A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Epigenetics and immunotherapy in colorectal cancer: progress and promise

Colorectal cancer (CRC) is a common malignant tumor with the third and second highest incidence and mortality rates among various malignant tumors. Despite significant advancements in the present therapy for CRC, the majority of CRC cases feature proficient mismatch repair/microsatellite stability and have no response to immunotherapy. Therefore, the search for new treatment options holds immense importance in the diagnosis and treatment of CRC. In recent years, clinical research on immunotherapy combined with epigenetic therapy has gradually increased, which may bring hope for these patients. This review explores the role of epigenetic regulation in exerting antitumor effects through its action on immune cell function and highlights the potential of certain epigenetic genes that can be used as markers of immunotherapy to predict therapeutic efficacy. We also discuss the application of epigenetic drug sensitization immunotherapy to develop new treatment options combining epigenetic therapy and immunotherapy.

The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.

DNA methylation signatures provide novel diagnostic biomarkers and predict responses of immune therapy for breast cancer

Breast cancer (BRCA) is one of the most common malignant tumors affecting women worldwide. DNA methylation modifications can influence oncogenic pathways and provide potential diagnostic and therapeutic targets for precision oncology. In this study, we used non-parametric permutation tests to identify differentially methylated positions (DMPs) between paired tumor and normal BRCA tissue samples from the Cancer Genome Atlas (TCGA) database. Then, we applied non-negative matrix factorization (NMF) to the DMPs to derive eight distinct DNA methylation signatures. Among them, signatures Hyper-S3 and Hypo-S4 signatures were associated with later tumor stages, while Hyper-S1 and Hypo-S3 exhibited higher methylation levels in earlier stages. Signature Hyper-S3 displayed an effect on overall survival. We further validated the four stage-associated signatures using an independent BRCA DNA methylation dataset from peripheral blood samples. Results demonstrated that 24 commonly hypomethylated sites in Hypo-S4 showed lower methylation in BRCA patients compared to healthy individuals, suggesting its potential as an early diagnostic biomarker. Furthermore, we found that methylation of 23 probes from four stage-related signatures exhibited predictive power for immune therapy response. Notably, methylation levels of all three probes from the Hypo-S4 and activity of the Hypo-S4 demonstrated highly positive relevance to PD-L1 gene expression, implying their significant predictive values for immunotherapy outcomes. GO and KEGG pathway enrichment analysis revealed that genes with these 23 immunotherapy-related methylation probes are mainly involved in glycan degradation or protein deglycosylation. These methylation signatures and probes may serve as novel epigenetic biomarkers for predicting tumor immunotherapy response. Our findings provide new insights into precision oncology approaches for BRCA.

Identification of DNA methylation characteristics associated with metastasis and prognosis in colorectal cancer

Colorectal cancer (CRC) is prone to metastasis and recurrence after surgery, which is one of the main causes for its poor treatment and prognosis. Therefore, it is essential to identify biomarkers associated with metastasis and recurrence in CRC. DNA methylation has a regulatory role in cancer metastasis, tumor immune microenvironment (TME), and prognosis and may be one of the most valuable biomarkers for predicting CRC metastasis and prognosis. We constructed a diagnostic model and nomogram that can effectively predict CRC metastasis based on the differential methylation CpG sites (DMCs) between metastatic and non-metastatic CRC patients. Then, we identified 17 DMCs associated with progression free survival (PFS) of CRC and constructed a prognostic model. The prognosis model based on 17 DMCs can predict the PFS of CRC with medium to high accuracy. The results of immunohistochemical analysis indicated that the protein expression levels of the genes involved in prognostic DMCs were different between normal and colorectal cancer tissues. According to the results of immune-related analysis, we found that the low-risk patients had better immunotherapy response. In addition, high risk scores were negatively correlated with high tumor mutation burden (TMB) levels, and patients with low TMB levels in the high-risk group had the worst PFS. Our work shows the clinical value of DNA methylation in predicting CRC metastasis and PFS, as well as their correlation with TME, immunotherapy, and TMB, which helps understand the changes of DNA methylation in CRC metastasis and improving the treatment and prognosis of CRC.

Apolipoprotein E is a Potential Biomarker for Predicting Cancer Prognosis and is Correlated with Immune Infiltration

Background

Apolipoprotein E (APOE) is a polymorphic protein that plays a role in lipoprotein transformation and metabolism. It is involved in numerous physiological processes within the body and is closely associated with tumor growth and metastasis. However, the role of APOE in pan-cancer has yet to be evaluated. Therefore, studying the association between APOE and various cancer types is crucial for providing a basis for individualized treatment strategies and clinical prognosis assessment.

Methods

We investigated the diagnostic and prognostic significance of APOE across 33 tumor types, as well as its correlation with tumor mutation burden (TMB) and microsatellite instability (MSI). Additionally, we employed the ESTIMATE and CIBERSORT algorithms to analyze the potential impact of APOE on the immune system. Furthermore, gene set enrichment analysis (GSEA) was conducted to explore its underlying physiological function.

Results

Based on observations from a pan-cancer dataset, APOE expression was significantly different between cancer and normal tissues, and was simultaneously associated with survival outcomes in terms of cancer type, clinical annotation, TMB, MSI, and TICs abundance. In addition, the results also showed that expression of APOE may respond to a variety of cancer chemotherapy.

Conclusion

The findings from this study strongly indicate a close association between APOE and tumor development. Moreover, APOE shows promise as a potential biomarker for predicting prognosis and response to immunotherapy in patients with pan-cancer.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

PIK3CD correlates with prognosis, epithelial-mesenchymal transition and tumor immune infiltration in breast carcinoma

BACKGROUND

Breast carcinoma (BRCA) is one of the most common, fatal, and aggressive cancers, with increasing morbidity that has a major impact on human health. PIK3CD appears to have important roles in the beginning and advancement of various forms of human cancer, according to mounting data. However,the particular role and mechanism of PIK3CD in BRCA remains not fully identified.

METHODOLOGY

The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ), Genotype-Tissue Expression (GTEx) data and the UCSC Xena browser ( https://xenabrowser.net ) data were used in this study's initial pan-cancer analysis of PIK3CD expression and prognosis. Circular RNAs (circRNAs) that regulated the expression of PIK3CD were subsequently found using a combination of in silico investigations of expression, correlation, and survival. Measurements of PIK3CD expression and an analysis of the in vitro function of PIK3CD in BRCA cells were performed using real-time RT-PCR, Western blotting and Transwell assays.

RESULTS

In BRCA GLI2, RAB32, LAMB1, MGAT2, ITGA8, CHF, COL6A3 and PRRX1-miR-30b-5p axis was identified as the most likely upstream CircRNA-related route of PIK3CD. PIK3CD was correlated with the expression of EMT markers. The PIK3CD cDNA improved the capacity for invasion and migration. The expression of PIK3CD was linked to some of the m1A/m5C/m6A regulators. Additionally, it was discovered that the expression of PIK3CD was found to be highly connected to the expression of immunological checkpoints, immune cell biomarkers, and tumor immune cell invasion.

CONCLUSIONS

Our findings reveal that PIK3CD expression is associated with prognosis, EMT, and tumor immune infiltration in BRCA patients.

Chromatin factors: Ready to roll as biomarkers in metastatic colorectal cancer?

Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and stands as the fourth leading cause of cancer-related fatalities in 2020. Survival rates for metastatic disease have slightly improved in recent decades, with clinical trials showing median overall survival of approximately 24-30 months. This progress can be attributed to the integration of chemotherapeutic treatments alongside targeted therapies and immunotherapy. Despite these modest improvements, the primary obstacle to successful treatment for advanced CRC lies in the development of chemoresistance, whether inherent or acquired, which remains the major cause of treatment failure. Epigenetics has emerged as a hallmark of cancer, contributing to master transcription regulation and genome stability maintenance. As a result, epigenetic factors are starting to appear as potential clinical biomarkers for diagnosis, prognosis, and prediction of treatment response in CRC.In recent years, numerous studies have investigated the influence of DNA methylation, histone modifications, and chromatin remodelers on responses to chemotherapeutic treatments. While there is accumulating evidence indicating their significant involvement in various types of cancers, the exact relationship between chromatin landscapes and treatment modulation in CRC remains elusive. This review aims to provide a comprehensive summary of the most pertinent and extensively researched epigenetic-associated mechanisms described between 2015 and 2022 and their potential usefulness as predictive biomarkers in the metastatic disease.

Construction of a serum diagnostic signature based on m5C-related miRNAs for cancer detection

Currently, no clinically relevant non-invasive biomarkers are available for screening of multiple cancer types. In this study, we developed a serum diagnostic signature based on 5-methylcytosine (m5C)-related miRNAs (m5C-miRNAs) for multiple-cancer detection. Serum miRNA expression data and the corresponding clinical information of patients were collected from the Gene Expression Omnibus database. Serum samples were then randomly assigned to the training or validation cohort at a 1:1 ratio. Using the identified m5C-miRNAs, an m5C-miRNA signature for cancer detection was established using a support vector machine algorithm. The constructed m5C-miRNA signature displayed excellent accuracy, and its areas under the curve were 0.977, 0.934, and 0.965 in the training cohort, validation cohort, and combined training and validation cohort, respectively. Moreover, the diagnostic capability of the m5C-miRNA signature was unaffected by patient age or sex or the presence of noncancerous disease. The m5C-miRNA signature also displayed satisfactory performance for distinguishing tumor types. Importantly, in the detection of early-stage cancers, the diagnostic performance of the m5C-miRNA signature was obviously superior to that of conventional tumor biomarkers. In summary, this work revealed the value of serum m5C-miRNAs in cancer detection and provided a new strategy for developing non-invasive and cost effective tools for large-scale cancer screening.

Immunological function and prognostic value of lymphoid-specific helicase in liver hepatocellular carcinoma

BACKGROUND

Lymphoid-specific helicase (HELLS), a SNF2-like chromatin-remodeling enzyme, plays a key role in tumor progression via its DNA methylation function. However, the effects of HELLS on immune infiltration and prognosis in liver hepatocellular carcinoma (LIHC) remain uncertain.

METHODS

The Tumor Immune Estimation Resource (TIMER) database was employed to explore the pan-cancer mRNA expression of HELLS and its correlation with immunity. GEPIA2 was used to verify the correlation between HELLS expression and survival. The role of HELLS in cancer was explored via gene set enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and the construction of gene-gene and protein-protein interaction networks (PPI). Additionally, correlations between DNA methylation, HELLS expression, and immune-related genes were explored in LIHC. HELLS expression in LIHC clinical samples was determined using qRT-PCR and western blotting. The effects of downregulated HELLS expression in hepatocellular carcinoma cells was explored via transfection experiments in vitro.

RESULTS

High HELLS mRNA expression was identified in several cancers and was significantly associated with poorer prognosis in LIHC. Furthermore, HELLS expression was positively correlated with tumor-infiltrating lymphocytes and immune checkpoint genes in LIHC. Bioinformatics analysis suggested that DNA methylation of HELLS may be associated with the immune response. Results from the TCGA-LIHC dataset, clinical samples, and functional analysis indicated that HELLS contributed to tumor progression in LIHC.

CONCLUSION

The study findings demonstrate that HELLS is an important factor in promoting LIHC malignancy and might serve as a potential biomarker for LIHC.

Exercise-induced IL-15 acted as a positive prognostic implication and tumor-suppressed role in pan-cancer

Objective: Exercise can produce a large number of cytokines that may benefit cancer patients, including Interleukin 15 (IL-15). IL-15 is a cytokine that has multiple functions in regulating the adaptive and innate immune systems and tumorigenesis of lung and breast cancers. However, the roles of IL-15 in other types of cancer remain unknown. In this article, we try to systematically analyze if IL-15 is a potential molecular biomarker for predicting patient prognosis in pan-cancer and its connection with anti-cancer effects of exercise. Methods: The expression of IL-15 was detected by The Cancer Genome Atlas (TCGA) database, Human protein Atlas (HPA), and Genotype Tissue-Expression (GTEX) database. Analysis of IL-15 genomic alterations and protein expression in human organic tissues was analyzed by the cBioPortal database and HPA. The correlations between IL-15 expression and survival outcomes, clinical features, immune-associated cell infiltration, and ferroptosis/cuproptosis were analyzed using the TCGA, ESTIMATE algorithm, and TIMER databases. Gene Set Enrichment Analysis (GSEA) was performed to evaluate the biological functions of IL-15 in pan-cancer. Results: The differential analysis suggested that the level of IL-15 mRNA expression was significantly downregulated in 12 tumor types compared with normal tissues, which is similar to the protein expression in most cancer types. The high expression of IL-15 could predict the positive survival outcome of patients with LUAD (lung adenocarcinoma), COAD (colon adenocarcinoma), COADREAD (colon and rectum adenocarcinoma), ESCA (esophageal carcinoma), SKCM (skin cutaneous melanoma), UCS (uterine carcinosarcoma), and READ (rectum adenocarcinoma). Moreover, amplification was found to be the most frequent mutation type of IL-15 genomic. Furthermore, the expression of IL-15 was correlated to the infiltration levels of various immune-associated cells in pan-cancer assessed by the ESTIMATE algorithm and TIMER database. In addition, IL-15 is positively correlated with ferroptosis/cuproptosis-related genes (ACSL4 and LIPT1) in pan-cancer. Levels of IL-15 were reported to be elevated in humans for 10-120 min following an acute exercise. Therefore, we hypothesized that the better prognosis of pan-cancer patients with regular exercise may be achieved by regulating level of IL-15. Conclusion: Our results demonstrated that IL-15 is a potential molecular biomarker for predicting patient prognosis, immunoreaction, and ferroptosis/cuproptosis in pan-cancer and partly explained the anti-cancer effects of exercise.

Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment

Background

The incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is crucial to improve the prognosis and treatment strategies. However, with existing diagnostic methods, detecting BCa in the early stage is challenging. Hence, novel biomarkers are urgently needed to improve early diagnosis and treatment efficiency.

Methods

The gene expression profile and gene methylation profile dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), differentially methylated genes (DMGs), and methylation-regulated differentially expressed genes (MeDEGs) were gradually identified. A cancer genome map was obtained using online gene expression profile interaction analysis, and survival implications were produced using Kaplan-Meier survival analysis. GSEA was employed to predict the marker pathways where DEGs were significantly involved. The study used bisulfite PCR amplification combined with bisulfite amplicon sequencing (BSAS) to screen for methylation analysis of multiple candidate regions of the adenylate cyclase 2 (ADCY2) based on the sequence design of specific gene regions and CpG islands.

Results

In this study, DEGs and DMGs with significantly up- or down-regulated expression were selected. The intersection method was used to screen the MeDEGs. The interaction network group in STRING was then visualized using Cytoscape, and the PPI network was constructed to identify the key genes. The key genes were then analyzed using functional enrichment. To compare the relationship between key genes and the prognosis of BCa patients, we further investigated ADCY2 and found that ADCY2 can be a potential clinical biomarker in BCa prognosis and immunotherapy response prediction. In human BCa 5637 and MGH1 cells, we developed and verified the effectiveness of ADCY2 primers using BSAS technology. The findings revealed that the expression of ADCY2 is highly regulated by the methylation of the promoter regions.

Conclusion

This study revealed that increased expression of ADCY2 was significantly correlated with increased tumor heterogeneity, predicting worse survival and immunotherapy response in BCa patients.

Pan-Cancer Analysis on the Oncogenic Role of Programmed Cell Death 10

Purpose

Programmed cell death factor 10 (PDCD10) is associated with intercellular junction, cytoskeleton organization, cell proliferation, apoptosis, exocytosis, and angiogenesis. However, the role of PDCD10 in human cancer is unclear. This study aims to explore the role of PDCD10 in various tumors and its possible mechanism through bioinformatics analysis.

Methods

We verified the expression of the PDCD10 gene based on data from the ONCOMINE, TIMER2.0, and TISDB databases. The correlation of PDCD10 with prognosis of patients with different tumors was analyzed using data from the GEPIA2 database. Proteins bound to PDCD10 were analyzed from the STRING database. PDCD10, PDCD10-binding proteins, and associated candidate genes were analyzed in DAVID for functional and pathway analyses. We also evaluated the immunological, clinical, and genetic aspects of distinct cancers by using TIMER2.0 and the connection between PDCD10 expression and tumor immune subtypes by using TISDB. Single-cell sequencing data from the CancerSEA database were used to characterize cancer cell functional states and generate heat maps.

Results

PDCD10 overexpression is linked to certain molecular subtypes of human cancer. Low PDCD10 expression in patients with bladder urothelial carcinoma (BLCA), lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), adrenocortical carcinoma (ACC), head and neck squamous cell carcinoma (HNSC), kidney chromophobe carcinoma (KICH), brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), uterine corpus endometrial carcinoma (UCEC), oral squamous cell carcinoma (OSCC), and esophageal adenocarcinoma (ESAD) was correlated with favorable OS, whereas high PDCD10 expression in patients with LUSC, KIRC, READ, SKCM, and THYM was correlated with good prognosis. STRING network prediction results showed that 20 proteins, namely, paxillin (PXN), CCM2 scaffold protein (CCM2), TRAF3 interacting protein 3 (TRAF3IP3), FGFR1 oncogene partner 2 (FGFR1OP2), chromosome 4 open reading frame 19 (C4orf19), suppressor of IKBKE 1 (SIKE1), serine/threonine kinase 25 (STK25), striatin (STRN), protein phosphatase 2 catalytic subunit alpha (PPP2CA), mammalian sterile-20-like kinase 4 (MST4), MOB family member 4 (MOB4), protein phosphatase 2 scaffold subunit Abeta (PPP2R1B), sarcolemma-associated protein (SLMAP), serine/threonine kinase 24 (STK24), striatin 4 (STRN4), STRN3, protein phosphatase 2 scaffold subunit A alpha (PPP2R1A), striatin interacting protein 1 (STRIP1), CTTNBP2 N-terminal like (CTTNBP2NL), and cortactin binding protein 2 (CTTNBP2), can bind to PDCD10. Gene enrichment analysis suggested that PDCD10 is involved in the occurrence of different tumors through the Hippo signalling pathway, RNA transport, mRNA monitoring pathway, endocytosis, and T cell receptor signalling pathway. An inverse relationship was found between PDCD10 expression and cancer-associated fibroblasts in LUSC and TGCT, and PDCD10 expression was strongly connected with immunological subtypes, such as C1 (wound healing), C2 (interferon-gamma dominant), C3 (inflammation), C4 (lymphocyte depletion), C5 (immune silenced), and C6 (TGF-beta dominant). Finally, analysis of single-cell sequencing data revealed that PDCD10 expression is linked to epigenetic reprogramming, DNA repair, cell cycle progression, cell differentiation, inflammation, cell proliferation, cell differentiation, cell invasion, and angiogenesis.

Conclusion

The results of our investigation demonstrate that PDCD10 has an oncogenic function in many cancer types. This study provides a reference for future research on antitumor therapeutic targets.

m6A/ m1A /m5C/m7G-related methylation modification patterns and immune characterization in prostate cancer

Methylation has a close relationship with immune reactions, metastasis, and cancer cell growth. Additionally, RNA methylation-related proteins have emerged as potential cancer therapeutic targets. The connection between the tumor microenvironment (TME) and methylation-related genes (MRGs) remains unclear. We explored the expression patterns of the MRGs in the genome and transcriptional fields of 796 prostate cancer (PCa) samples using two separate data sets. We identified a relationship between patient clinicopathological characteristics, prognosis, TME cell infiltrating qualities, and different MRG changes, as well as the identification of two distinct molecular groupings. Then, we formed an MRGs model to predict overall survival (OS), and we tested the accuracy of the model in patients with PCa. In addition, we developed a very accurate nomogram to improve the MRG model’s clinical applicability. The low-risk group had fewer tumor mutational burden (TMB), greater tumor immune dysfunction and exclusion (TIDE) ratings, fewer mutant genes, and better OS prospects. We discuss how MGRs may affect the prognosis, clinically important traits, TME, and immunotherapy responsiveness in PCa. In order to get a better understanding of MRGs in PCa, we could further explore the prognosis and create more effective immunotherapy regimens to open new avenues.

The pyroptosis-related gene signature predicts prognosis and reveals characterization of the tumor immune microenvironment in acute myeloid leukemia

Background: Pyroptosis is a novel inflammatory form of programmed cell death and a prospective target for cancer therapy. Nevertheless, little is known about the association between pyroptosis-related genes (PRGs) and acute myeloid leukemia (AML) prognosis. Herein, we systematically investigated the specific functions and clinical prognostic value of multiple PRGs in AML.

Methods: Univariate and LASSO Cox regression analyses based on TCGA and GTEx databases were used to generate the PRG signature, whose predictive efficacy of survival was evaluated using survival analysis, ROC, univariate and multivariate Cox analyses as well as subgroup analysis. The BeatAML cohort was used for data validation. The association between risk score and immune cell infiltration, HLA, immune checkpoints, cancer stem cell (CSC), tumor mutation burden (TMB), and therapeutic drug sensitivity were also analyzed.

Results: Six -PRG signatures, namely, CASP3, ELANE, GSDMA, NOD1, PYCARD, and VDR were generated. The high-risk score represented a poorer prognosis and the PRG risk score was also validated as an independent predictor of prognosis. A nomogram including the cytogenetic risk, age, and risk score was constructed for accurate prediction of 1-, 3-, and 5-year survival probabilities. Meanwhile, this risk score was significantly associated with the tumor immune microenvironment (TIME). A high-risk score is characterized by high immune cell infiltration, HLA, and immune checkpoints, as well as low CSC and TMB. In addition, patients with low-risk scores presented significantly lower IC50 values for ATRA, cytarabine, midostaurin, doxorubicin, and etoposide.

Conclusion: Our findings might contribute to further understanding of PRGs in the prognosis and development of AML and provide novel and reliable biomarkers for its precise prevention and treatment.

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.