

Conclusions

We constructed and validated a stemness-hypoxia-related prognostic signature that can be used to predict the efficacy of ICIs therapy. We also verified that C7, CLEC1B, and CXCL6 are indeed associated with stemness and hypoxia through a hypoxic cell model, which may provide new ideas for individualized immunotherapy.

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Lipid metabolism affects cell proliferation, differentiation, membrane homeostasis and drug resistance. An in-depth exploration of lipid metabolism in gliomas might provide a novel direction for gliomas treatment. A lipid metabolism-related risk signature was constructed in our study to assess the prognosis of patients with gliomas. Lipid metabolism-related genes were extracted. Differentially expressed genes (DEGs) were screened, and a risk signature was built. The ability of the risk signature to predict the outcomes of patients with gliomas was assessed using the log-rank test and Cox regression analysis. The relationships between immunological characteristics, drug sensitivity and the risk score were evaluated, and the risk-related mechanisms were also estimated. Twenty lipid metabolism-related DEGs associated with the patient prognosis were included in the risk signature. The survival rate of high-risk patients was worse than that of low-risk patients. The risk score independently predicted the outcomes of patients. Immunological parameters, drug sensitivity, immunotherapy benefits, and numerous molecular mechanisms were significantly associated with the risk score. A lipid metabolism-related risk signature might effectively assess the prognosis of patients with gliomas. The risk score might guide individualized treatment and further clinical decision-making for patients with gliomas.