Novel necroptosis-related gene signature for predicting the prognosis of pancreatic adenocarcinoma

Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management

Background

Breast cancer is the leading cause of cancer-related death in women. Necroptosis, a form of programmed necrotic cell death, occurs in many solid tumors, including breast cancer, and influences anti-tumor immunity. The role of necroptosis in managing breast cancer recurrence remains unclear.

Methods

Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO (GSE20685, GSE21653, GSE25055) and TCGA databases. Data analysis and visualization were performed using R. Unsupervised Consensus Clustering and LASSO-COX regression stratified breast cancer patients. GO, KEGG, GSVA, ESTIMATE, and ROC analyses were used to investigate necroptotic signatures. In vitro and in vivo experiments validated necroptosis's role in breast cancer immunity.

Results

The potential function of necroptotic signature in immunity was first indicated with GO analysis in BRCA cohort. Next, two prognostic models based on the necroptotic profiles both suggested a link between low-risk group with a particular necroptotic immune signature. And a variety of immune cells and immune pathways were shown to be positively associated with a patient's risk score. As an altered immune checkpoint pattern was observed after regulating necroptotic genes, where TIM-3 and LAGLS9 elevated significantly in low-risk group, further validation in vitro and in vivo demonstrated that manipulating a subset of necroptotic gene set could sensitize tumor response to the co-blockade immunotherapy of anti-TIM-3 and anti-PD-1.

Conclusion

We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis. A subset of necroptotic gene set, composed of TLR3, RIPK3, NLRP3, CASP1, ALDH2 and EZH2, was identified as a biomarker set for predicting immunotherapy-response and recurrence-related prognosis. Targeting necroptosis could helpfacilitate the development of novel breast cancer treatments and tailor personalized medical treatment.

Mechanisms and clinical landscape of N6-methyladenosine (m6A) RNA modification in gastrointestinal tract cancers

Epigenetics encompasses reversible and heritable chemical modifications of non-nuclear DNA sequences, including DNA and RNA methylation, histone modifications, non-coding RNA modifications, and chromatin rearrangements. In addition to well-studied DNA and histone methylation, RNA methylation has emerged as a hot topic in biological sciences over the past decade. N6-methyladenosine (m6A) is the most common and abundant modification in eukaryotic mRNA, affecting all RNA stages, including transcription, translation, and degradation. Advances in high-throughput sequencing technologies made it feasible to identify the chemical basis and biological functions of m6A RNA. Dysregulation of m6A levels and associated modifying proteins can both inhibit and promote cancer, highlighting the importance of the tumor microenvironment in diverse biological processes. Gastrointestinal tract cancers, including gastric, colorectal, and pancreatic cancers, are among the most common and deadly malignancies in humans. Growing evidence suggests a close association between m6A levels and the progression of gastrointestinal tumors. Global m6A modification levels are substantially modified in gastrointestinal tumor tissues and cell lines compared to healthy tissues and cells, possibly influencing various biological behaviors such as tumor cell proliferation, invasion, metastasis, and drug resistance. Exploring the diagnostic and therapeutic potential of m6A-related proteins is critical from a clinical standpoint. Developing more specific and effective m6A modulators offers new options for treating these tumors and deeper insights into gastrointestinal tract cancers.

Deciphering the SOX4/MAPK1 regulatory axis: a phosphoproteomic insight into IQGAP1 phosphorylation and pancreatic Cancer progression

OBJECTIVE

This study aims to elucidate the functional role of IQGAP1 phosphorylation modification mediated by the SOX4/MAPK1 regulatory axis in developing pancreatic cancer through phosphoproteomics analysis.

METHODS

Proteomics and phosphoproteomics data of pancreatic cancer were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Differential analysis, kinase-substrate enrichment analysis (KSEA), and independent prognosis analysis were performed on these datasets. Subtype analysis of pancreatic cancer patients was conducted based on the expression of prognostic-related proteins, and the prognosis of different subtypes was evaluated through prognosis analysis. Differential analysis of proteins in different subtypes was performed to identify differential proteins in the high-risk subtype. Clinical correlation analysis was conducted based on the expression of prognostic-related proteins, pancreatic cancer typing results, and clinical characteristics in the pancreatic cancer proteomics dataset. Functional pathway enrichment analysis was performed using GSEA/GO/KEGG, and most module proteins correlated with pancreatic cancer were selected using WGCNA analysis. In cell experiments, pancreatic cancer cells were grouped, and the expression levels of SOX4, MAPK1, and the phosphorylation level of IQGAP1 were detected by RT-qPCR and Western blot experiments. The effect of SOX4 on MAPK1 promoter transcriptional activity was assessed using a dual-luciferase assay, and the enrichment of SOX4 on the MAPK1 promoter was examined using a ChIP assay. The proliferation, migration, and invasion functions of grouped pancreatic cancer cells were assessed using CCK-8, colony formation, and Transwell assays. In animal experiments, the impact of SOX4 on tumor growth and metastasis through the regulation of MAPK1-IQGAP1 phosphorylation modification was studied by constructing subcutaneous and orthotopic pancreatic cancer xenograft models, as well as a liver metastasis model in nude mice.

RESULTS

Phosphoproteomics and proteomics data analysis revealed that the kinase MAPK1 may play an important role in pancreatic cancer progression by promoting IQGAP1 phosphorylation modification. Proteomics analysis classified pancreatic cancer patients into two subtypes, C1 and C2, where the high-risk C2 subtype was associated with poor prognosis, malignant tumor typing, and enriched tumor-related pathways. SOX4 may promote the occurrence of the high-risk C2 subtype of pancreatic cancer by regulating MAPK1-IQGAP1 phosphorylation modification. In vitro cell experiments confirmed that SOX4 promoted IQGAP1 phosphorylation modification by activating MAPK1 transcription while silencing SOX4 inhibited the proliferation, migration, and invasion of pancreatic cancer cells by reducing the phosphorylation level of MAPK1-IQGAP1. In vivo, animal experiments further confirmed that silencing SOX4 suppressed the growth and metastasis of pancreatic cancer by reducing the phosphorylation level of MAPK1-IQGAP1.

CONCLUSION

The findings of this study suggest that SOX4 promotes the phosphorylation modification of IQGAP1 by activating MAPK1 transcription, thereby facilitating the growth and metastasis of pancreatic cancer.

Effect of human heart valve-derived ECM and NP/PCL electrospun nanofibrous sheet on mice bone marrow mononuclear cells and cardiac repair

Background

Biomaterials can improve cardiac repair combined with transplantation of bone marrow mononuclear cells (BMMNCs). In this study, we compared the phenotype and cardiac repair between human heart valve-derived scaffold (hHVS) and natural protein/polycaprolactone (NP/PCL) anchored BMNNCs.

Methods and results

BMMNCs were obtained from mice five days following myocardial infarction. Subsequently, BMMNCs were separately cultured on hHVS and PCL. Proliferation and cardiomyogenic differentiation were detected in vitro. Cardiac function was measured after transplantation of cell-seeded cardiac patch on MI mice. After that, the BMMNCs were collected for mRNA sequencing after culturing on the scaffolds. Upon anchoring onto hHVS or PCL, BMMNCs exhibited an increased capacity for proliferation in vitro, however, the cells on hHVS exhibited superior cardiomyogenic differentiation ability. Moreover, both BMMNCs-seeded biomaterials effectively improved cardiac function after 4 weeks of transplantation, with reduced infarction area and restricted LV remodeling. Cell-seeded hHVS was superior to cell-seeded PCL.

Conclusion

BMMNCs on hHVS showed better capacity in both cell cardiac repairing and improvement for cardiac function than on PCL. Compared with seeded onto PCL, BMMNCs on hHVS had 253 genes up regulated and 189 genes down regulated. The reason for hHVS' better performance than PCL as a scaffold for BMMNCs might be due to the fact that optimized method of decellularization let more cytokines in ECM retained.

Molecular subtypes of clear cell renal carcinoma based on PCD-related long non-coding RNAs expression: insights into the underlying mechanisms and therapeutic strategies

BACKGROUND

PCD-related long non-coding RNAs (PRLs) are rarely investigated in relation to clear cell renal carcinoma (ccRCC). As part of this study, we evaluated the immunological potential of PRL signatures as a biomarker for ccRCC prognosis and immunological function.

MATERIALS AND METHODS

Data were downloaded from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. A Pearson correlation analysis was conducted on the 27 PCD-associated genes to determine whether lncRNAs were significantly associated with PCD. Kaplan-Meier analysis, biological function identification, immune infiltration analysis, estimation of efficacy of immunotherapy and targeted drug screening, and exploration of the landscape of mutation status were conducted by analyzing the risk scores.

RESULTS

Seven PRLs, LINC02747, AP001636.3, AC022126.1, LINC02657, LINC02609, LINC02154, and ZNNT1, were used to divide patients with ccRCC into groups with high and low risk. High-risk patients had a worse prognosis than low-risk patients, according to the results, and the PRL signature showed promising predictive ability. More immune cells were clustered in the high-risk group, whereas the immune cell function of the low-risk group was significantly suppressed. The high-risk group was less sensitive to immunotherapy, whereas the low-risk group had positive responses to most drugs.

CONCLUSIONS

Collectively, we established and verified a PRL signature that could competently guide the prognostic survival and immunotherapy of ccRCC. In addition, molecular subtypes were determined for ccRCC based on PRL expression, which may help elucidate the underlying molecular mechanism of ccRCC and develop targeted treatments.

Identification of necroptosis-associated miRNA signature for predicting prognosis and immune landscape in stomach adenocarcinoma

PURPOSE

This study aims to identify the potential necroptosis related genes (NRGs)-associated miRNAs signature and explore the impact on the prognosis of stomach adenocarcinoma (STAD).

METHODS

Employing rigorous methodologies, we utilized univariate Cox, Lasso and multivariate Cox regression analyses to develop a prognostic signature. Kaplan-Meier (K-M) and ROC curves were applied to assess the prognostic value of signature in a training group and an independent test group. Furthermore, we conducted Gene Set Enrichment Analysis (GSEA) for enrichment of tumor-related pathways. The risk score was calculated for each patient based on the expression of miRNAs which were enrolled in the signature. Patients were stratified into high- and low-risk groups. The immune cell infiltration and immunotherapy were compared between the two groups. Finally, the diagnostic potential of the miRNA was explored by RT-qPCR.

RESULTS

We constructed a prognostic model based on 6 NRGs-associated miRNAs. K-M plots underscored superior survival outcomes in the low-risk group. GSEA results revealed the enrichment of several tumor-related pathways in the high-risk group. Notably, CD8+ T cells, Tregs and activated memory CD4+ T cells exhibited negative correlations with the risk score. Additionally, a few immune checkpoint genes, such as CTLA4, PD1 and PD-L1, were significantly upregulated in the low-risk group. Furthermore, the serum expression levels of all these 6 miRNAs were significantly elevated in STAD patients.

CONCLUSIONS

Our study identified a robust risk score derived from a signature of 6 NRGs-associated miRNAs, demonstrating high efficacy for prognosis of STAD. These results not only contributed to our understanding of STAD pathogenesis, but also held promise for potential clinical applications, particularly in the realm of personalized immunotherapy for STAD patients.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

An E3 ubiquitin-proteasome gene signature for predicting prognosis in patients with pancreatic cancer

Pancreatic cancer is the seventh leading cause of cancer death worldwide, which is demonstrated with remarkable resistance to radiotherapy and chemotherapy. The identification of prognosis signature and novel prognostic markers will facilitate patient stratification and an individualized precision therapy strategy. In this study, TCGA-PAAD was used to screen prognostic E3 ubiquitin ligases and establish prognostic signatures, and GEO database was used to verify the accuracy of prognostic signatures. Functional analysis, in vitro experiments and clinical cohort studies were used to analyze the function and prognostic efficacy of the target gene. An E3 ligase-based signature of 9 genes and the nomogram were developed, and the signature was proved to accurately predict the prognosis of patients with pancreatic cancer. WDR37 might be the most prognostic E3 ubiquitin ligase in pancreatic cancer, and the clinical cohort analyses suggested a tumor-suppressive role. The results of functional analysis and in vitro experiments indicated that WDR37 may promote the degradation of TCP1 complex to inhibit tumor and improve immune cell infiltration. The E3 ligase-based signature accurately predicted the prognosis of patients with pancreatic cancer, so it can be used as a decision-making tool to guide the treatment of patients with pancreatic cancer. At the same time, WDR37, the main gene in E3PMP signature, can be used as the most prognostic E3 ubiquitin ligase in the treatment of pancreatic cancer.

Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated.

METHODS

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA.

RESULTS

FRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

CONCLUSIONS

FRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.

Identification of basement membrane-related signatures for estimating prognosis, immune infiltration landscape and drug candidates in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a frequent digestive system cancer, which has high mortality and bad outcome. However, the role of basement membrane (BM)-related gene in assessing patient's outcome, microenvironment and treatment response remain unclear. Methods: Basement membrane (BM)-associated genes were detected by univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses using data from the TCGA databases. A risk score system was constructed to distinguish patients in the high- and low-risk groups. Prognostic gene distribution in various immune cell forms was explored through scRNA-seq. Immune cell infiltration was assessed using CIBERSORT and ESTIMATE. The IC50 of common chemotherapeutic drugs and useful molecule compounds were evaluated. The mRNA and protein expression of important signatures were validated utilizing GEPIA and HPA databases. Results: Compared to low risk PAAD patients, PAAD patients with high risk showed a significant much worse overall survival (OS). Risk score of BM-associated genes could estimate patient outcome well, and areas under the curve (AUC) of receiver operating characteristic (ROC) survival curve were 0.76, 0.85, and 0.85 at 1-, 3-, and 5-year. Clinical impact curve (CIC) curve demonstrated the clinical importance of risk score. scRNA-seq revealed that BM-related genes were mainly distributed in malignant cells. Significant variations existed in the immune microenvironment, immune checkpoint expression and chemotherapy response between the studied groups. Furthermore, the mRNA expression levels of FAM83A, LY6D, MET, MUC16, MYEOV, and WNT7A were elevated in PAAD tissues, while the protein expression patterns of LY6D, MET, MUC16, and WNT7A were higher in tumor sample. RO-90-7501, Scriptaid, TG-101348, XMD-892, and XMD-1150 may be valuable small molecule drugs to treat PAAD. Conclusions: In conclusion, we develop a novel BM-related gene signature provide new insights and targets for the diagnosis, outcome estimation, candidate drugs and therapy management of PAAD patients.

Family with sequence similarity 111 member B contributes to tumor growth and metastasis by mediating cell proliferation, invasion, and EMT via transforming acidic coiled-coil protein 3/PI3K/AKT signaling pathway in hepatocellular carcinoma

As a complex systemic disease, primary liver cancer ranks third in death rate for solid tumors worldwide. Family with sequence similarity 111 member B (FAM111B), which was found to be aberrantly mutated in multiple cancers, is a candidate oncogene. We aimed to determine the function and mechanism of FAM111B in hepatocellular carcinoma (HCC). The expression of FAM111B was evaluated in HCC tissues, adjacent tissues, HCC cell lines. The impact of FAM111B on proliferation, invasion, apoptosis and EMT of HCC cells were detected by CCK-8, Transwell, flow cytometry and Western blot assays. The relationship between FAM111B and transforming acidic coiled-coil protein 3 (TACC3) was assessed by CoIP and Immunofluorescence (IF) staining assays. The effect of FAM111B on tumor growth was detected by using xenograft model of nude mice. The expression of FAM111B was upregulated in HCC tissues and cell lines, and the prognosis of HCC patients was worse in the high FAM111B expression group, and its expression level was associated with the TNM stage of HCC. FAM111B silencing inhibited HCC cell proliferation and invasion, EMT and induced apoptosis. Besides, TACC3 served as an interactor for FAM111B, which could enhance TACC3 expression, thus activing PI3K/AKT pathway. Rescue experiments revealed that elevated of TACC3 restored the inhibitory effect of FAM111B overexpression on the cell functions via PI3K/AKT pathway. In vivo, FAM111B inhibition hampered tumor growth and metastasis of HCC. This study highlighted a key player of FAM111B in modulating the malignant biological progression of HCC via TACC3/PI3K/AKT signaling pathway, displaying a potential therapeutic target for HCC.

Ferroptosis-related genes prognostic signature for pancreatic cancer and immune infiltration: potential biomarkers for predicting overall survival

BACKGROUND

Pancreatic adenocarcinoma (PAAD) constitutes a lethal malignancy, notorious for its elevated mortality rates due to the difficulties in early diagnosis and rapid metastasis. The emerging paradigm of ferroptosis-an iron-catalyzed, regulated cell death distinguished by the accrual of lipid peroxides-has recently garnered scholarly focus. However, the expression landscape of ferroptosis-related genes (FRGs) in PAAD and their prognostic implications remain enigmatic.

METHODS

We undertook a rigorous quantification of FRGs in PAAD samples, sourcing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. These repositories also provided extensive metadata, encompassing mesenchymal stemness index (mRNAsi), genomic mutations, copy number variations (CNV), tumor mutational burden (TMB), and other clinical attributes. A predictive model was constructed utilizing Lasso regression analysis, and a co-expression study was executed to elucidate the complex interconnections between FRGs and other gene sets.

RESULTS

Intriguingly, FRGs were substantially upregulated in the high-risk cohort, even in the absence of clinically manifest symptoms, emphasizing their utility as prognostic biomarkers. Gene set enrichment analysis (GSEA) revealed significant enrichment of immune and tumor-related pathways in this high-risk demographic. Striking heterogeneities in immune function and N6-methyladenosine (m6A) RNA modification were observed between the low- and high-risk groups. Our analysis further implicated a cohort of genes-including LINC01559, C11orf86, SERPINB5, DSG3, MSLN, EREG, FAM83A, CXCL5, LY6D, and PSCA-as cardinal mediators in PAAD pathogenesis. A convergence of our predictive model with an analysis of CNVs, single nucleotide polymorphisms (SNPs), and drug sensitivities, revealed an intricate relationship with the FRGs.

CONCLUSIONS

Our findings accentuate the salient role of FRGs as critical modulators in the pathogenesis and progression of PAAD. Importantly, our composite prognostic framework offers invaluable insights into PAAD clinical trajectory. Moreover, the complex crosstalk between FRGs and immune cell landscapes in the tumor microenvironment (TME) may elucidate prospective therapeutic strategies. The clinical translational utility of these insights, however, requires further in-depth empirical exploration. Accordingly, the FRG signature introduces a compelling new avenue for risk stratification and targeted therapeutic interventions in this devastating malignancy.

Comprehensive analysis of necroptotic patterns and associated immune landscapes in individualized treatment of skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) constitutes a malignant cutaneous neoplasm characterized by an exceedingly unfavorable prognosis. Over the past years, necroptosis, a manifestation of inflammatory programmed cell demise, has gained substantial traction in its application. However, a conclusive correlation between the expression of necroptosis-related genes (NRGs) and SKCM patient's prognosis remains elusive. In this endeavor, we have undertaken an integrative analysis of genomic data, aiming to provide an exhaustive evaluation of the intricate interplay between melanoma necroptosis and immune-infiltration nuances within the tumor microenvironment. Through meticulous scrutiny, we have endeavored to discern the prognostic potency harbored by individual necroptosis-associated genes. Our efforts culminated in the establishment of a risk stratification framework, allowing for the appraisal of necroptosis irregularities within each afflicted cutaneous melanoma patient. Notably, those SKCM patients classified within the low-risk cohort exhibited a markedly elevated survival quotient, in stark contrast to their high-risk counterparts (p < 0.001). Remarkably, the low-risk cohort not only displayed a more favorable survival rate but also exhibited an enhanced responsiveness to immunotherapeutic interventions, relative to their high-risk counterparts. The outcomes of this investigation proffer insights into a conceivable mechanistic underpinning linking necroptosis-related attributes to the intricacies of the tumor microenvironment. This prompts a conjecture regarding the plausible association between necroptosis characteristics and the broader tumor microenvironmental milieu. However, it is imperative to emphasize that the pursuit of discerning whether the expression profiles of NRG genes can indeed be regarded as viable therapeutic targets necessitates further comprehensive exploration and scrutiny. In conclusion, our study sheds light on the intricate interrelationship between necroptosis-related factors and the tumor microenvironment, potentially opening avenues for therapeutic interventions. However, the prospect of translating these findings into clinical applications mandates rigorous investigation.

Identification of tryptophan metabolism-related genes in immunity and immunotherapy in Alzheimer's disease

Recent studies have highlighted the significant involvement of tryptophan metabolism in the pathogenesis of Alzheimer's disease (AD). However, a comprehensive investigation of the precise role of tryptophan metabolism in the context of AD is still lacking. This study employed a bioinformatics approach to identify and validate potential tryptophan metabolism-related genes (TrpMgs) associated with AD. The discovery of TrpMgs was facilitated through the intersection of the Weighted Gene Co-expression Network Analysis (WGCNA) test and 17 known tryptophan metabolism pathways. Subsequently, the putative biological functions and pathways of the TrpMgs were elucidated using Gene Set Variation Analysis (GSVA). Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to identify hub genes and evaluate the diagnostic efficiency of the 5 TrpMgs in distinguishing AD. The relationship between hub TrpMgs and clinical characteristics was also investigated. Finally, in vivo verification of the five TrpMgs was performed using APP/PS1 mice. We identified 5 TrpMgs associated with AD, including propionyl-CoA carboxylase subunit beta (PCCB), TEA Domain Transcription Factor 1 (TEAD1), Phenylalanyl-TRNA Synthetase Subunit Beta (FARSB), Neurofascin (NFASC), and Ezrin (EZR). Among these genes, PCCB, FARSB, NFASC, and TEAD1 showed correlations with age. In the hippocampus of APP/PS1 mice, we observed down-regulation of FARSB, PCCB, and NFASC mRNA expressions. Furthermore, PCCB and NFASC protein expressions were also down-regulated in the cerebral cortex and hippocampus of APP/PS1 mice. Our study paves the way for future research aimed at unraveling the intricate mechanisms underlying tryptophan metabolism dysregulation in AD and its therapeutic implications.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma

BACKGROUND

Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers.

METHODS

The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level.

RESULTS

The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs.

CONCLUSION

We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

A systematic framework for identifying prognostic necroptosis-related lncRNAs and verification of lncRNA CRNDE/miR-23b-3p/IDH1 regulatory axis in glioma

Glioma remains the most frequent malignancy of the central nervous system. Recently, necroptosis has been identified as a cell death process that mediates the proliferation and development of tumor cells. LncRNAs play a key role in the diagnosis and treatment of various diseases. However, the impact that necrosis-related lncRNAs (NRLs) have on glioma remains unclear. In our studies, we selected 9 NRLs to construct a prognostic model. Meanwhile, we assessed the survival curves of these 9 NRLs. Our findings found ADGRA1-AS1 and WAC-AS1 were protective lncRNAs, while MIR210HG, LINC01503, CRNDE, HOXC-AS1, ZIM2-AS1, MIR22HG and PLBD1-AS1 were risk lncRNAs. Specifically, 12 immune cells, 25 immune-correlated pathways, and TME score were differentially expressed in the both risk groups. Additionally, the study predicted and validated the necroptosis-related lncRNA CRNDE/miR-23b-3p/IDH1 axis. CRNDE was strongly expressed in glioma specimens and several cell lines. Inhibiting CRNDE resulted in a substantial reduction in the proliferation and migration of U-118MG and U251 cells. Furthermore, the study predicted that CRNDE may exhibit oncogenic features by adsorbing miR-23b-3p and positively regulating IDH1 expression. Overall, the study constructed a prognostic model in glioma, and predicted a lncRNA CRNDE/miR-23b-3p/IDH1 axis, which could potentially be useful for gene therapy of glioma.

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

Background

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

Methods

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

Results

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

Conclusion

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

Genome, Metabolism, or Immunity: Which Is the Primary Decider of Pancreatic Cancer Fate through Non-Apoptotic Cell Death?

Pancreatic ductal adenocarcinoma (PDAC) is a solid tumor characterized by poor prognosis and resistance to treatment. Resistance to apoptosis, a cell death process, and anti-apoptotic mechanisms, are some of the hallmarks of cancer. Exploring non-apoptotic cell death mechanisms provides an opportunity to overcome apoptosis resistance in PDAC. Several recent studies evaluated ferroptosis, necroptosis, and pyroptosis as the non-apoptotic cell death processes in PDAC that play a crucial role in the prognosis and treatment of this disease. Ferroptosis, necroptosis, and pyroptosis play a crucial role in PDAC development via several signaling pathways, gene expression, and immunity regulation. This review summarizes the current understanding of how ferroptosis, necroptosis, and pyroptosis interact with signaling pathways, the genome, the immune system, the metabolism, and other factors in the prognosis and treatment of PDAC.

Construction and experimental validation of a necroptosis-related lncRNA signature as a prognostic model and immune-landscape predictor for lung adenocarcinoma

Necroptosis is a new form of cell death. Since the discovery that long non-coding RNAs can affect the proliferation of lung adenocarcinoma, much has been learned about it, yet those of necroptosis-related long non-coding RNAs (NRlncRNAs) in lung adenocarcinoma (LUAD) remain enigmatic. This study aims to explore novel biomarkers and therapeutic targets for LUAD. The LUAD data was downloaded from The Cancer Genome Atlas, and necroptosis-related genes were retrieved from published literature. Co-expression analysis, univariate Cox analysis, least absolute shrinkage and selection operator regression analysis were used to identify necroptosis-related prognostic long non-coding RNAs. A comprehensive evaluation of tumor immunity for necrosis-related features was performed, and we identified a 9-NRlncRNA signature. Kaplan-Meier and Cox regression analyses confirmed that the signature was an independent predictor of LUAD outcome in the test and train sets (all P < 0.05). The areas of 1-, 2-, and 3-year overall survival under the time-dependent receiver operating characteristics (ROC) curve (AUC) were 0.754, 0.746, and 0.720, respectively. The GSEA results showed that 9 NRlncRNAs were associated with multiple malignancy-associated and immunoregulatory pathways. Based on this model, we found that the immune status and level of response to chemotherapy and targeted therapy were significantly different in the low-risk group compared with the high-risk group. qRT-PCR assay revealed that 9 NRlncRNAs were involved in the regulation of tumor cell proliferation and may affect the expression of programmed cell death 1 (PD1) and CD28 at human immune checkpoints. Our results indicated that the novel signature involving 9 NRlncRNAs (AL031600.2, LINC01281, AP001178.1, AL157823.2, LINC01290, MED4-AS1, AC026355.2, AL606489.1, FAM83A-AS1) can predict the prognosis of LUAD and are associated with the immune response. This will provide new insights into the pathogenesis and development of therapies for LUAD.

Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer

Background

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.

Methods

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.

Results

PMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

Conclusion

PMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

Background

Bladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.

Methods

Predictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.

Results

PyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.

Conclusions

BLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets.

The Structural Dynamics, Complexity of Interactions, and Functions in Cancer of Multi-SAM Containing Proteins

SAM domains are crucial mediators of diverse interactions, including those important for tumorigenesis or metastasis of cancers, and thus SAM domains can be attractive targets for developing cancer therapies. This review aims to explore the literature, especially on the recent findings of the structural dynamics, regulation, and functions of SAM domains in proteins containing more than one SAM (multi-SAM containing proteins, MSCPs). The topics here include how intrinsic disorder of some SAMs and an additional SAM domain in MSCPs increase the complexity of their interactions and oligomerization arrangements. Many similarities exist among these MSCPs, including their effects on cancer cell adhesion, migration, and metastasis. In addition, they are all involved in some types of receptor-mediated signaling and neurology-related functions or diseases, although the specific receptors and functions vary. This review also provides a simple outline of methods for studying protein domains, which may help non-structural biologists to reach out and build new collaborations to study their favorite protein domains/regions. Overall, this review aims to provide representative examples of various scenarios that may provide clues to better understand the roles of SAM domains and MSCPs in cancer in general.

A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy

Alzheimer's disease (AD) is characterized as a distinct onset and progression of cognitive and functional decline associated with age, as well as a specific neuropathology. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. However, a full investigation of its role in Alzheimer's disease is still missing. This study intended to find and confirm potential Gln-related genes associated with AD using bioinformatics analysis. The discovery of GlnMgs was made possible by the intersection of the WGCNA test and 26 Gln-metabolism genes (GlnMgs). GlnMgs' putative biological functions and pathways were identified using GSVA. The LASSO method was then used to identify the hub genes as well as the diagnostic efficiency of the four GlnMgs in identifying AD. The association between hub GlnMgs and clinical characteristics was also studied. Finally, the GSE63060 was utilized to confirm the levels of expression of the four GlnMgs. Four GlnMgs were discovered (ATP5H, NDUFAB1, PFN2, and SPHKAP). For biological function analysis, cell fate specification, atrioventricular canal development, and neuron fate specification were emphasized. The diagnostic ability of the four GlnMgs in differentiating AD exhibited a good value. This study discovered four GlnMgs that are linked to AD. They shed light on potential new biomarkers for AD and tracking its progression.

Analysis of cuproptosis-related lncRNA signature for predicting prognosis and tumor immune microenvironment in pancreatic cancer

Pancreatic cancer (PC) is a highly malignant digestive tract tumor, with a dismal 5-year survival rate. Recently, cuproptosis was found to be copper-dependent cell death. This work aims to establish a cuproptosis-related lncRNA signature which could predict the prognosis of PC patients and help clinical decision-making. Firstly, cuproptosis-related lncRNAs were identified in the TCGA-PAAD database. Next, a cuproptosis-related lncRNA signature based on five lncRNAs was established. Besides, the ICGC cohort and our samples from 30 PC patients served as external validation groups to verify the predictive power of the risk signature. Then, the expression of CASC8 was verified in PC samples, scRNA-seq dataset CRA001160, and PC cell lines. The correlation between CASC8 and cuproptosis-related genes was validated by Real-Time PCR. Additionally, the roles of CASC8 in PC progression and immune microenvironment characterization were explored by loss-of-function assay. As showed in the results, the prognosis of patients with higher risk scores was prominently worse than that with lower risk scores. Real-Time PCR and single cell analysis suggested that CASC8 was highly expressed in pancreatic cancer and related to cuproptosis. Additionally, gene inhibition of CASC8 impacted the proliferation, apoptosis and migration of PC cells. Furthermore, CASC8 was demonstrated to impact the expression of CD274 and several chemokines, and serve as a key indicator in tumor immune microenvironment characterization. In conclusion, the cuproptosis-related lncRNA signature could provide valuable indications for the prognosis of PC patients, and CASC8 was a candidate biomarker for not only predicting the progression of PC patients but also their antitumor immune responses.

High FAM111B expression predicts aggressive clinicopathologic features and poor prognosis in ovarian cancer

BACKGROUNDS

Ovarian cancer (OC) is the second most common gynecological tumor with the highest mortality rate worldwide. High FAM111B expression has been reported as a predictor of poor prognosis in other cancers, but its correlation with OC has not been reported.

METHODS

Immunohistochemistry of tissue microarrays was performed to detect FAM111B expression levels in 141 OC patient tissues. The prognostic value of FAM111B was determined by Kaplan-Meier survival analysis, and correlations between FAM111B expression and clinicopathologic features were investigated by the Clu-square test. The significance of FAM111B expression was verified bioinformatically using the Gene Expression Omnibus database. Protein-protein interaction were performed to explore downstream mechanisms of FAM111B in OC.

RESULTS

Among 141 OC patients, FAM111B was positively expressed in 87.23%, 58.16%, and 87.94%; and highly expressed in 8.51%, 17.02%, and 19.86%, as evaluated by cytoplasmic, nuclear, and combined cytoplasmic/nuclear staining. FAM111B expression was positively correlated with the expression of tumor protein markers KI67, EGFR, and PDL-1. Patients with high FAM111B expression had aggressive clinicopathologic features and shorter overall survival (P value 0.0428, 0.0050, 0.0029) and progression-free survival (P value 0.0251, 0.012, 0.0596) compared to the low FAM111B expression group for cytoplasmic, nuclear, and combined cytoplasmic/nuclear groups, respectively. These results were verified using patient data from the Gene Expression Omnibus. Seventeen genes co-expressed with FAM111B were primarily involved in "negative regulation of histone modification", "hippo signaling" and "inner ear receptor cell differentiation".

CONCLUSIONS

High FAM111B expression may serve as a novel prognostic predictor and molecular therapeutic target for OC.

Profiling of a novel circadian clock-related prognostic signature and its role in immune function and response to molecular targeted therapy in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PADA) represents a devastating type of pancreatic cancer with high mortality. Defining a prognostic gene signature that can stratify patients with different risk will benefit cancer treatment strategies.

METHODS

Gene expression profiles of PADA patients were acquired from the Cancer Genome Atlas and Gene Expression Omnibus, including GSE62452 and GSE28735. Differential expression analysis was carried out using the package edgeR in R. Intro-tumor immune infiltrates were quantified by six different computational algorithms XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT. Biological processes were investigated based on R package "clusterProfiler".

RESULTS

13 genes (ARNTL2, BHLHE40, FBXL17, FBXL8, PPP1CB, RBM4B, ADRB1, CCAR2, CDK1, CSNK1D, KLF10, PSPC1, SIAH2) were eligible for the development of a prognostic gene signature. Performance of the prognostic gene signature was assessed in the discovery set (n = 210), validation set (n = 52), and two external data set (GSE62452, n = 65, and GSE28735, n = 84). Area under the curve (AUC) for predicting 3-year overall survival was 0.727, 0.732, 0.700, and 0.658 in the training set, the validation set, and the two test sets, respectively. KM curve revealed that the low-risk group had an improved prognosis than the high-risk group in all four datasets. PCA analysis demonstrated that the low-risk group was apparently separated from the high-risk group. CD8 T cell and B cell were significantly reduced in the high-risk group than in the low-risk group, while neutrophils were significantly augmented in the high-risk group than in the low-risk group. BMS-536924, Foretinib, Linsitinib, and Sabutoclax were more sensitive in the low-risk group, whereas Erlotinib was more effective in the high-risk group.

CONCLUSIONS

We successfully established and verified a novel circadian clock-related gene signature, which could stratify patients with different risk and be reflective of the therapeutic effect of molecular targeted therapy. Our findings could incorporate the pharmacological modulation of circadian clock into future therapeutic strategies.

Cuproptosis regulatory genes greatly contribute to clinical assessments of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common abdominal cancer with dissatisfactory therapeutic effects. The discovery of cuproptosis lights on new approach for cancer treatment and assessment. So far, there is extremely limited research investigating the roles of cuproptosis-related (CR) genes in cancers.

METHODS

A novel CR risk signature was constructed using the Lasso regression analysis. Its prognostic value was assessed via a series of survival analyses and validated in three GEO cohorts. The effects of CR risk signature on tumor immune microenvironment (TIM) were explored through CIBERSORT, ESTIMATE, and ssGSEA algorithms. Using GESA, we investigated its impacts on various metabolism process. The somatic mutation features of CR signature genes were also explored via cBioPortal database. Using tumor mutation burden, expressions of immune checkpoints, TIDE score, IMvigor 210 cohort, and GSE109211 dataset, we explored the potential associations of CR risk score with the efficacy of immune checkpoint inhibitors (ICIs) and sorafenib. Finally, the biofunctions of DLAT in HCC cells were ascertained through qPCR, immunohistochemistry, colony formation, and Transwell assays.

RESULTS

FDX1, DLAT, CDKN2A and GLS constituted the CR risk signature. CR risk signature possessed high prognostic value and was also applicable to three validation cohorts. Meanwhile, it could improve the accuracy and clinical making-decision benefit of traditional prognostic model. Moreover, high CR risk was indicative of unfavorable anti-tumor immune response and active metabolisms of glycolysis and nucleotide. As for therapeutic correlation, CR risk score was a potential biomarker for predicting the efficacy of ICIs and sorafenib. Through qPCR and immunohistochemistry detection in clinical samples, we reconfirmed DLAT was significantly upregulated in HCC samples. Overexpression of DLAT could promote the proliferation, migration, and invasion of HepG2 and HuH-7 cells.

CONCLUSIONS

The novel CR risk signature greatly contributed to the clinical assessment of HCC. Cuproptosis regulatory gene DLAT possessed cancer-promoting capacities and was expected to be a promising therapeutic target for HCC.

Immune infiltration and a necroptosis-related gene signature for predicting the prognosis of patients with cervical cancer

Background: Cervical cancer (CC), the fourth most common cancer among women worldwide, has high morbidity and mortality. Necroptosis is a newly discovered form of cell death that plays an important role in cancer development, progression, and metastasis. However, the expression of necroptosis-related genes (NRGs) in CC and their relationship with CC prognosis remain unclear. Therefore, we screened the signature NRGs in CC and constructed a risk prognostic model. Methods: We downloaded gene data and clinical information of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from The Cancer Genome Atlas (TCGA) database. We performed functional enrichment analysis on the differentially expressed NRGs (DENRGs). We constructed prognostic models and evaluated them by Cox and LASSO regressions for DENRGs, and validated them using the International Cancer Genome Consortium (ICGC) dataset. We used the obtained risk score to classify patients into high- and low-risk groups. We employed the ESTIMATE and single sample gene set enrichment analysis (ssGSEA) algorithms to explore the relationship between the risk score and the clinical phenotype and the tumor immune microenvironment. Results: With LASSO regression, we established a prognostic model of CC including 16 signature DENRGs (TMP3, CHMP4C, EEF1A1, FASN, TNF, S100A10, IL1A, H1.2, SLC25A5, GLTP, IFNG, H2AC13, TUBB4B, AKNA, TYK2, and H1.5). The risk score was associated with poor prognosis in CC. Survival was lower in the high-risk group than the low-risk group. The nomogram based on the risk score, T stage, and N stage showed good prognostic predictive power. We found significant differences in immune scores, immune infiltration analysis, and immune checkpoints between the high- and low-risk groups (p < 0.05). Conclusion: We screened for DENRGs based on the TCGA database by using bioinformatics methods, and constructed prognostic models based on the signature DENRGs, which we confirmed as possibly having important biological functions in CC. Our study provides a new perspective on CC prognosis and immunity, and offers a series of new targets for future treatment.

Novel insights into the interplay between m6A modification and programmed cell death in cancer

N6-methyladenosine (m6A) methylation, the most prevalent and abundant RNA modification in eukaryotes, has recently become a hot research topic. Several studies have indicated that m6A modification is dysregulated during the progression of multiple diseases, especially in cancer development. Programmed cell death (PCD) is an active and orderly method of cell death in the development of organisms, including apoptosis, autophagy, pyroptosis, ferroptosis, and necroptosis. As the study of PCD has become increasingly profound, accumulating evidence has revealed the mutual regulation of m6A modification and PCD, and their interaction can further influence the sensitivity of cancer treatment. In this review, we summarize the recent advances in m6A modification and PCD in terms of their interplay and potential mechanisms, as well as cancer therapeutic resistance. Our study provides promising insights and future directions for the examination and treatment of cancers.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer

Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.

A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis

Background

As a tumor type with high mortality and poor therapeutic effect, the pathogenesis of pancreatic cancer is still unclear. It is necessary to explore the significance of necroptosis in pancreatic cancer.

Methods

Pancreatic cancer transcriptome data were obtained from the TCGA database, ICGC database, and GSE85916 in the GEO database. The TCGA cohort was set as a training cohort, while the ICGC and GSE85916 cohort were set as the validation cohorts. Single-cell sequencing data of pancreatic cancer were obtained from GSE154778 in the GEO database. The genes most associated with necroptosis were identified by weighted co-expression network analysis and single-cell sequencing analysis. COX regression and Lasso regression were performed for these genes, and the prognostic model was established. By calculating risk scores, pancreatic cancer patients could be divided into NCPTS_high and NCPTS_low groups, and survival analysis, immune infiltration analysis, and mutation analysis between groups were performed. Cell experiments including gene knockdown, CCK-8 assay, clone formation assay, transwell assay and wound healing assay were conducted to explore the role of the key gene EPS8 in pancreatic cancer. PCR assays on clinical samples were further used to verify EPS8 expression.

Results

We constructed the necroptosis-related signature in pancreatic cancer using single-cell sequencing analysis and transcriptome analysis. The calculation formula of risk score was as follows: NCPTS = POLR3GL * (-0.404) + COL17A1 * (0.092) + DDIT4 * (0.007) + PDE4C * (0.057) + CLDN1 * 0.075 + HMGA2 * 0.056 + CENPF * 0.198 +EPS8 * 0.219. Through this signature, pancreatic cancer patients with different cohorts can be divided into NCPTS_high and NCPTS_low group, and the NCPTS_high group has a significantly poorer prognosis. Moreover, there were significant differences in immune infiltration level and mutation level between the two groups. Cell assays showed that in CAPAN-1 and PANC-1 cell lines, EPS8 knockdown significantly reduced the viability, clonogenesis, migration and invasion of pancreatic cancer cells. Clinical PCR assay of EPS8 expression showed that EPS8 expression was significantly up-regulated in pancreatic cancer (*P<0.05).

Conclusion

Our study can provide a reference for the diagnosis, treatment and prognosis assessment of pancreatic cancer.

Pan-cancer analysis of a novel indicator of necroptosis with its application in human cancer

As a type of programmed cell death, necroptosis is thought to play a dual role in tumorigenesis. However, a comprehensive assessment of necroptosis-related regulators across human cancers has not been reported. Therefore, in this study, we established a quantitative index to evaluate the necroptosis rate and determine its correlations with clinical prognosis, signaling pathways and molecular features, immune cell infiltration and regulation, immunotherapy, and chemotherapy sensitivity across cancers. Our results indicated that the necroptosis score can act as a favorable or risky prognostic factor in various cancer types. A gene set variation analysis suggested that necroptosis is significantly associated with immune- and inflammation-related signaling pathways, cell growth and apoptosis, and energy metabolism. Furthermore, necroptosis can affect the tumor microenvironment and immunity regulation, and the effect of necroptosis on immunity is different in different tumor types. There is crosstalk between components of necroptosis, pyroptosis, ferroptosis and autophagy pathways in multiple types of cancers. Finally, the necroptosis rate can be an indicator of immunotherapy effectiveness in multiple cancers and can affect the chemotherapy sensitivity of cancer cells. Our study presents a characterization of necroptosis across human cancers, highlights the potential necroptotic effects on immune regulation, and provides new insights into the development of individualized tumor treatments and clinical applications of immunotherapy.

A novel necroptosis-related gene signature associated with immune landscape for predicting the prognosis of papillary thyroid cancer

Background: Necroptosis, a type of programmed cell death, has been implicated in a variety of cancer-related biological processes. However, the roles of necroptosis-related genes in thyroid cancer yet remain unknown.

Methods: A necroptosis-related gene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis. The predictive value of the prognostic signature was validated in an internal cohort. Additionally, the single-sample gene set enrichment analysis (ssGSEA) was used to examine the relationships between necroptosis and immune cells, immunological functions, and immune checkpoints. Next, the modeled genes expressions were validated in 96 pairs of clinical tumor and normal tissue samples. Finally, the effects of modeled genes on PTC cells were studied by RNA interference approaches in vitro.

Results: In this study, the risk signature of seven necroptosis-related genes was created to predict the prognosis of papillary thyroid cancer (PTC) patients, and all patients were divided into high- and low-risk groups. Patients in the high-risk group fared worse in terms of overall survival than those in the low-risk group. The area under the curve (AUC) of the receiving operating characteristic (ROC) curves proved the predictive capability of created signature. The risk score was found to be an independent risk factor for prognosis in multivariate Cox analysis. The low-risk group showed increased immune cell infiltration and immunological activity, implying that they might respond better to immune checkpoint inhibitor medication. Next, GEO database and qRT-PCR in 96 pairs of matched tumorous and non-tumorous tissues were used to validate the expression of the seven modeled genes in PTCs, and the results were compatible with TCGA database. Finally, overexpression of IPMK, KLF9, SPATA2 could significantly inhibit the proliferation, invasion and migration of PTC cells.

Conclusion: The created necroptosis associated risk signature has the potential to have prognostic capability in PTC for patient outcome. The findings of this study could pave the way for further research into the link between necroptosis and tumor immunotherapy.

Identification and analysis of necroptosis-associated signatures for prognostic and immune microenvironment evaluation in hepatocellular carcinoma

Background

Hepatocellular carcinoma remains the third most common cause of cancer-related deaths worldwide. Although great achievements have been made in resection, chemical therapies and immunotherapies, the pathogenesis and mechanism of HCC initiation and progression still need further exploration. Necroptosis genes have been reported to play an important role in HCC malignant activities, thus it is of great importance to comprehensively explore necroptosis-associated genes in HCC.

Methods

We chose the LIHC cohort from the TCGA, ICGC and GEO databases for this study. ConsensusClusterPlus was adopted to identify the necroptosis genes-based clusters, and LASSO cox regression was applied to construct the prognostic model based on necroptosis signatures. The GSEA and CIBERSORT algorithms were applied to evaluate the immune cell infiltration level. QPCR was also applied in this study to evaluate the expression level of genes in HCC.

Results

We identified three clusters, C1, C2 and C3. Compared with C2 and C3, the C1 cluster had the shortest overall survival time and highest immune score. The C1 was samples were significantly enriched in cell cycle pathways, some tumor epithelial-mesenchymal transition related signaling pathways, among others. The DEGs between the 3 clusters showed that C1 was enriched in cell cycle, DNA replication, cellular senescence, and p53 signaling pathways. The LASSO cox regression identified KPNA2, SLC1A5 and RAMP3 as prognostic model hub genes. The high risk-score subgroup had an elevated expression level of immune checkpoint genes and a higher TIDE score, which suggested that the high risk-score subgroup had a lower efficiency of immunotherapies. We also validated that the necroptosis signatures-based risk-score model had powerful prognosis prediction ability.

Conclusion

Based on necroptosis-related genes, we classified patients into 3 clusters, among which C1 had significantly shorter overall survival times. The proposed necroptosis signatures-based prognosis prediction model provides a novel approach in HCC survival prediction and clinical evaluation.

A panel of necroptosis-related genes predicts the prognosis of pancreatic adenocarcinoma

•

The 5-NRGs signature can predict the prognosis of pancreatic adenocarcinoma.

•

The 5-NRGs signature reflects the immune landscape of pancreatic adenocarcinoma.

•

The 5-NRGs can be detected in exosomes of serum using RT-qPCR method.

Pancreatic adenocarcinoma (PAAD) has become one of the deadliest malignancies in the world. Since necroptosis plays a crucial role in regulating the immune system, it is necessary to develop novel prognostic biomarkers associated with necroptosis and explore its potential role in PAAD. The transcriptome RNA-seq data of PAAD were downloaded from the TCGA and GTEx databases. A prognostic signature was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression, and its prognostic value was evaluated by nomogram and validated in an independent GEO cohort. We identified a total of 24 differentially expressed NRGs in PAAD, and constructed a prognostic signature with 5 NRGs, which showed good performance in predicting the prognosis of PAAD patients. The ROC curves for 1-, 3-, and 5-year survival rate were 0.652, 0.778, and 0.817, respectively. This prognostic signature showed consistent prognosis prediction in an independent patient cohort. Furthermore, the correlations between 5-NRGs signature and TMB, MSI, histopathological classification, immune infiltration, immune types, and immunomodulators were all significant. Notably, the expression profiles of the five NRGs in exosomes of serum were consistent with their expression in tumor tissues. These data suggested that the 5-NRGs signature is a promising biomarker for predicting the prognosis of PAAD.

Necroptosis throws novel insights on patient classification and treatment strategies for hepatocellular carcinoma

Introduction

Necroptosis is a novel pattern of immunogenic cell death and has triggered an emerging wave in antitumor therapy. More evidence has suggested the potential associations between necroptosis and intra-tumoral heterogeneity. Currently, the underlying role of necroptosis remains elusive in hepatocellular carcinoma (HCC) at antitumor immunity and inter-tumoral heterogeneity.

Methods

This study enrolled a total of 728 HCC patients and 139 immunotherapy patients from eight public datasets. The consensus clustering approach was employed to depict tumor heterogeneity of cancer necroptosis. Subsequently, our study further decoded the heterogeneous clinical outcomes, genomic landscape, biological behaviors, and immune characteristics in necroptosis subtypes. For each patient, providing curative clinical recommendations and developing potential therapeutic drugs were used to promote precise medicine.

Results

With the use of the weighted gene coexpression network analysis (WGCNA) algorithm, necroptosis-associated long non-coding RNAs (lncRNAs) (NALRs) were identified in HCC. Based on the NALR expression, two heterogeneous subtypes were decoded with distinct clinical outcomes. Compared to patients in C1, patients in C2 harbored superior pathological stage and presented more unfavorable overall survival and recurrence-free survival. Then, the robustness and reproducibility of necroptosis subtypes were further validated via the nearest template prediction (NTP) approach and classical immune phenotypes. Through comprehensive explorations, C1 was characterized by enriched immune-inflammatory and abundant immune infiltration, while C2 possessed elevated proliferative and metabolic activities and highly genomic instability. Moreover, our results indicated that C1 was more prone to obtain desirable benefits from immunotherapy. For patients in C2, numerous underlying therapeutic agents were developed, which might produce significant efficacy.

Conclusion

This study identified two necroptosis subtypes with distinct characteristics, decoding the tumor heterogeneity. For an individualized patient, our work tailored corresponding treatment strategies to improve clinical management.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Identification and Validation of Prognosis-Related Necroptosis Genes for Prognostic Prediction in Hepatocellular Carcinoma

Background

The prediction of hepatocellular carcinoma (HCC) survival is challenging because of its rapid progression. In recent years, necroptosis was found to be involved in the progression of multiple cancer types. However, the role of necroptosis in HCC remains unclear.

Methods

Clinicopathological parameters and transcriptomic data of 370 HCC patients were obtained from TCGA-LIHC dataset. Prognosis-related necroptosis genes (PRNGs) were identified and utilized to construct a LASSO risk model. The GEO cohorts (GSE54236 and GSE14520) were used for external validation. We evaluated the distribution of HCC patients, the difference in prognosis, and the accuracy of the prognostic prediction of the LASSO risk model. The immune microenvironment and functional enrichment of different risk groups were further clarified. Finally, we performed a drug sensitivity analysis on the PRNGs that constructed the LASSO model and verified their mRNA expression levels in vitro. Results: A total of 48 differentially expressed genes were identified, 23 of which were PRNGs. We constructed the LASSO risk model using nine genes: SQSTM1, FLT3, HAT1, PLK1, MYCN, KLF9, HSP90AA1, TARDBP, and TNFRSF21. The outcomes of low-risk patients were considerably better than those of high-risk patients in both the training and validation cohorts. In addition, stronger bile acid metabolism, xenobiotic metabolism, and more active immune cells and immune functions were observed in low-risk patients, and high expressions of TARDBP, PLK1, and FLT3 were associated with greater drug sensitivity. With the exception of FLT3, the mRNA expression of the other eight genes was verified in Huh7 and 97H cells. Conclusions. The PRNG signature provides a novel and effective method for predicting the outcome of HCC as well as potential targets for further research.

Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality and is usually associated with therapeutic resistance and poor prognosis because of individual biological heterogeneity. There is an unmet need to screen for reliable parameters, especially immunotherapy-related biomarkers to predict the patient’s outcomes. Necroptosis is a special caspase-independent form of necrotic cell death associated with the pathogenesis, progression, and prognosis of multiple tumors but the potential connection between necroptosis-related genes (NRGs) and LUAD still remains unclear. In this study, we expounded mutational and transcriptional alterations of 67 NRGs in 522 LUAD samples and proposed a consensus-clustering subtype of these patients into two cohorts with distinct immunological and clinical prognosis characteristics. Cluster B patients were associated with a better prognosis and characterized by relatively lower expression of NRGs, higher immune scores in the tumor microenvironment (TME), more mild clinical stages, and downregulated expression of immunotherapy checkpoints. Subsequently, the NRG score was further established to predict the overall survival (OS) of LUAD patients using univariate Cox, LASSO, and multivariate Cox regression analyses. The immunological characteristics and potential predictive capability of NRG scores were further validated by 583 LUAD patients in external datasets. In addition to better survival and immune-activated conditions, low-NRG-score cohorts exhibited a significant positive correlation with the mRNA stem index (mRNAsi) and tumor mutation burden (TMB) levels. Combined with classical clinical characteristics and NRG scores, we successfully defined a novel necroptosis-related nomogram to accurately predict the 1/3/5-year survival rate of individual LUAD patients, and the potential predictive capability was further estimated and validated in multiple test datasets with high AUC values. Integrated transcriptomic analysis helps us seek vital NRGs and supplements a novel clinical application of NRG scores in predicting the overall survival and therapeutic benefits for LUAD patients.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

Background

Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown.

Methods

The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes.

Results

Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs.

Conclusion

Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma

Background: Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. Methods: We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature (NLPS). We used univariate and multivariate regression to assess the independent prognostic value of NLPS. Based on the optimal cut-off, patients were divided into high- and low-risk groups. In addition, the immune profile, multi-omics alteration, and pharmacological landscape of NLPS were further revealed. Results: A total of 21 NRLs associated with survival were identified by univariate regression in four cohorts. We constructed and validated a best prognostic model (NLPS). Compared to the low-risk group, patients in the high group demonstrated a more dismal prognosis. After adjusting for clinical features by multivariate analysis, NLPS still displayed independent prognostic value. Additionally, further analysis found that patients in the low-risk group showed more abundant immune cell infiltration and immunotherapy response. In contrast, patients in the high-risk group were more sensitive to multiple chemotherapeutic agents. Conclusion: As a promising tool, the establishment of NLPS provides guidance and assistance in the clinical management and personalized treatment of HNSCC.