The Identification and Validation of Two Heterogenous Subtypes and a Risk Signature Based on Ferroptosis in Hepatocellular Carcinoma

Machine Learning and Weighted Gene Coexpression Network-Based Identification of Biomarkers Predicting Immune Profiling and Drug Resistance in Lung Adenocarcinoma

Background: The prognosis for lung adenocarcinoma (LUAD) is poor, and the recurrence rate is high. Thus, to evaluate patients' prognoses and direct therapy choices, new prognostic markers are desperately needed. Methods: First, gene modules associated with LUAD were identified by weighted gene coexpression network analysis (WGCNA) analysis. The expression profiles obtained were intersected with the differential expressed genes taken between LUAD samples and paracancerous samples. Afterward, stepwise regression analysis and the LASSO were used to compress the genes further, and a risk model was created. Furthermore, a nomogram based on risk scores and clinical features was created to validate the model. After that, the distinctions between the pertinent biological processes and signaling pathways among the various subgroups were investigated. Additionally, drug sensitivity testing, immunotherapy, immune infiltration analysis, and enrichment analysis were carried out. Finally, the biological role of ANLN in LUAD was explored by qPCR, cell scratch assay, and transwell. Results: A total of 257 intersected genes were obtained by taking the intersection of the differential genes between 2866 LUAD samples and paraneoplastic samples with the module genes after we screened two particular modules that had the strongest link with LUAD by WGCNA. ANLN, CASS4, and NMUR1 were found to be distinctive genes for the development of risk models after the intersecting genes were screened to find 176 genes linked to the prognosis for LUAD. Based on risk assessments, high- and low-risk groups of LUAD patients were divided. Low-risk patients exhibited a significantly higher overall survival (OS) than those in the high-risk group. Expression of model genes correlates with infiltration of the vast majority of immune cells. Significant differences in the biological pathways, immune microenvironment, and abundance of immune cell infiltration were found between the two groups. The drug sensitivity study showed that patients in the high-risk group had higher IC50 values for BMS-754807_2171 and Doramapimod_10424. Finally, in vitro experiments demonstrated that knocking down ANLN noticeably inhibited the viability, migration, and invasion of A549 cells. Conclusion: This study may provide a theoretical reference for future exploration of potential diagnostic and prognostic biomarkers for LUAD.

[Research progress on ferroptosis regulation in tumor immunity of hepatocellular carcinoma]

Ferroptosis is a form of regulated cell death, which is dependent on iron metabolism imbalance and characterized by lipid peroxidation. Ferroptosis plays a crucial role in various pathological processes. Studies have shown that the occurrence of ferroptosis is closely associated with the progression of hepatocellular carcinoma (HCC). Ferroptosis is involved in regulating the lipid metabolism, iron homeostasis, mitochondrial metabolism, and redox processes in HCC. Additionally, ferroptosis plays a key role in HCC tumor immunity by modulating the phenotype and function of various immune cells in the tumor microenvironment, affecting tumor immune escape and progression. Ferroptosis-induced lipid peroxidation and oxidative stress can promote the polarization of M1 macrophages and enhance the pro-inflammatory response in tumors, inhibiting immune suppressive cells such as myeloid-derived suppressor cells and regulatory T cells to disrupt their immune suppression function. The regulation of expression of ferroptosis-related molecules such as GPX4 and SLC7A11 not only affects the sensitivity of tumor cells to immunotherapy but also directly influences the activity and survival of effector cells such as T cells and dendritic cells, further enhancing or weakening host antitumor immune response. Targeting ferroptosis has demonstrated significant clinical potential in HCC treatment. Induction of ferroptosis by nanomedicines and molecular targeting strategies can directly kill tumor cells or enhance antitumor immune responses. The integration of multimodal therapies with immunotherapy further expands the application of ferroptosis targeting as a cancer therapy. This article reviews the relationship between ferroptosis and antitumor immune responses and the role of ferroptosis in HCC progression from the perspective of tumor immune microenvironment, to provide insights for the development of antitumor immune therapies targeting ferroptosis.

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

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

Targeting ferroptosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with complex survival mechanism and drug resistance, resulting in cancer-related high mortality in the world. Ferroptosis represents a form of regulated cell death, typically distinguished by iron-dependent lipid peroxidation. Cancer cells often employ antioxidant defenses to evade the harmful effects of excess iron. Recent research has proposed that directing interventions towards ferroptosis could serve as an effective strategy in curbing the proliferation and invasion of HCC. Immunotherapy has made some preliminary progress in the remodeling of immune microenvironment, but it has not completely inhibited HCC growth, invasion and drug resistance. Furthermore, ferroptosis is widely observed in the formation of immune microenvironment of HCC and mediates the response of many targeted drugs and immunotherapy. Clarifying the role of ferroptosis in these complex processes is expected to provide a new prospect for HCC treatment. In this review, we outline the mechanisms by which HCC develops invasiveness and drug resistance by evading iron-dependent death, and paint a comprehensive landscape of ferroptosis in different cell types in the HCC immune microenvironment.

A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker

Previous studies on the molecular classification of cholangiocarcinoma (CCA) focused on certain anatomical sites, and disregarded tissue contamination biases in transcriptomic profiles. We aim to provide universal molecular classification scheme and prognostic biomarker of CCAs across anatomical locations. Comprehensive bioinformatics analysis is performed on transcriptomic data from 438 CCA cases across various anatomical locations. After excluding CCA tumors showing normal tissue expression patterns, we identify two universal molecular subtypes across anatomical subtypes, explore the molecular, clinical, and microenvironmental features of each class. Subsequently, a 30-gene classifier and a biomarker (called "CORE-37") are developed to predict the molecular subtype of CCA and prognosis, respectively. Two subtypes display distinct molecular characteristics and survival outcomes. Key findings are validated in external cohorts regardless of the stage and anatomical location. Our study provides a CCA classification scheme that complements the conventional anatomy-based classification and presents a promising prognostic biomarker for clinical application.

A ferroptosis-related LncRNAs signature for predicting prognoses and screening potential therapeutic drugs in patients with lung adenocarcinoma: A retrospective study

BACKGROUND

Lung adenocarcinoma (LUAD) has a high mortality rate. Ferroptosis is linked to tumor initiation and progression.

AIMS

This study aims to develop prognostic models of ferroptosis-related lncRNAs, evaluate the correlation between differentially expressed genes and tumor microenvironment, and identify prospective drugs for managing LUAD.

METHODS AND RESULTS

In this study, transcriptomic and clinical data were downloaded from the TCGA database, and ferroptosis-related genes were obtained from the FerrDb database. Through correlation analysis, Cox analysis, and the LASSO algorithm for constructing a prognostic model, we found that ferroptosis-related lncRNA-based gene signatures (FLncSig) had a strong prognostic predicting ability in the LUAD patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments reconfirmed that ferroptosis is related to receptor-ligand activity, enzyme inhibitor activity, and the IL-17 signaling pathway. Next, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) algorithms, and pRRophetic were used to predict immunotherapy response and chemotherapy sensitivity. The IMvigor210 cohort was also used to validate the prognostic model. In the tumor microenvironment, Type_II_IFN_Response and HLA were found to be a group of low-risk pathways, while MHC_class_I was a group of high-risk pathways. Patients in the high-risk subgroup had lower TIDE scores. Exclusion, MDSC, CAF, and TAMM2 were significantly and positively correlated with risk scores. In addition, we found 15 potential therapeutic drugs for LUAD. Finally, differential analysis of stemness index based on mRNA expression (mRNAsi) indicated that mRNAsi was correlated with gender, primary tumor (T), distant metastasis (M), and the tumor, node, and metastasis (TNM) stage in LUAD patients.

CONCLUSIONS

In conclusion, the prognostic model based on FLncSig can alleviate the difficulty in predicting the prognosis and immunotherapy of LUAD patients. The identified FLncSig and the screened drugs exhibit potential for clinical application and provide references for the treatment of LUAD.

Oncogenic pathways refine a new perspective on the classification of hepatocellular carcinoma

BACKGROUND

Genetic alterations in oncogenic pathways are critical for cancer initiation, development, and treatment resistance. However, studies are limited regarding pathways correlated with prognosis, sorafenib, and transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC).

METHODS

In this study, 1928 patients from 11 independent datasets and a clinical in-house cohort were screened to explore the relationships among canonical pathway alterations in HCC patients. The molecular mechanisms, biological functions, immune landscape, and clinical outcomes among three heterogeneous phenotypes were further explored.

RESULTS

We charted the detailed landscape of pathway alterations in the TCGA-LIHC cohort, screened three pivotal pathways (p53, PI3K, and WNT), identified co-occurrence patterns and mutual exclusively, and stratified patients into three altered-pathway dominant phenotypes (ADPs). P53|PI3K ADP characterized by genomic instability (e.g., highest TMB, FGA, FGG, and FGL) indicated an unfavorable prognosis. While, patients in WNT ADP suggested a median prognosis, enhanced immune activation, and sensitivity to PD-L1 therapy. Remarkably, sorafenib and TACE exhibited efficacy for patients in WNT ADP and low frequent alteration phenotype (LFP). Additionally, ADP could work independently of common clinical traits (e.g., AJCC stage) and previous molecular classifications (e.g., iCluster, serum biomarkers).

CONCLUSIONS

ADP provides a new perspective for identifying patients at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in HCC.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

Chromatin regulators drive cancer epigenetic changes, and lncRNA can play an important role in epigenetic changes as chromatin regulators. We used univariate Cox, LASSO, and multivariate Cox regression analysis to select epigenetic-associated lncRNA signatures. Twenty-five epigenetic-associated lncRNA signatures (CELncSig) were identified to establish the immune prognostic model. According to Kaplan-Meier analysis, the overall survival of the high-risk group was significantly lower than the low-risk group. Receiver operating characteristic (ROC) curves, C-index, survival curve, nomogram, and principal component analysis (PCA) were performed to validate the risk model. In GO/KEGG analysis, differentially expressed lncRNAs were correlated with the PI3K-Akt pathway, suggesting that they were highly associated with the metastasis of LUAD. Interestingly, in the immune escape analysis, the TIDE score was lower, and the possibility of immune dysfunction is also slighter in the high-risk group, which means they still have the potential to receive immunotherapy. And CELncsig is highly correlated with immune pathways T_cell_co-inhibition and Check-point. Also, the IMvigor210 cohort analysis indicated that our risk-scoring model has significant potential clinical application value in lung cancer immunotherapy. And we also screened out ten potential chemotherapy agents using the 'pRRophetic' package.

TACE responser NDRG1 acts as a guardian against ferroptosis to drive tumorgenesis and metastasis in HCC

BACKGROUND

The treatment efficacy of transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC) varies widely between individuals. The aim of this study was to identify subtype landscapes and responser related to TACE, and further clarify the regulatory effect and corresponding mechanism of NDRG1 on HCC tumorgenesis and metastasis.

METHODS

The principal component analysis (PCA) algorithm was used to construct a TACE response scoring (TRscore) system. The random forest algorithm was applied to identify the TACE response-related core gene NDRG1 of HCC, and its role in the prognosis of HCC was explored. The role of NDRG1 in the progression and metastasis of HCC and functional mechanism were confirmed using several experimental methods.

RESULTS

Based on the GSE14520 and GSE104580 cohorts, we identified 2 TACE response-related molecular subtypes for HCC with significant differences in clinical features, and the TACE prognosis of Cluster A was significantly better than that of Cluster B (p < 0.0001). We then established the TRscore system and found that the low TRscore group showed a higher probability of survival and a lower rate of recurrence than the high TRscore group (p < 0.05) in both the HCC and TACE-treated HCC cohorts within the GSE14520 cohort. NDRG1 was determined to be the the hub gene associated with the TACE response of HCC and its high expression suggested a poor prognosis. Furthermore, The suppression of NDRG1 konckdown in tumorgenesis and metastasis of HCC was clarified in both vivo and vitro, which was importantly achieved through inducing ferroptosis in HCC cells, especially contributing to RLS3-induced ferroptosis.

CONCLUSION

The constructed TACE response-related molecular subtypes and TRscores can specifically and accurately predict TACE prognosis for HCC. In addition, the TACE response-related hub gene NDRG1 may act as a guardian against ferroptosis to drive tumorgenesis and metastasis in HCC, which laid a new foundation for the development of new potential targeted therapy strategies to improve disease prognosis in HCC patients.

M6A regulator-mediated immune infiltration and methylation modification in hepatocellular carcinoma microenvironment and immunotherapy

Introduction: Tremendous evidence indicates that N6-methyladenosine (m6A) epigenetic modification and m6A-related enzymes constitute a complex network, which jointly regulates prevailing pathological processes and various signaling pathways in humankind. Currently, the role of the m6A-mediated molecular regulatory network in hepatocellular carcinoma (HCC) remains elusive. Methods: We recruited expression and pathological files of 368 HCC patients from The Cancer Genome Atlas cohort. Four public datasets serve as external authentication sets for nearest template prediction (NTP) validation. The correlation between 35 regulators and their prognostic value was compared. Gene set variation analysis (GSVA) was used to explore the latent mechanism. Four independent algorithms (ssGSEA, xCell, MCP-counter, and TIMER) were used to calculate the ratio of tumor cells and non-tumor cells to evaluate the tumor immune microenvironment. The m6Ascore model was established by principal component analysis (PCA). Prediction of immunotherapy and potential drugs was performed using TIDE and SubMap. Results: A total of 35 m6A regulators were widely associated, most of which were risk factors for HCC patients. The m6A phenotypic-cluster revealed differences in regulator transcriptional level, gene mutation frequency, functional pathways, and immune cell infiltration abundance under distinct m6A patterns. As expected, the m6A gene cluster confirmed the aforementioned results. The m6Ascore model further found that patients in the high-m6Ascore group were associated with lower tumor purity, higher enrichment of immune and stromal cells, upregulation of metabolic pathways, lower expression of m6A regulators, and favorable outcomes. Low-m6Ascore patients were associated with adverse outcomes. Notably, low-m6Ascore patients might be more sensitive to anti-PD-L1 therapy. Conclusion: This study found that a classification model based on the m6A manner could predict HCC prognosis and response to immunotherapy for HCC patients, which might improve prognosis and contribute to clinical individualized decision-making.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance

The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.

Relationships of Cuproptosis-Related Genes With Clinical Outcomes and the Tumour Immune Microenvironment in Hepatocellular Carcinoma

Background: Cuproptosis is a recently identified form of regulated cell death that plays a critical role in the onset and progression of various cancers. However, the effects of cuproptosis-related genes (CRGs) on hepatocellular carcinoma (HCC) are poorly understood. This study aimed to identify the cuproptosis subtypes and established a novel prognostic signature of HCC. Methods: We collected gene expression data and clinical outcomes from the TCGA, ICGC, and GEO datasets, analysed and identified 16 CRGs and the different subtypes of cuproptosis related to overall survival (OS), and further examined the differences in prognosis and immune infiltration among the subtypes. Subtypes-related differentially expressed genes (DEGs) were employed to build a prognostic signature. The relationship of the signature with the immune landscape as well as the sensitivity to different therapies was explored. Moreover, a nomogram was constructed to predict the outcome based on different clinicopathological characteristics. Results: Three cuproptosis subtypes were identified on the basis of 16 CRGs, and subtype B had an advanced clinical stage and worse OS. The immune response and function in subtype B were significantly suppressed, which may be an important reason for its poor prognosis. Based on the DEGs among the three subtypes, a prognostic model of five CRGs was constructed in the training set, and its predictive ability was validated in two external validation sets. HCC patients were classified into high and low-risk subgroups according to the risk score, and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (p < 0.001). The independent predictive performance of the risk score was assessed and verified by multivariate Cox regression analysis (p < 0.001). We further created an accurate nomogram to improve the clinical applicability of the risk score, showing good predictive ability and calibration. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the high-risk group were found to be resistant to immunotherapy and a variety of chemotherapy drugs. Conclusion: Our study identified three cuproptosis subtypes and established a novel prognostic model that provides new insights into HCC subtype prognostic assessment and guides more effective treatment regimens.

Identification of cuprotosis-mediated subtypes, the development of a prognosis model, and influence immune microenvironment in hepatocellular carcinoma

Purpose

Cuprotosis is a newly discovered form of non-apoptotic regulated cell death and is characterized by copper-dependent and associated with mitochondrial respiration. However, the prognostic significance and function of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are unknown. This study aims to develop cuprotosis-mediated patterns-related gene (CMPRG) prediction models for the prognosis of patients with HCC, exploring the functional underlying the CRGs on the influence of tumor microenvironment (TME) features.

Experimental design

This study obtained transcriptome profiling and the corresponding clinical information from the TCGA and GEO databases. Besides, the Cox regression model with LASSO was implemented to build a multi-gene signature, which was then validated in an internal validation set and two external validation sets through Kaplan-Meier, DCA, and ROC analyses.

Results

According to the LASSO analysis, we screened out a cuprotosis-mediated pattern 5-gene combination (including PBK; MMP1; GNAZ; GPC1 and AKR1D1). A nomogram was constructed for the presentation of the final model. The ROC curve assessed the model’s predictive ability, which resulted in an area under the curve (AUC) values ranging from 0.604 to 0.787 underwent internal and two external validation sets. Meanwhile, the risk score divided the patients into two groups of high and low risk, and the survival rate of high-risk patients was significantly lower than that of low-risk patients (P<0.01). The risk score could be an independent prognostic factor in the multifactorial Cox regression analysis (P<0.01). Functional analysis revealed that immune status, mutational loads, and drug sensitivity differed between the two risk groups.

Conclusions

In summary, we identified three cuprotosis-mediated patterns in HCC. And CMPRGs are a promising candidate biomarker for HCC early detection, owing to their strong performance in predicting HCC prognosis and therapy. Quantifying cuprotosis-mediated patterns in individual samples may help improve the understanding of multiomic characteristics and guide the development of targeted therapy for HCC.

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.

The Pyroptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular Carcinoma

Pyroptosis was recently demonstrated to be an inflammatory form of gasdermin-regulated programmed cell death characterized by cellular lysis and the release of several proinflammatory factors and participates in tumorigenesis. However, the effects of pyroptosis-related long noncoding RNAs (lncRNAs) on hepatocellular carcinoma (HCC) have not yet been completely elucidated. Based on the regression coefficients of ZFPM2-AS1, KDM4A-AS1, LUCAT1, NRAV, CRYZL2P-SEC16B, AL031985.3, SNHG4, AL049840.5, AC008549.1, MKLN1-AS, AC099850.3, and LINC01224, HCC patients were classified into a low- or high-risk group. The high-risk score according to pyroptosis-related lncRNA signature was significantly associated with poor overall survival even after adjusting for age and clinical stage. Receiver operating characteristic curves and principal component analysis further supported the accuracy of the model. Our study revealed that a higher pyroptosis-related lncRNA risk score was significantly associated with tumor staging, pathological grade, and tumor-node-metastasis stages. The nomogram incorporating the pyroptosis-related lncRNA risk score and clinicopathological factors demonstrated good accuracy. Furthermore, we observed distinct tumor microenvironment cell infiltration characteristics between high- and low-risk tumors. Notably, based on the risk model, we found that the risk score is closely related to the expression of immune checkpoint genes, immune subtypes of tumors, and the sensitivity of HCC to chemotherapy drugs and immunotherapy. In conclusion, our novel risk score of pyroptosis-related lncRNA can serve as a promising prognostic biomarker for HCC patients and provide help for HCC patients to guide precision drug treatment and immunotherapy.

Identification and Validation of a GPX4-Related Immune Prognostic Signature for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a commonly occurring histological subtype of lung cancer. Glutathione peroxidase 4 (GPX4) is an important regulatory factor of ferroptosis and is involved in the development of many cancers, but its prognostic significance has not been systematically described in LUAD. In this study, we focused on developing a robust GPX4-related prognostic signature (GPS) for LUAD. Data for the training cohort was extracted from The Cancer Genome Atlas, and that for the validation cohort was sourced from the GSE72094 dataset including 863 LUAD patients. GPX4-related genes were screened out by weighted gene coexpression network analysis and Spearman’s correlation analysis. Then, Cox regression and least absolute shrinkage and selection operator regression analyses were employed to construct a GPS. The ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and GSEA were utilized to evaluate the relationship between GPS and the tumor microenvironment (TME). We constructed and validated a GPS premised on four GPX4-related genes (KIF14, LATS2, PRKCE, and TM6SF1), which could classify LUAD patients into low- and high-score cohorts. The high-risk cohort presented noticeably poorer overall survival (OS) as opposed to the low-risk cohort, meaning that the GPS may be utilized as an independent predictor of the OS of LUAD. The GPS was also adversely correlated with multiple tumor-infiltrating immune cells and immune-related processes and pathways in TME. Furthermore, greater sensitivity to erlotinib and lapatinib were identified in the low-risk cohort based on the GDSC database. Our findings suggest that the GPS can effectively forecast the prognosis of LUAD patients and may possibly regulate the TME of LUAD.

Identification and Validation of Immune Molecular Subtypes and Immune Landscape Based on Colon Cancer Cohort

Background

The incidence and mortality rates of colon adenocarcinoma (COAD), which is the fourth most diagnosed cancer worldwide, are high. A subset of patients with COAD has shown promising responses to immunotherapy. However, the percentage of patients with COAD benefiting from immunotherapy is unclear. Therefore, gaining a better understanding of the immune milieu of colon cancer could aid in the development of immunotherapy and suitable combination strategies.

Methods

In this study, gene expression profiles and clinical follow-up data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and molecular subtypes were identified using the ConsensusClusterPlus package in R. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of immune subtypes. The graph structure learning method was used to reduce the dimension to reveal the internal structure of the immune system. Weighted correlation network analysis (WGCNA) was performed to identify immune-related gene modules. Finally, western blotting was performed to verify the gene expression patterns in COAD samples.

Results

The results showed that 424 COAD samples could be divided into three subtypes based on 1921 immune cell-related genes, with significant differences in prognosis between subtypes. Furthermore, immune-related genes could be divided into five functional modules, each with a different distribution pattern of immune subtypes. Immune subtypes and gene modules were highly reproducible across many data sets. There were significant differences in the distribution of immune checkpoints, molecular markers, and immune characteristics among immune subtypes. Four core genes, namely, CD2, FGL2, LAT2, and SLAMF1, with prognostic significance were identified by WGCNA and univariate Cox analysis.

Conclusion

Overall, this study provides a conceptual framework for understanding the tumor immune microenvironment of colon cancer.

Construction of a Lactate-Related Prognostic Signature for Predicting Prognosis, Tumor Microenvironment, and Immune Response in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent primary malignancies with high heterogeneity in the urological system. Growing evidence implies that lactate is a significant carbon source for cell metabolism and plays a vital role in tumor development, maintenance, and therapeutic response. However, the global influence of lactate-related genes (LRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response has not been comprehensively elucidated in patients with KIRC. In the present study, we collected RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts. Unsupervised clustering of 17 differentially expressed LRG profiles divided the samples into three clusters with distinct immune characteristics. Three genes (FBP1, HADH, and TYMP) were then identified to construct a lactate-related prognostic signature (LRPS) using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The novel signature exhibited excellent robustness and predictive ability for the overall survival of patients. In addition, the constructed nomogram based on the LRPS-based risk scores and clinical factors (age, gender, tumor grade, and stage) showed a robust predictive performance. Furthermore, patients classified by risk scores had distinguishable immune status, tumor mutation burden, response to immunotherapy, and sensitivity to drugs. In conclusion, we developed an LRPS for KIRC that was closely related to the immune landscape and therapeutic response. This LRPS may guide clinicians to make more precise and personalized treatment decisions for KIRC patients.

Long noncoding RNA LINC01606 protects colon cancer cells from ferroptotic cell death and promotes stemness by SCD1-Wnt/β-catenin-TFE3 feedback loop signalling

Background

Ferroptosis is principally caused by iron catalytic activity and intracellular lipid peroxidation. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis. However, the potential interplay between lncRNA LINC01606 and ferroptosis in colon cancer remains elusive.

Methods

The expression level of LNC01606 in colon cancer tissue was detected by quantitative real‐time polymerase chain reaction. The functional role of LNC01606 was investigated by gain‐ and loss‐of‐function assays both in vitro and in vivo. The LINC01606‐SCD1‐Wnt/β‐catenin‐TFE3 axis were screened and validated by DNA/RNA pull down, gas chromatography‐mass spectrometry, RNA immunoprecipitation and dual‐luciferase reporter.

Results

The expression of lncRNA LINC01606 was frequently upregulated in human colon cancer and strongly associated with a poor prognosis. LINC01606 functioned as an oncogene and promotes colon cancer cell growth, invasion and stemness both in vitro and in vivo. Moreover, LINC01606 protected colon cancer cells from ferroptosis by decreasing the concentration of iron, lipid reactive oxygen species, mitochondrial superoxide and increasing mitochondrial membrane potential. Mechanistically, LINC01606 enhanced the expression of stearoyl‐CoA desaturase 1 (SCD1), serving as a competing endogenous RNA to modulate miR‐423‐5p expression, subsequently activating the canonical Wnt/β‐catenin signaling, and transcription factor binding to IGHM enhancer 3 (TFE3) increased LINC01606 transcription after recruitment to the promoter regions of LINC01606. Furthermore, we confirmed that upregulated LINC01606 and Wnt/β‐catenin formed a positive feedback regulatory loop, further inhibiting ferroptosis and enhancing stemness.

Conclusions

LINC01606 functions as an oncogene to facilitate tumor cell stemness, proliferation and inhibit ferroptosis and is a promising therapeutic target for colon cancer.

Apoptosis-Related Signature Predicts Prognosis and Immune Microenvironment Infiltration in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), a malignancy with high incidence and mortality rates worldwide, contains multiple genomic and epigenomic abnormalities. And the useful tumor markers associated with these abnormalities need further investigation. Whereas apoptosis is a form of programmed cell death, the expression of apoptosis-related genes in LUAD and its relationship with prognosis is unclear. In the present study, we identified 64 differentially expressed apoptosis-related genes (DEARGs) that were differentially expressed between LUAD tissue and normal lung tissue. Based on these DEARGs, all LUAD cases were classified into two subtypes using The Cancer Genome Atlas (TCGA) cohort to assess the prognostic value of apoptosis-related genes for survival. An 11-gene signature was established by applying the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression method to construct a multigene prediction model and classify all LUAD patients in the TCGA cohort into high or low AS-score groups. Patients in the low AS-score group had significantly higher survival and prognosis than those in the high AS-score group. Taking the median risk score of the AS-score, LUAD patients in the GSE68465 cohort were divided into two risk groups, low and high. The overall survival (OS) time was longer in the low AS-score group. Combined with clinical characteristics, the AS-score was an independent predictor of LUAD patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that the differential genes between the two groups were mainly enriched in cellular immunity. Further analysis revealed higher immune checkpoint protein expression and higher tumor mutational burden (TMB) in the high AS-score group, suggesting better efficacy of immunotherapy in the high AS-score group than the low AS-score group. And the high AS-score group was better in chemotherapy and targeted therapy efficiency. In conclusion, the AS-score constructed based on apoptosis-related genes can predict the prognosis of LUAD patients and provide some guidance for the antitumor treatment of LUAD patients.

Ferroptosis in Hepatocellular Carcinoma: Mechanisms, Drug Targets and Approaches to Clinical Translation

Ferroptosis, an iron and reactive oxygen species (ROS)-dependent non-apoptotic type of regulated cell death, is characterized by a massive iron overload and peroxidation of polyunsaturated fatty acids (PUFAs), which finally results in cell death. Recent studies suggest that ferroptosis can influence carcinogenesis negatively and therefore may be used as a novel anti-cancer strategy. Hepatocellular carcinoma (HCC) is a deadly malignancy with poor chances of survival and is the second leading cause of cancer deaths worldwide. Diagnosis at an already late stage and general resistance to current therapies may be responsible for the dismal outcome. As the liver acts as a key factor in iron metabolism, ferroptosis is shown to play an important role in HCC carcinogenesis and, more importantly, may hold the potential to eradicate HCC. In this review, we summarize the current knowledge we have of the role of ferroptosis in HCC and the application of ferroptosis as a therapy option and provide an overview of the potential translation of ferroptosis in the clinical practice of HCC.

Multi-omics landscape and clinical significance of a SMAD4-driven immune signature: Implications for risk stratification and frontline therapies in pancreatic cancer

SMAD4 mutation was recently implicated in promoting invasion and poor prognosis of pancreatic cancer (PACA) by regulating the tumor immune microenvironment. However, SMAD4-driven immune landscape and clinical significance remain elusive. In this study, we applied the consensus clustering and weighted correlation network analysis (WGCNA) to identify two heterogeneous immune subtypes and immune genes. Combined with SMAD4-driven genes determined by SMAD4 mutation status, a SMAD4-driven immune signature (SDIS) was developed in ICGC-AU2 (microarray data) via machine learning algorithm, and then was validated by RNA-seq data (TCGA, ICGC-AU and ICGC-CA) and microarray data (GSE62452 and GSE85916). The high-risk group displayed a worse prognosis, and multivariate Cox regression indicated that SDIS was an independent prognostic factor. In six cohorts, SDIS also displayed excellent accuracy in predicting prognosis. Moreover, the high-risk group was characterized by higher frequencies of TP53/CDKN2A mutations and SMAD4 deletion, superior immune checkpoint molecules expression and more sensitive to chemotherapy and immunotherapy. Meanwhile, the low-risk group was significantly enriched in metabolism-related pathways and suggested the potential to target tumor metabolism to develop specific drugs. Overall, SDIS could robustly predict prognosis in PACA, which might serve as an attractive platform to further tailor decision-making in chemotherapy and immunotherapy in clinical settings.

Identification of Subtypes and a Delayed Graft Function Predictive Signature Based on Ferroptosis in Renal Ischemia-Reperfusion Injury

Renal ischemia-reperfusion injury (IRI) is an inevitable process in kidney transplantation, leading to acute kidney injury, delayed graft function (DGF), and even graft loss. Ferroptosis is an iron-dependent regulated cell death in various diseases including IRI. We aimed to identify subtypes of renal IRI and construct a robust DGF predictive signature based on ferroptosis-related genes (FRGs). A consensus clustering analysis was applied to identify ferroptosis-associated subtypes of 203 renal IRI samples in the GSE43974 dataset. The FRG-associated DGF predictive signature was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO), and its robustness was further verified in the validation set GSE37838. The present study revealed two ferroptosis-related patient clusters (pBECN1 and pNF2 cluster) in renal IRI samples based on distinct expression patterns of BECN1 and NF2 gene clusters. Cluster pBECN1 was metabolically active and closely correlated with less DGF, while pNF2 was regarded as the metabolic exhausted subtype with higher incidence of DGF. Additionally, a six-gene (ATF3, SLC2A3, CXCL2, DDIT3, and ZFP36) ferroptosis-associated signature was constructed to predict occurrence of DGF in renal IRI patients and exhibited robust efficacy in both the training and validation sets. High-risk patients tended to have more infiltration of dendritic cells, macrophages, and T cells, and they had significantly enriched chemokine-related pathway, WNT/β-catenin signaling pathway, and allograft rejection. Patients with low risks of DGF were associated with ferroptosis-related pathways such as glutathione and fatty acid metabolism pathways. In conclusion, patient stratification with distinct metabolic activities based on ferroptosis may help distinguish patients who may respond to metabolic therapeutics. Moreover, the DGF predictive signature based on FRGs may guide advanced strategies toward prevention of DGF in the early stage.

Integrated Analysis of Multi-Omics Alteration, Immune Profile, and Pharmacological Landscape of Pyroptosis-Derived lncRNA Pairs in Gastric Cancer

Background: Recent evidence demonstrates that pyroptosis-derived long non-coding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNAs (PDLs) in gastric cancer (GC) remain elusive.

Methods: We comprehensively analyzed the multi-omics data of 839 GC patients from three independent cohorts. The previous gene set enrichment analysis embedding algorithm was utilized to identify PDLs. A gene pair pipeline was developed to facilitate clinical translation via qualitative relative expression orders. The LASSO algorithm was used to construct and validate a pyroptosis-derived lncRNA pair prognostics signature (PLPPS). The associations between PLPPS and multi-omics alteration, immune profile, and pharmacological landscape were further investigated.

Results: A total of 350 PDLs and 61,075 PDL pairs in the training set were generated. Cox regression revealed 15 PDL pairs associated with overall survival, which were utilized to construct the PLPPS model via the LASSO algorithm. The high-risk group demonstrated adverse prognosis relative to the low-risk group. Remarkably, genomic analysis suggested that the lower tumor mutation burden and gene mutation frequency (e.g., TTN, MUC16, and LRP1B) were found in the high-risk group patients. The copy number variants were not significantly different between the two groups. Additionally, the high-risk group possessed lower immune cell infiltration abundance and might be resistant to a few chemotherapeutic drugs (including cisplatin, paclitaxel, and gemcitabine).

Conclusion: PDLs were closely implicated in the biological process and prognosis of GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options

Background

Stemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.

Methods

This study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.

Results

Based on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.

Conclusions

This study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

Clinical Significance and Immune Landscape of a Pyroptosis-Derived LncRNA Signature for Glioblastoma

Introduction: Pyroptosis was recently implicated in the initiation and progression of tumors, including glioblastoma (GBM). This study aimed to explore the clinical significance of pyroptosis-related lncRNAs (PRLs) in GBM.

Methods: Three independent cohorts were retrieved from the TCGA and CGGA databases. The consensus clustering and weighted gene coexpression network analysis (WGCNA) were applied to identify PRLs. The LASSO algorithm was employed to develop and validate a pyroptosis-related lncRNA signature (PRLS) in three independent cohorts. The molecular characteristics, clinical significances, tumor microenvironment, immune checkpoints profiles, and benefits of chemotherapy and immunotherapy regarding to PRLS were also explored.

Results: In the WGCNA framework, a key module that highly correlated with pyroptosis was extracted for identifying PRLs. Univariate Cox analysis further revealed the associations between PRLs and overall survival. Based on the expression profiles of PRLs, the PRLS was initially developed in TCGA cohort (n = 143) and then validated in two CGGA cohorts (n = 374). Multivariate Cox analysis demonstrated that our PRLS model was an independent risk factor. More importantly, this signature displayed a stable and accurate performance in predicting prognosis at 1, 3, and 5 years, with all AUCs above 0.7. The decision curve analysis also indicated that our signature had promising clinical application. In addition, patients with high PRLS score suggested a more abundant immune infiltration, higher expression of immune checkpoint genes, and better response to immunotherapy but worse to chemotherapy.

Conclusion: A novel pyroptosis-related lncRNA signature with a robust performance was constructed and validated in multiple cohorts. This signature provided new perspectives for clinical management and precise treatments of GBM.

Immune Landscape Refines the Classification of Colorectal Cancer With Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies

The immune microenvironment has profound impacts on the initiation and progression of colorectal cancer (CRC). Therefore, the goal of this article is to identify two robust immune subtypes in CRC, further provide novel insights for the underlying mechanisms and clinical management. In this study, two CRC immune subtypes were identified using the consensus clustering of immune-related gene expression profiles in the meta-GEO dataset (n = 1,198), and their reproducibility was further verified in the TCGA-CRC dataset (n = 638). Subsequently, we characterized the immune escape mechanisms, gene alterations, and clinical features of two immune subtypes. Cluster 1 (C1) was defined as the “immune cold subtype” with immune cell depletion and deficiency, while cluster 2 (C2) was designed as the “immune hot subtype”, with abundant immune cell infiltration and matrix activation. We also underlined the potential immune escape mechanisms: lack of MHC molecules and defective tumor antigen presentation capacity in C1, increased immunosuppressive molecules in C2. The prognosis and sensitivity to 5-FU, Cisplatin and immunotherapy differed between two subtypes. According to the two immune subtypes, we developed a prognosis associated risk score (PARS) with the accurate performance for predicting the prognosis. Additionally, two nomograms for overall survival (OS) and disease-free survival (DFS) were further constructed to facilitate clinical management. Overall, our research provides new references and insights for understanding and refining the CRC.

Ferroptosis Mediation Patterns Reveal Novel Tool to Implicate Immunotherapy and Multi-Omics Characteristics in Bladder Cancer

Background: The regulatory role of ferroptosis in malignant tumours has been recently demonstrated. However, the potential roles of ferroptosis mediation patterns in bladder cancer remain elusive. Materials and Methods: The ferroptosis mediation patterns of 889 bladder cancer samples were comprehensively evaluated based on ferroptosis-related genes. The underlying correlations between these mediation patterns and multi-omic characteristics of bladder cancer were systematically analysed. The ferroptosis mediation patterns of individual samples were quantified by ferropscore using the principal component analysis algorithm. The typical ferroptosis-related genes with prognostic roles were further randomly validated using immunohistochemical staining, real-time polymerase chain reaction and western blotting. Results: Three different ferroptosis mediation patterns were identified. The abundance of infiltration of 23 immune cells was different among the three mediation patterns. The quantification of ferroptosis mediation patterns in individual samples served as a promising tool for predicting patient survival outcomes; immune cell infiltration abundance; tumour mutation burden; oncogenic mutation status and tumour grade, stage and molecular subtypes. Low ferropscore combined with high tumour mutation burden was associated with the best survival prognosis. Expressions of PD-L1 (p < 0.001), PD-1 (p = 0.002) and CTLA-4 (p = 0.003) were all significantly upregulated in the high ferropscore group. Low ferropscores also predicted good immunotherapy response for anti-CTLA4 strategy. The mRNA and protein levels of FADS2, a typical ferroptosis-related gene used in the study, were higher in bladder cancer cell lines than in controlled SV-HUC-1 cells. In addition, immunohistochemical staining revealed significantly higher expression levels of FADS2 in human bladder cancer tumour tissues than in normal tissues. Conclusion: This study identified three distinct ferroptosis mediation patterns in bladder cancer. Quantification of ferroptosis mediation patterns in individual samples may help to improve the understanding of multiomic characteristics and guide future immunotherapy responses to bladder cancer.

Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma

Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC.

Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions.

Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC.

Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer with high malignancy and poor prognosis. Metabolic reprogramming is a hallmark of cancer and has great importance on the tumor microenvironment (TME). As an abundant metabolite, lactate plays a crucial role in cancer progression and the immunosuppressive TME. Nonetheless, the potential roles of lactate in HCC remain unclear. In this study, we downloaded transcriptomic data of HCC patients with corresponding clinical information from the TCGA and ICGC portals. The TCGA-HCC dataset used as the training cohort, while the ICGC-LIRI-JP dataset was served as an external validation cohort. Cox regression analysis and the LASSO regression model were combined to construct the lactate metabolism-related gene signature (LMRGS). Then, we assessed the clinical significance of LMRGS in HCC. Besides, enriched molecular functions, tumor mutation burden (TMB), infiltrating immune cells, and immune checkpoint were comprehensively analyzed in different LMRGS subgroups. In total, 66 differentially expressed lactate metabolism-related genes (LMRGs) were screened. The functions of LMRGs were mainly enriched in mitochondrial activity and metabolic processes. The LMRGS comprised of six key LMRGs (FKTN, PDSS1, PET117, PUS1, RARS1, and RNASEH1) had significant clinical value for independently predicting the prognosis of HCC patients. The overall survival and median survival of patients in the LMRGS-high group were significantly shorter than in the LMRGS-low group. In addition, there were differences in TMB between the two LMRGS subgroups. The probability of genetic mutations was higher in the LMRGS-high group. Most importantly, the LMRGS reflected the TME characteristics. In the LMRGS-high group, the immune microenvironment presented a suppressed state, accompanied by more inhibitory immune cell infiltration, including follicular helper T cells and regulatory T cells. Additionally, the expression of inhibitory checkpoint molecules was much higher in the LMRGS-high group. Our study suggested that the LMRGS was a robust biomarker to predict the clinical outcomes and evaluate the TME of patients with HCC.

LCK and CD3E Orchestrate the Tumor Microenvironment and Promote Immunotherapy Response and Survival of Muscle-Invasive Bladder Cancer Patients

Background: Studies have demonstrated the significance of multiple biomarkers for bladder cancer. Here, we attempt to present biomarkers potentially predictive of the prognosis and immunotherapy response of muscle-invasive bladder cancer (MIBC). Method: Immune and stromal scores were calculated for MIBC patients from The Cancer Genome Atlas (TCGA). Core differential expression genes (DEGs) with prognostic value were identified and validated using an independent dataset GSE31684. The clinical implications of prognostic genes and the inter-gene correlation were presented. The distribution of tumor-infiltrating immune cells (TICs), the correlation with tumor mutation burden (TMB), and the expression of eight immune checkpoint-relevant genes and CD39 were accordingly compared. Two bladder cancer cohorts (GSE176307 and IMvigor210) receiving immunotherapy were recruited to validate the prognostic value of LCK and CD3E for immunotherapy. Results: 361 MIBC samples from TCGA revealed a worse overall survival for higher stromal infiltration (p = 0.009) but a better overall survival for higher immune infiltration (p = 0.042). CD3E and LCK were independently validated by TCGA and GSE31684 to be prognostic for MIBC. CD3E was the most correlative gene of LCK, with a coefficient of r = 0.86 (p < 0.001). CD8⁺ T cells and macrophage M1 are more abundant in favor of a higher expression of CD3E and LCK in MIBC and across pan-cancers. Immune checkpoints like CTLA4, CD274 (PD-1), and PDCD1 (PD-L1) were highly expressed in high-CD3E and high-LCK groups for MIBC and also for pan-cancers, except for thymoma. LCK and CD3E had a moderate positive correlation with CD39 expression. Importantly, high-LCK and high-CD3E groups had a higher percentage of responders than the low-expression groups both in GSE176307 (LCK: 22.73vs. 13.64%, CD3E: 22.00 vs. 13.16%) and IMvigor210 cohorts (LCK: 28.19 vs. 17.45%, CD3E: 25.50 vs. 20.13%). Conclusion: CD3E and LCK were potential biomarkers of MIBC. CD3E and LCK were positively correlated with several regular immunotherapy biomarkers, which is supported by real-world outcomes from two immunotherapy cohorts.

An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma

Introduction: Fibrosis, a primary cause of hepatocellular carcinoma (HCC), is intimately associated with inflammation, the tumor microenvironment (TME), and multiple carcinogenic pathways. Currently, due to widespread inter- and intra-tumoral heterogeneity of HCC, the efficacy of immunotherapy is limited. Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative. Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. Subsequently, comprehensive exploration was conducted to identify distinct genomic alterations, clinical features, biological functions, and immune landscapes of HCC patients. Results: The FAIS was an independent prognostic predictor of overall survival and recurrence-free survival in HCC. In parallel, the FAIS exhibited stable and accurate performance at predicting prognosis based on the evaluation of Kaplan-Meier survival curves, receiver operator characteristic curves, decision curve analysis, and Harrell's C-index. Further investigation elucidated that the high-risk group presented an inferior prognosis with advanced clinical traits and a high mutation frequency of TP53, whereas the low-risk group was characterized by superior CD8⁺ T cell infiltration, a higher TIS score, and a lower TIDE score. Additionally, patients in the low-risk group might yield more benefits from immunotherapy. Conclusion: The FAIS was an excellent scoring system that could stratify HCC patients and might serve as a promising tool to guide surveillance, improve prognosis, and facilitate clinical management.

CD8+ T Cell-Based Molecular Classification With Heterogeneous Immunogenomic Landscapes and Clinical Significance of Clear Cell Renal Cell Carcinoma

The tumor microenvironment (TME) exerts a high impact on tumor biology and immunotherapy. The heterogeneous phenotypes and the clinical significance of CD8⁺ T cells in TME have not been fully elucidated. Here, a comprehensive immunogenomic analysis based on multi-omics data was performed to investigate the clinical significance and tumor heterogeneity between CD8⁺ T cell-related molecular clusters. We identified two distinct molecular clusters of ccRCC (C1 and C2) in TCGA and validated in E-MTAB-1980 cohorts. The C1 cluster was characterized by unfavorable prognosis, increased expression levels of CD8⁺ T cell exhaustion markers, high immune infiltration levels as well as more immune escape mechanisms. The C2 cluster was featured by favorable prognosis, elevated expression levels of CD8⁺ T cell effector markers, low load of copy number loss and low frequency of 9p21.3 deletion. Moreover, the effect of molecular classifications on Nivolumab therapeutic efficacy in the CheckMate 025 cohort was examined, and the C2 cluster exhibited a better prognosis. Taken together, we determine two CD8⁺ T cell-related molecular clusters in ccRCC, and provide new insights for evaluating the functions of CD8⁺ T cells. Our molecular classification is a potential strategy for prognostic prediction and immunotherapeutic guidance for ccRCC patients.

Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown.

Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort.

Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis.

Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

Abstract Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.

Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.

Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8⁺ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.

Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

A Prognostic Model of Pancreatic Cancer Based on Ferroptosis-Related Genes to Determine Its Immune Landscape and Underlying Mechanisms

Pancreatic cancer is one of the malignant tumors with the worst prognosis in the world. As a new way of programmed cell death, ferroptosis has been proven to have potential in tumor therapy. In this study, we used the TCGA-PAAD cohort combined with the previously reported 60 ferroptosis-related genes to construct and validate the prognosis model and in-depth analysis of the differences in the function and immune characteristics of different RiskTypes. The results showed that the six-gene signature prognostic model that we constructed has good stability and effectiveness. Further analysis showed that the upregulated genes in the high-risk group were mainly enriched in extracellular matrix receptor-related pathways and other tumor-related pathways and the infiltration of immune cells, such as B, T, and NK cells, was suppressed. In short, our model shows good stability and effectiveness. Further studies have found that the prognostic differences between different RiskTypes may be due to the changes in the ECM-receptor pathway and activation of the immune system. Additionally, ICI drugs can treat pancreatic cancer in high-risk groups.

Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer

Colorectal cancer (CRC), a seriously threat that endangers public health, has a striking tendency to relapse and metastasize. Redox-related signaling pathways have recently been extensively studied in cancers. However, the study and potential role of redox in CRC remain unelucidated. We developed and validated a risk model for prognosis and recurrence prediction in CRC patients via identifying gene signatures driven by redox-related signaling pathways. The redox-driven prognostic signature (RDPS) was demonstrated to be an independent risk factor for patient survival (including OS and RFS) in four public cohorts and one clinical in-house cohort. Additionally, there was an intimate association between the risk score and tumor immune infiltration, with higher risk score accompanied with less immune cell infiltration. In this study, we used redox-related factors as an entry point, which may provide a broader perspective for prognosis prediction in CRC and have the potential to provide more promising evidence for immunotherapy.

Computational Recognition and Clinical Verification of TGF-β-Derived miRNA Signature With Potential Implications in Prognosis and Immunotherapy of Intrahepatic Cholangiocarcinoma

MicroRNAs (miRNAs) were recently implicated in modifying the transforming growth factor β (TGF-β) signaling in multiple cancers. However, TGF-β-derived miRNAs and their potential clinical significance remain largely unexplored in intrahepatic cholangiocarcinoma (ICC). In this study, we proposed an integrated framework that enables the identification of TGF-β-derived miRNAs in ICC (termed “TGFmitor”). A total of 36 TGF-β-derived miRNAs were identified, of which nine significantly correlated with overall survival (OS) and aberrantly expressed in ICC. According to these miRNAs, we discovered and validated a TGF-β associated miRNA signature (TAMIS) in GSE53870 (n =63) and TCGA-CHOL (n =32). To further confirm the clinical interpretation of TAMIS, another validation based on qRT-PCR results from 181 ICC tissues was performed. TAMIS was proven to be an independent risk indicator for both OS and relapse-free survival (RFS). TAMIS also displayed robust performance in three cohorts, with satisfactory AUCs and C-index. Besides, patients with low TAMIS were characterized by superior levels of CD8+ T cells infiltration and PD-L1 expression, while patients with high TAMIS possessed enhanced CMTM6 expression. Kaplan-Meier analysis suggested CMTM6 could further stratify TAMIS. The TAMIS^highCMTM6^high subtype had the worst prognosis and lowest levels of CD8A and PD-L1 expression relative to the other subtypes, indicating this subtype might behave as “super-cold” tumors. Notably, the improved discrimination was observed when CMTM6 was combined with TAMIS. Overall, our signature could serve as a powerful tool to help improve prognostic management and immunotherapies of ICC patients.

Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

Background

Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.

Methods

Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.

Results

The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.

Conclusions

The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

Clinical Application Value of Circulating Cell-free DNA in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and a leading cause of cancer-related deaths. Due to late diagnosis, early intrahepatic metastasis and nonresponse to systemic treatments, surgical resection and/or biopsy specimens remain the gold standard for disease staging, grading and clinical decision-making. Since only a small amount of tissue was obtained in a needle biopsy, the conventional tissue biopsy is unable to represent tumor heterogeneity in HCC. For this reason, it is imperative to find a new non-invasive and easily available diagnostic tool to detect HCC at an early stage and to monitor HCC recurrence. The past decade has witnessed considerable evolution in the development of liquid biopsy technologies with the emergence of next-generation sequencing. As a liquid biopsy approach, molecular analysis of cell-free DNA (cfDNA), characterized by noninvasiveness and real-time analysis, may accurately represent the tumor burden and comprehensively reflect genetic profile of HCC. Therefore, cfDNA may be used clinically as a predictive biomarker in early diagnosis, outcome assessment, and even molecular typing. In this review, we provide an update on the recent advances made in clinical applications of cfDNA in HCC.

CELF2 is a candidate prognostic and immunotherapy biomarker in triple-negative breast cancer and lung squamous cell carcinoma: A pan-cancer analysis

CUGBP Elav-like family member 2(CELF2) plays crucial roles in the development and activation of T cell. However, the impacts of CELF2 on tumour-infiltrating immune cells (TIICs) and clinical outcomes of tumours remain unclear. In this study, we found that elevated CELF2 expression was markedly correlated with prolonged survival in multiple tumours, particularly in breast and lung cancers. Notably, CELF2 only impacted the prognosis of triple-negative breast cancer (TNBC) with lymph node metastasis. Further investigation showed CELF2 expression was positively correlated with the infiltration abundance of dendritic cells (DCs), CD8+ T cells and neutrophils in breast invasive carcinoma (BRCA) and DCs in lung squamous cell carcinoma (LUSC). CELF2 also had strong correlations with markers of diverse TIICs such as T cells, tumour-associated macrophages and DCs in BRCA and LUSC. Importantly, CELF2 was significantly associated with plenty of immune checkpoint molecules (ICMs) and outperformed five prevalent biomarkers including PD-1, PD-L1, CTLA-4, CD8 and tumour mutation burden in predicting immunotherapeutic responses. Immunohistochemistry also revealed lower protein levels of CELF2 in TNBC and LUSC compared to normal tissues, and patients with high expression showed significantly prolonged prognosis. In conclusion, we demonstrated that increased CELF2 expression was closely related to better prognosis and superior TIIC infiltration and ICM expression, particularly in BRCA and LUSC. CELF2 also performed well in evaluating the immunotherapeutic efficacy, suggesting CELF2 might be a promising biomarker.