DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load

LRP1 induces anti-PD-1 resistance by modulating the DLL4-NOTCH2-CCL2 axis and redirecting M2-like macrophage polarisation in bladder cancer

The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response.

SIGNIFICANCE

Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Sex-related DNA methylation is associated with inflammation and gene expression in the lungs of healthy individuals

Lung cancer exhibits sex-biased molecular characteristics and epidemiological trends, suggesting a need for sex-specific approaches to understanding its etiology and treatment. DNA methylation alterations play critical roles in lung carcinogenesis and may serve as valuable biomarkers for precision medicine strategies. We employed the Infinium MethylationEPIC array to identify autosomal sex-related differentially methylated CpG sites (DM-CpGs) in lung epithelium of healthy individuals (32 females and 37 males) while controlling for age, BMI, and tobacco use. We correlated DM-CpGs with gene expression in lung epithelium and immune responses in bronchoalveolar lavage. We validated these DM-CpGs in lung tumors and adjacent normal tissue from The Cancer Genome Atlas (TCGA). Among 522 identified DM-CpGs, 61% were hypermethylated in females, predominantly located in promoter regions. These DM genes were implicated in cell-to-cell signaling, cellular function, transport, and lipid metabolism. Correlation analysis revealed sex-specific patterns between DM-CpGs and gene expression. Additionally, several DM-CpGs were correlated significantly with cytokines (IL-1β, IL-4, IL-12p70, and IFN-γ), macrophage, and lymphocyte counts. Also, some DM-CpGs were observed in TCGA lung adenocarcinoma, squamous cell carcinoma, and adjacent normal tissues. Our findings highlight sex-specific DNA methylation patterns in healthy lung epithelium and their associations with lung gene expression and lung immune biomarkers. These findings underscore the potential role of lung sex-related CpGs as epigenetic predispositions influencing sex disparities in lung cancer risk and outcomes, warranting further investigation for personalized lung cancer management strategies.

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

SIRPG expression positively associates with an inflamed tumor microenvironment and response to PD-1 blockade

BACKGROUND

This study aimed to investigate the relationship between signal regulatory protein gamma (SIRPG) and tumor immune microenvironment phenotypes or T cell mediated-adaptive antitumor immunity, and its predictive value for response to PD-1 blockade in cancers.

METHODS

Pan-cancer analysis of SIRPG expression and immune deconvolution was performed using transcriptomic data across 33 tumor types. Transcriptomic and clinical data from 157 patients with non-small-cell lung cancer (NSCLC) and melanoma received PD-1 blockade were analyzed. Expression characteristics of SIRPG were investigated using single-cell RNA sequencing (scRNA-seq) data of 103,599 cells. The effect of SIRPG expression was evaluated via SIRPG knockdown or overexpression in Jurkat T cells.

RESULTS

The results showed that most cancers with high SIRPG expression had significantly higher abundance of T cells, B cells, NK cells, M1 macrophages and cytotoxic lymphocytes and increased expression level of immunomodulatory factors regulating immune cell recruitment, antigen presentation, T cell activation and cytotoxicity, but markedly lower abundance of neutrophils, M2 macrophages, and myeloid-derived suppressor cells. High SIRPG expression was associated with favorable response to PD-1 blockade in both NSCLC and melanoma. scRNA-seq data suggested SIRPG was mainly expressed in CD8+ exhausted T and CD4+ regulatory T cells, and positively associated with immune checkpoint expression including PDCD1 and CTLA4. In vitro test showed SIRPG expression in T cells could facilitate expression of PDCD1 and CTLA4.

CONCLUSION

High SIRPG expression is associated with an inflamed immune phenotype in cancers and favorable response to PD-1 blockade, suggesting it would be a promising predictive biomarker for PD-1 blockade and novel immunotherapeutic target.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach

Background

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method

Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results

A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions

This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Converting "cold" to "hot": epigenetics strategies to improve immune therapy effect by regulating tumor-associated immune suppressive cells

Significant developments in cancer treatment have been made since the advent of immune therapies. However, there are still some patients with malignant tumors who do not benefit from immunotherapy. Tumors without immunogenicity are called "cold" tumors which are unresponsive to immunotherapy, and the opposite are "hot" tumors. Immune suppressive cells (ISCs) refer to cells which can inhibit the immune response such as tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), regulatory T (Treg) cells and so on. The more ISCs infiltrated, the weaker the immunogenicity of the tumor, showing the characteristics of "cold" tumor. The dysfunction of ISCs in the tumor microenvironment (TME) may play essential roles in insensitive therapeutic reaction. Previous studies have found that epigenetic mechanisms play an important role in the regulation of ISCs. Regulating ISCs may be a new approach to transforming "cold" tumors into "hot" tumors. Here, we focused on the function of ISCs in the TME and discussed how epigenetics is involved in regulating ISCs. In addition, we summarized the mechanisms by which the epigenetic drugs convert immunotherapy-insensitive tumors into immunotherapy-sensitive tumors which would be an innovative tendency for future immunotherapy in "cold" tumor.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Different lactate metabolism subtypes reveal heterogeneity in clinical outcomes and immunotherapy in lung adenocarcinoma patients

Background

The excessive accumulation of lactate within the tumor microenvironment (TME) has been demonstrated to facilitate tumor advancement and evade the immune system. Nonetheless, the metabolic status of lactate in lung adenocarcinoma (LUAD) remains uncertain.

Method

By analyzing the transcriptome profile of patients with LUAD, we created a lactate metabolism score (LMS) to predict survival. We then conducted a comprehensive examination of the biological functions and immune infiltration among different LMS patient groups. Moreover, we assessed the LMS predictive efficacy in chemotherapy and immunotherapy. Finally, we validated the detrimental phenotypic effects of SLC16A3 on LUAD cell lines (PC9 and A549) through in vitro experiments. We collected clinical samples to assess the prognostic impact of SLC16A3.

Results

We constructed an LMS model with 6 lactate metabolism regulatory factors using LASSO regression. The high LMS model indicates worse clinical outcomes for LUAD patients. High LMS patients are associated with metabolic dysregulation and increased infiltration of M0 and M1 macrophages. Low LMS patients are related to upregulated citric acid metabolism pathways and memory immune cells. High LMS patients are suitable for traditional chemotherapy, while patients with low LMS are more likely to benefit from immunotherapy. Lastly, downregulating SLC16A3 significantly reduces the proliferative and invasive capabilities of LUAD cell lines. Clinical cohort shows that patients with high expression of SLC16A3 have a worse prognosis.

Conclusions

The LMS model constructed based on the lactate metabolism pathway displays high effectiveness in predicting the outcome of patients with LUAD. LMS can offer direction regarding chemotherapy as well as immunotherapy in LUAD.

Role of Exosomes in Cancer and Aptamer-Modified Exosomes as a Promising Platform for Cancer Targeted Therapy

Exosomes are increasingly recognized as important mediators of intercellular communication in cancer biology. Exosomes can be derived from cancer cells as well as cellular components in tumor microenvironment. After secretion, the exosomes carrying a wide range of bioactive cargos can be ingested by local or distant recipient cells. The released cargos act through a variety of mechanisms to elicit multiple biological effects and impact most if not all hallmarks of cancer. Moreover, owing to their excellent biocompatibility and capability of being easily engineered or modified, exosomes are currently exploited as a promising platform for cancer targeted therapy. In this review, we first summarize the current knowledge of roles of exosomes in risk and etiology, initiation and progression of cancer, as well as their underlying molecular mechanisms. The aptamer-modified exosome as a promising platform for cancer targeted therapy is then briefly introduced. We also discuss the future directions for emerging roles of exosome in tumor biology and perspective of aptamer-modified exosomes in cancer therapy.

Integrated analysis of single-cell and bulk RNA sequencing data reveals a myeloid cell-related regulon predicting neoadjuvant immunotherapy response across cancers

BACKGROUND

Immunotherapy has brought about a paradigm shift in the treatment of cancer. However, the majority of patients exhibit resistance or become refractory to immunotherapy, and the underlying mechanisms remain to be explored.

METHODS

Sing-cell RNA sequencing (scRNA‑seq) datasets derived from 1 pretreatment and 1 posttreatment achieving pathological complete response (pCR) patient with lung adenocarcinoma (LUAD) who received neoadjuvant immunotherapy were collected, and pySCENIC was used to find the gene regulatory network (GRN) between cell types and immune checkpoint inhibitor (ICI) response. A regulon predicting ICI response was identified and validated using large‑scale pan-cancer data, including a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 5 ICI transcriptomic cohorts. Symphony reference mapping was performed to construct the myeloid cell map.

RESULTS

Thirteen major cluster cell types were identified by comparing pretreatment and posttreatment patients, and the fraction of myeloid cells was higher in the posttreatment group (19.0% vs. 11.8%). A PPARG regulon (containing 23 target genes) was associated with ICI response, and its function was validated by a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, TCGA pan-cancer cohort, and 5 ICI transcriptomic cohorts. Additionally, a myeloid cell map was developed, and cluster I, II, and III myeloid cells with high expression of PPARG were identified. Moreover, we constructed a website called PPARG ( https://pparg.online/PPARG/ or http://43.134.20.130:3838/PPARG/ ), which provides a powerful discovery tool and resource value for researchers.

CONCLUSIONS

The PPARG regulon is a predictor of ICI response. The myeloid cell map enables the identification of PPARG subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.

Androgen receptor activity inversely correlates with immune cell infiltration and immunotherapy response across multiple cancer lineages

There is now increasing recognition of the important role of androgen receptor (AR) in modulating immune function. To gain a comprehensive understanding of the effects of AR activity on cancer immunity, we employed a computational approach to profile AR activity in 33 human tumor types using RNA-Seq datasets from The Cancer Genome Atlas. Our pan-cancer analysis revealed that the genes most negatively correlated with AR activity across cancers are involved in active immune system processes. Importantly, we observed a significant negative correlation between AR activity and IFNγ pathway activity at the pan-cancer level. Indeed, using a matched biopsy dataset from subjects with prostate cancer before and after AR-targeted treatment, we verified that inhibiting AR enriches immune cell abundances and is associated with higher IFNγ pathway activity. Furthermore, by analyzing immunotherapy datasets in multiple cancers, our results demonstrate that low AR activity was significantly associated with a favorable response to immunotherapy. Together, our data provide a comprehensive assessment of the relationship between AR signaling and tumor immunity.

Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis

BACKGROUND

Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.

METHODS

ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.

RESULTS

Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.

CONCLUSIONS

Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

Conserved immuno-collagenic subtypes predict response to immune checkpoint blockade

BACKGROUND

Immune checkpoint blockade (ICB) has revolutionized the treatment of various cancer types. Despite significant preclinical advancements in understanding mechanisms, identifying the molecular basis and predictive biomarkers for clinical ICB responses remains challenging. Recent evidence, both preclinical and clinical, underscores the pivotal role of the extracellular matrix (ECM) in modulating immune cell infiltration and behaviors. This study aimed to create an innovative classifier that leverages ECM characteristics to enhance the effectiveness of ICB therapy.

METHODS

We analyzed transcriptomic collagen activity and immune signatures in 649 patients with cancer undergoing ICB therapy. This analysis led to the identification of three distinct immuno-collagenic subtypes predictive of ICB responses. We validated these subtypes using the transcriptome data from 9,363 cancer patients from The Cancer Genome Atlas (TCGA) dataset and 1,084 in-house samples. Additionally, novel therapeutic targets were identified based on these established immuno-collagenic subtypes.

RESULTS

Our categorization divided tumors into three subtypes: "soft & hot" (low collagen activity and high immune infiltration), "armored & cold" (high collagen activity and low immune infiltration), and "quiescent" (low collagen activity and immune infiltration). Notably, "soft & hot" tumors exhibited the most robust response to ICB therapy across various cancer types. Mechanistically, inhibiting collagen augmented the response to ICB in preclinical models. Furthermore, these subtypes demonstrated associations with immune activity and prognostic predictive potential across multiple cancer types. Additionally, an unbiased approach identified B7 homolog 3 (B7-H3), an available drug target, as strongly expressed in "armored & cold" tumors, relating with poor prognosis.

CONCLUSION

This study introduces histopathology-based universal immuno-collagenic subtypes capable of predicting ICB responses across diverse cancer types. These findings offer insights that could contribute to tailoring personalized immunotherapeutic strategies for patients with cancer.

High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer

Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.

Nanomedomics

Nanomaterials have attractive physicochemical properties. A variety of nanomaterials such as inorganic, lipid, polymers, and protein nanoparticles have been widely developed for nanomedicine via chemical conjugation or physical encapsulation of bioactive molecules. Superior to traditional drugs, nanomedicines offer high biocompatibility, good water solubility, long blood circulation times, and tumor-targeting properties. Capitalizing on this, several nanoformulations have already been clinically approved and many others are currently being studied in clinical trials. Despite their undoubtful success, the molecular mechanism of action of the vast majority of nanomedicines remains poorly understood. To tackle this limitation, herein, this review critically discusses the strategy of applying multiomics analysis to study the mechanism of action of nanomedicines, named nanomedomics, including advantages, applications, and future directions. A comprehensive understanding of the molecular mechanism could provide valuable insight and therefore foster the development and clinical translation of nanomedicines.

Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication

BACKGROUND

Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.

METHODS

Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.

RESULTS

The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.

CONCLUSIONS

We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

Novel insights into tumorigenesis revealed by molecular analysis of Lynch syndrome cases with multiple colorectal tumors

Background

Lynch syndrome (LS) is an autosomal dominant multi-organ cancer syndrome with a high lifetime risk of cancer. The number of cumulative colorectal adenomas in LS does not generally exceed ten, and removal of adenomas via routine screening minimizes the cancer burden. However, abnormal phenotypes may mislead initial diagnosis and subsequently cause suboptimal treatment.

Aim

Currently, there is no standard guide for the care of multiple colorectal adenomas in LS individuals. We aimed to shed insight into the molecular features and reasons for multiplicity of adenomas in LS patients.

Methods

We applied whole exome sequencing on nine adenomas (ten samples) and three assumed primary carcinomas (five samples) of an LS patient developing the tumors during a 21-year follow-up period. We compared the findings to the tumor profiles of two additional LS cases ascertained through colorectal tumor multiplicity, as well as to ten adenomas and 15 carcinomas from 23 unrelated LS patients with no elevated adenoma burden from the same population. As LS associated cancers can arise via several molecular pathways, we also profiled the tumors for CpG Island Methylator Phenotype (CIMP), and LINE-1 methylation.

Results

All tumors were microsatellite unstable (MSI), and MSI was present in several samples derived from normal mucosa as well. Interestingly, frequent frameshift variants in RNF43 were shared among substantial number of the tumors of our primary case and the tumors of LS cases with multiple tumors but almost absent in our control LS cases. The RNF43 variants were completely absent in the normal tissue, indicating tumor-associated mutational hotspots. The RNF43 status correlated with the mutational signature SBS96. Contrary to LS tumors from the reference set with no elevated colorectal tumor burden, the somatic variants occurred significantly more frequently at C>T in the CpG context, irrespective of CIMP or LINE-1 status, potentially indicating other, yet unknown methylation-related mechanisms. There were no signs of somatic mosaicism affecting the MMR genes. Somatic variants in APC and CTNNB1 were unique to each tumor.

Conclusion

Frequent somatic RNF43 hot spot variants combined with SBS96 signature and increased tendency to DNA methylation may contribute to tumor multiplicity in LS.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Inference of Developmental Hierarchy and Functional States of Exhausted T Cells from Epigenetic Profiles with Deep Learning

Exhausted T cells are a key component of immune cells that play a crucial role in the immune response against cancer and influence the efficacy of immunotherapy. Accurate assessment and measurement of T-cell exhaustion (TEX) are critical for understanding the heterogeneity of TEX in the tumor microenvironment (TME) and tailoring individualized immunotherapeutic strategies. In this study, we introduced DeepEpiTEX, a novel computational framework based on deep neural networks, for inferring the developmental hierarchy and functional states of exhausted T cells in the TME from epigenetic profiles. DeepEpiTEX was trained using various modalities of epigenetic data, including DNA methylation data, microRNA expression data, and long non-coding RNA expression data from 30 bulk solid cancer types in the TCGA pan-cancer cohort, and identified five optimal TEX subsets with significant survival differences across the majority of cancer types. The performance of DeepEpiTEX was further evaluated and validated in external multi-center and multi-type cancer cohorts, consistently demonstrating its generalizability and applicability in different experimental settings. In addition, we discovered the potential relationship between TEX subsets identified by DeepEpiTEX and the response to immune checkpoint blockade therapy, indicating that individuals with immune-favorable TEX subsets may experience the greatest benefits. In conclusion, our study sheds light on the role of epigenetic regulation in TEX and provides a powerful and promising tool for categorizing TEX in different disease settings.

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets

Introduction

Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies.

Methods

Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified.

Results

Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified.

Conclusion

In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model

BACKGROUND

Molecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC).

METHODS

Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients' clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers.

RESULTS

We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors.

CONCLUSIONS

Our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

A method for predicting drugs that can boost the efficacy of immune checkpoint blockade

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer

PURPOSE

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures.

METHODS

In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs).

RESULTS

We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level.

CONCLUSION

Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

Effectiveness of Immunotherapy in Non-Small Cell Lung Cancer Patients with a Diagnosis of COPD: Is This a Hidden Prognosticator for Survival and a Risk Factor for Immune-Related Adverse Events?

The interplay between the immune system and chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC) is complex and multifaceted. In COPD, chronic inflammation and oxidative stress can lead to immune dysfunction that can exacerbate lung damage, further worsening the respiratory symptoms. In NSCLC, immune cells can recognise and attack the cancer cells, which, however, can evade or suppress the immune response by various mechanisms, such as expressing immune checkpoint proteins or secreting immunosuppressive cytokines, thus creating an immunosuppressive tumour microenvironment that promotes cancer progression and metastasis. The interaction between COPD and NSCLC further complicates the immune response. In patients with both diseases, COPD can impair the immune response against cancer cells by reducing or suppressing the activity of immune cells, or altering their cytokine profile. Moreover, anti-cancer treatments can also affect the immune system and worsen COPD symptoms by causing lung inflammation and fibrosis. Immunotherapy itself can also cause immune-related adverse events that could worsen the respiratory symptoms in patients with COPD-compromised lungs. In the present review, we tried to understand the interplay between the two pathologies and how the efficacy of immunotherapy in NSCLC patients with COPD is affected in these patients.

Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors

Background

The utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment.

Methods

The ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses.

Results

A total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients.

Conclusion

Gene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.

EPRIM: An approach of identifying cancer immune-related epigenetic regulators

Epigenetic regulation contributes to the dysregulation of gene expression involved in cancer biology. Nevertheless, the roles of epigenetic regulators (ERs) in tumor immunity and immune response remain basically unclear. Here, we developed the epigenetic regulator in immunology (EPRIM) approach to identify immune-related ERs and comprehensively dissected the ER regulation in tumor immune response across 33 cancers. The identified immune-related ERs were related to immune infiltration and could stratify cancer patients into two risk groups in multiple independent datasets. These patient groups were characterized by distinct immune functions, immune infiltrates, driver gene mutations, and prognoses. Furthermore, we constructed an immune ER-based signature and highlighted its potential utility in predicting clinical benefit from immunotherapy and selecting therapeutic agents. Taken together, our identification and evaluation of immune-related ERs highlight the usefulness of EPRIM for the understanding of ERs in immune regulation and the clinical relevance in evaluation of cancer patient prognosis and response to immune checkpoint blockade therapy.

Identification of a CpG-based signature coupled with gene expression as prognostic indicators for melanoma: a preliminary study

DNA methylation is an important part of the genomic biology, which recently allowed the identification of key biomarkers for a variety of cancers, including cutaneous melanoma. Despite the current knowledge in cutaneous melanoma, there is a clear need for new efficient biomarkers in clinical application of detection. We use The Cancer Genome Atlas data as a training set and a multi-stage screening strategy to identify prognostic characteristics of melanoma based on DNA methylation. Three DNA methylation CpG sites were identified to be related to the overall survival in the skin cutaneous melanoma cohort. This signature was validated in two independent datasets from Gene Expression Omnibus. The stratified analysis by clinical stage, age, gender, and grade retained the statistical significance. The methylation signature was significantly correlated with immune cells and anti-tumor immune response. Moreover, gene expression corresponding to the candidate CpG locus was also significantly correlated with the survival rate of the patient. About 49% of the prognostic effects of methylation are mediated by affecting the expression of the corresponding genes. The prognostic characteristics of DNA methylation combined with clinical information provide a better prediction value tool for melanoma patients than the clinical information alone. However, more experiments are required to validate these findings. Overall, this signature presents a prospect of novel and wide-ranging applications for appropriate clinical adjuvant trails.

Bayesian Multi-View Clustering given complex inter-view structure

Multi-view datasets are becoming increasingly prevalent. These datasets consist of different modalities that provide complementary characterizations of the same underlying system. They can include heterogeneous types of information with complex relationships, varying degrees of missingness, and assorted sample sizes, as is often the case in multi-omic biological studies. Clustering multi-view data allows us to leverage different modalities to infer underlying systematic structure, but most existing approaches are limited to contexts in which entities are the same across views or have clear one-to-one relationships across data types with a common sample size. Many methods also make strong assumptions about the similarities of clusterings across views. We propose a Bayesian multi-view clustering approach (BMVC) which can handle the realities of multi-view datasets that often have complex relationships and diverse structure. BMVC incorporates known and complex many-to-many relationships between entities via a probabilistic graphical model that enables the joint inference of clusterings specific to each view, but where each view informs the others. Additionally, BMVC estimates the strength of the relationships between each pair of views, thus moderating the degree to which it imposes dependence constraints. We benchmarked BMVC on simulated data to show that it accurately estimates varying degrees of inter-view dependence when inter-view relationships are not limited to one-to-one correspondence. Next, we demonstrated its ability to capture visually interpretable inter-view structure in a public health survey of individuals and households in Puerto Rico following Hurricane Maria. Finally, we showed that BMVC clusters integrate the complex relationships between multi-omic profiles of breast cancer patient data, improving the biological homogeneity of clusters and elucidating hypotheses for functional biological mechanisms. We found that BMVC leverages complex inter-view structure to produce higher quality clusters than those generated by standard approaches. We also showed that BMVC is a valuable tool for real-world discovery and hypothesis generation.

Multiomics analysis for ferroptosisrelated genes as prognostic factors in cutaneous melanoma

OBJECTIVES

Melanoma is highly malignant and heterogeneous. It is essential to develop a specific prognostic model for improving the patients' survival and treatment strategies. Recent studies have shown that ferroptosis results from the overproduction of lipid peroxidation and is an iron-dependent form of programmed cell death. Despite this, ferroptosis-related genes (FRGs) and their clinical significances remain unknown in malignant melanoma. This study aims to assess the role of FRGs in melanoma, with the goal of developing a novel prognostic model that provides new insights into personalized treatment and improvement of therapeutic outcomes for melanoma.

METHODS

We systematically characterized the genetic alterations and mRNA expression of 73 FRGs in The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) dataset in this study. The results were validated with real-time RT-PCR and Western blotting. Subsequently, a multi-gene feature model was constructed using the TCGA-SKCM cohort. Melanoma patients were classified into a high-risk group and a low-risk group based on the feature model. As a final step, correlations between ferroptosis-related signatures and immune features, immunotherapy efficacy, or drug response were analyzed.

RESULTS

By analyzing melanoma samples from TCGA-SKCM dataset, FRGs exhibited a high frequency of genetic mutations and copy number variations (CNVs), significantly impacting gene expression. Additionally, compared with normal skin tissue, 30 genes with significantly differential expression were identified in melanoma tissues. A prognostic model related to FRGs, constructed using the LASSO Cox regression method, identified 13 FRGs associated with overall survival prognosis in patients and was validated with external datasets. Finally, functional enrichment and immune response analysis further indicated significant differences in immune cell infiltration, mutation burden, and hypoxia status between the high-risk group and the low-risk group, and the model was effective in predicting responses to immunotherapy and drug sensitivity.

CONCLUSIONS

This study develops a strong ferroptosis-related prognostic signature model which could put forward new insights into target therapy and immunotherapy for patients with melanoma.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

BACKGROUND

Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.

METHODS

Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.

RESULTS

A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.

CONCLUSIONS

This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.

TRIAL REGISTRATION

CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Translational research on drug development and biomarker discovery for hepatocellular carcinoma

Translational research plays a key role in drug development and biomarker discovery for hepatocellular carcinoma (HCC). However, unique challenges exist in this field because of the limited availability of human tumor samples from surgery, the lack of homogenous oncogenic driver mutations, and the paucity of adequate experimental models. In this review, we provide insights into these challenges and review recent advancements, with a particular focus on the two main agents currently used as mainstream therapies for HCC: anti-angiogenic agents and immunotherapy. First, we examine the pre-clinical and clinical studies to highlight the challenges of determining the optimal therapeutic combinations with biologically effective dosage for HCC. Second, we discuss biomarker studies focusing on anti-PD1/anti-PD-L1-based combination therapy. Finally, we discuss the progress made in our collective understanding of tumor immunology and in multi-omics analysis technology, which enhance our understanding of the mechanisms underlying immunotherapy, characterize different patient subgroups, and facilitate the development of novel combination approaches to improve treatment efficacy. In summary, this review provides a comprehensive overview of efforts in translational research aiming at advancing our understanding of and improving the treatment of HCC.

An atlas of cell-type-specific interactome networks across 44 human tumor types

BACKGROUND

Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors.

METHODS

We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions.

RESULTS

We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes.

CONCLUSIONS

This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.

Anoikis-related gene signature is associated with immune infiltration and predicts the prognosis of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common histological type of lung cancer. With the in-depth exploration of cell death manners, numerous studies found that anoikis is an important mechanism that associated with treatment. Therefore, we aimed to explore the prognostic value and treatment guidance of anoikis in NSCLC patients. In the current study, we first constructed a prognostic model based on the anoikis-related genes based on bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) dataset. Then, immuno-correlations of anoikis-related risk scores (ARGRS) were analyzed. In addition, HMGA1, a risky gene in ARGRS, was further explored to define its expression and immuno-correlation. Results showed that patients with higher ARGRS had worse clinical outcomes. Moreover, the five genes in the prognostic model were all highly expressed on tumor cells. Moreover, further analysis found that the ARGRS was negatively correlated with ImmuneScore, but positively with tumor purity. Besides, patients in the ARGRS-high group had lower levels of immunological characteristics, such as the immune-related signaling pathways and subpopulations. Additionally, in the immunotherapy cohorts, patients with the ARGRS-high phenotype were more resistant to immunotherapy and tended to not achieve remission after treatment. Last, HMGA1 was chosen as the representative biomarker, and analysis of the in-house cohort showed that HMGA1 was highly expressed in tumor tissues and correlated with decreased T cell infiltration. To sum up, ARGRS was correlated with a desert tumor microenvironment and identified immune-cold tumors, which can be a novel biomarker for the recognition of immunological characteristics and an immunotherapeutic response in NSCLC.

Comprehensive analysis reveals the prognostic and immunogenic characteristics of DNA methylation regulators in lung adenocarcinoma

DNA methylation regulators (DMRs) play a key role in DNA methylation, thus mediating tumor occurrence, metastasis, and immunomodulation. However, the effects of DMRs on clinical outcomes and immunotherapy response remain unexplored in lung adenocarcinoma (LUAD). In this study, eight LUAD cohorts and one immunotherapeutic cohort of lung cancer were utilized. We constructed a DNA methylation regulators-related signature (DMRRS) using univariate and multivariate COX regression analysis. The DMRRS-defined low-risk group was preferentially associated with favorable prognosis, tumor-inhibiting microenvironment, more sensitivity to several targeted therapy drugs, and better immune response. Afterward, the prognostic value and predictive potential in immunotherapy response were validated. Collectively, our findings uncovered that the DMRRS was closely associated with the tumor immune microenvironment and could effectively predict the clinical outcome and immune response of LUAD patients.

DNA-methylome-derived epigenetic fingerprint as an immunophenotype indicator of durable clinical immunotherapeutic benefits in head and neck squamous cell carcinoma

BACKGROUND

Cancer immunotherapy provides durable response and improves survival in a subset of head and neck squamous cell carcinoma (HNSC) patients, which may due to discriminative tumor microenvironment (TME). Epigenetic regulations play critical roles in HNSC tumorigenesis, progression, and activation of functional immune cells. This study aims to identify an epigenetic signature as an immunophenotype indicator of durable clinical immunotherapeutic benefits in HNSC patients.

METHODS

Unsupervised consensus clustering approach was applied to distinguish immunophenotypes based on five immune signatures in The Cancer Genome Atlas (TCGA) HNSC cohort. Two immunophenotypes (immune 'Hot' and immune 'Cold') that had different TME features, diverse prognosis, and distinct DNA methylation patterns were recognized. Immunophenotype-related methylated signatures (IPMS) were identified by the least absolute shrinkage and selector operation algorithm. Additionally, the IPMS score by deconvolution algorithm was constructed as an immunophenotype classifier to predict clinical outcomes and immunotherapeutic response.

RESULTS

The 'Hot' HNSC immunophenotype had higher immunoactivity and better overall survival (p = 0.00055) compared to the 'Cold' tumors. The immunophenotypes had distinct DNA methylation patterns, which was closely associated with HNSC tumorigenesis and functional immune cell infiltration. 311 immunophenotype-related methylated CpG sites (IRMCs) was identified from TCGA-HNSC dataset. IPMS score model achieved a strong clinical predictive performance for classifying immunophenotypes. The area under the curve value (AUC) of the IPMS score model reached 85.9% and 89.8% in TCGA train and test datasets, respectively, and robustness was verified in five HNSC validation datasets. It was also validated as an immunophenotype classifier for predicting durable clinical benefits (DCB) in lung cancer patients who received anti-PD-1/PD-L1 immunotherapy (p = 0.017) and TCGA-SKCM patients who received distinct immunotherapy (p = 0.033).

CONCLUSIONS

This study systematically analyzed DNA methylation patterns in distinct immunophenotypes to identify IPMS with clinical prognostic potential for personalized epigenetic anticancer approaches in HNSC patients. The IPMS score model may serve as a reliable epigenome prognostic tool for clinical immunophenotyping to guide immunotherapeutic strategies in HNSC.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Identification and validation of prognostic genes and immune landscape signatures based on a fatty acid oxidation‑related risk score model in glioma

Fatty acid oxidation (FAO) plays a crucial role in glioma metabolism and its interaction with the immune microenvironment. The aim of the present study was to investigate the relationship between FAO-related genes and glioma by constructing gene clusters using a glioma cohort. A total of 287 differentially expressed genes related to FAO were identified and the top 50 genes were selected based on their P-values. Subsequently, patients were classified into two distinct gene subtypes (A and B) based on these genes. Scores for each patient were calculated using the 50 genes and the patients were divided into the high and low-score groups accordingly. Patients in subtype B exhibited higher tumor grades and poor prognostic factors such as older age and worse survival rates. The high-score subgroup had unfavorable indicators, including isocitrate dehydrogenase 1 wild-type, high tumor grade and 1p19q non-codeleted, while immune checkpoint expression was generally higher in the high-score subgroup. The constructed scoring model was validated using an external dataset, and the tissue inhibitor of metalloproteinase 1 gene was identified through protein interaction analysis, suggesting its potential involvement in glioma malignancy and promotion of glioblastoma proliferation. In conclusion, FAO-related genes may contribute to tumor development through immune mechanisms and the present study may provide novel insights for potential therapeutic strategies in glioma treatment.

Comprehensive Analysis on Prognostic Signature Based on T Cell-Mediated Tumor Killing Related Genes in Gastric Cancer

Although immunotherapy is a valuable treatment for gastric cancer (GC), identifying the patients who would benefit most from this approach presents a challenge. In this study, GC patients were divided into two subtypes by consensus clustering according to T cell-mediated tumor killing related genes (TTKRGs), and there were significant differences in tumor-infiltrating immune cells, signaling pathways, and gene expression of immunomodulators and inhibitory immune checkpoints between the two subtypes. Then, we developed an individualized signature based on TTKRGs, and its clinical and predictive value in GC patients for chemotherapeutic and immunotherapeutic responses was assessed. We confirmed the expression levels of signature genes in GC tumor tissue using quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, to improve the accuracy of GC prognosis predictions, we established a nomogram. We further identified some compounds as sensitive drugs targeting GC risk groups. The signature showed significant predictive ability across RNA-seq, microarray, and qRT-PCR cohorts, which could assist in predicting survival, immunotherapeutic and chemotherapeutic outcomes in GC patients.

In-depth proteomic analysis identifies key gene signatures predicting therapeutic efficacy of anti-PD-1/PD-L1 monotherapy in non-small cell lung cancer

Background

Immunotherapy has opened up a new era of individualized treatment for non-small cell lung cancer (NSCLC) with negative driver gene mutations. Anti-programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies have been the main options for immunotherapy over the past decade. Screening for predictive markers of anti-PD-1/PD-L1-responsive patients remains a focus in the field of immunotherapy, especially on the protein level in which relevant proteomic biomarkers are still lacking.

Methods

We collected samples from 23 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy and were followed up for three years. The proteomic profile of the tumor was obtained by mass spectrometry (MS). Meanwhile, we combined the RNA sequencing (RNA-seq) data of 27 patients treated with anti-PD-1/PD-L1 therapy in a previous study to establish an integrated gene network. Weighted correlation network analysis (WGCNA) and elastic network were implemented to screen the top gene modules for predicting treatment-responsive patients. Gene expression related mutational patterns were also retrieved for validation in the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort.

Results

Our results showed the gene expression profile of MOXD1, PHAF1, KRT7, ANKRD30A, TMEM184A, KIR3DL1, and KCNK4 could better predict the durable response to anti-PD-1/PD-L1 therapy, with the specificity and sensitivity of 0.76 and 0.6, respectively. Besides, the mutational gene profile associated with these genes also suggested an association with favorable response in the MSKCC cohort. Patient-specific protein-protein interaction (PPI) network also indicated strong correlation among KRT7, TMEM184A and ANKRD30A.

Conclusions

Our study indicated that key gene signatures identified by machine learning model could be utilized for clinical screening of patients who might benefit from anti-PD-1 therapy. Further mechanistic investigations around these genes are warranted.

Complement and coagulation cascades are associated with prognosis and the immune microenvironment of lower-grade glioma

Background

Abnormal coagulation is a common feature of glioma. There is a strong correlation between coagulation and the complement system, named complement and coagulation cascades (CCC). However, the role of CCC genes in lower-grade glioma (LGG) remains unclear. This study aimed to investigate the role of CCC genes in LGG.

Methods

In total, 5,628 differential expressed genes were identified between 498 LGG tissues from The Cancer Genome Atlas (TCGA) and 207 normal brain tissues from Genotype-Tissue Expression Project (GTEx). Among them, 20 overlapped CCC genes were identified as differentially expressed CCC genes. Then, comprehensive bioinformatics analysis was used to investigate the role of CCC genes in LGG; 271 LGG tissues from the Chinese Glioma Genome Atlas (CGGA) were used as the validation dataset. Cell Counting Kit-8 (CCK8) proliferation assay, colony formation assay, and wound healing assay were conducted to explore the anti-glioma effect of the sensitive drugs we predicted.

Results

We constructed a risk signature consisting of six CCC genes, including F2R, SERPINA1, TFPI, C1QC, C2, and C3AR1. The CCC gene-based risk signature could accurately predict the prognosis of patients with LGG. In addition, we found that the JAK-STAT, NOD-like receptor, Notch, PI3K-Akt, and Rap1 signaling pathways might be activated and had crosstalk with CCC in the high-risk group. Our findings analyses demonstrated that samples in high- and low-risk groups had different immune landscapes. Moreover, patients in the high-risk group might have greater resistance to immunotherapy. We validated the accuracy of the risk signature in predicting immunotherapy response in two public immunotherapy cohorts, GSE135222 and GSE78220. By means of oncoPredict, MG-132, BMS-536924, PLX-4720, and AZD6482 were identified as potential sensitive drugs for high-risk patients, of which MG-132 was particularly recommended for high-risk patients. We performed in vitro experiments to explore the anti-glioma effect of MG-132, and the results demonstrated MG-132 could inhibit the proliferation and migration of glioma cells.

Conclusions

Our findings show that CCC genes are associated with the prognosis and immune infiltration of LGG and provide possible immunotherapeutic and novel chemotherapeutic strategies for patients with LGG based on the risk signature.

Deciphering the tumor-suppressive role of PSMB9 in melanoma through multi-omics and single-cell transcriptome analyses

Skin cutaneous melanoma (SKCM) poses a significant challenge in skin cancers. Recent immunotherapy breakthroughs have revolutionized melanoma treamtment, yet tumor heterogeneity persists as an obstacle. Epigenetic modifications orchestrated by DNA methylation contributed to tumorigenesis, thus potentially unveiling melanoma prognosis. Here, we identified an interferon-gamma (IFN-g) sensitive subtype, which possesses favorable outcomes, robust infiltration CD8+T cells, and IFN-g score in bulk RNA-seq profile. Subsequently, we established an IFN-g sensitivity signature based on machine learning. We validated that PSMB9 is strongly correlated with immunotherapy response in both methylation and expression cohorts in this 10-probe signature. We assumed that PSMB9 acts as a putative melanoma suppressor, for its activation of CD8+T cell; capacity to modulate IFN-γ secretion; and dynamics altering IFN-g receptors in bulk tissue. We performed single-cell RNA-seq on immunotherapy patients' tissue to uncover the nuanced role of PSMB9 in activating CD8T + cells, enhancing IFN-g, and influencing malignant cells receptors and transcriptional factors. Overexpress PSMB9 in two SKCM cell lines to mimic the hypomethylated state to approve our conjecture. Strong cell proliferation and migration inhibition were detected on both cells, indicating that PSMB9 is present in tumor cells and that high expression is detrimental to tumor growth and migration. Overall, comprehensive integrated analysis shows that PSMB9 emerges as a vital prognostic marker, acting predictive potential regarding immunotherapy in melanoma. This evidence not only reveals the multifaceted impact of PSMB9 on both malignant and immune cells but also serves as a prospective target for undergoing immunotherapeutic strategies in the future.