Integrated analysis of tumor-associated macrophage infiltration and prognosis in ovarian cancer

Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer

PURPOSE

To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.

METHOD

The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.

RESULTS

A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.

CONCLUSION

VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.

Immunomic longitudinal profiling of the NeoPembrOv trial identifies drivers of immunoresistance in high-grade ovarian carcinoma

PD-1/PD-L1 blockade has so far shown limited survival benefit for high-grade ovarian carcinomas. By using paired samples from the NeoPembrOv randomized phase II trial (NCT03275506), for which primary outcomes are published, and by combining RNA-seq and multiplexed immunofluorescence staining, we explore the impact of NeoAdjuvant ChemoTherapy (NACT) ± Pembrolizumab (P) on the tumor environment, and identify parameters that correlated with response to immunotherapy as a pre-planned exploratory analysis. Indeed, i) combination therapy results in a significant increase in intraepithelial CD8+PD-1+ T cells, ii) combining endothelial and monocyte gene signatures with the CD8B/FOXP3 expression ratio is predictive of response to NACT + P with an area under the curve of 0.93 (95% CI 0.85-1.00) and iii) high CD8B/FOXP3 and high CD8B/ENTPD1 ratios are significantly associated with positive response to NACT + P, while KDR and VEGFR2 expression are associated with resistance. These results indicate that targeting regulatory T cells and endothelial cells, especially VEGFR2+ endothelial cells, could overcome immune resistance of ovarian cancers.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC

Background

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Methods

We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments.

Results

We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment.

Conclusions

We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Cryptotanshinone suppresses ovarian cancer via simultaneous inhibition of glycolysis and oxidative phosphorylation

Ovarian cancer is one of the most lethal cancers in female reproductive system due to heterogeneity and lack of effective treatment. Targeting aerobic glycolysis, a predominant energy metabolism of cancer cells has been recognized a novel strategy to overcome cancer cell growth. However, the capability of cancer cells to undergo metabolic reprogramming guarantees their survival even when glycolysis is inhibited. Here in this study, we have shown that Cryptotanshinone (CT), a lipid-soluble bioactive anticancer molecule of Salvia miltiorrhiza, inhibits both glycolysis and oxidative phosphorylation (OXPHOS) in ovarian cancer cells leading to growth suppression and apoptosis induction. Our mechanistic study revealed that CT decreased glucose uptake and lactate production, and inhibited the kinase activity of LDHA and HK2. The molecular docking study showed that CT could directly bind with GLUT1, LDHA, HK2, PKM2 and complex-1. The immunoblotting data showed that CT decreased the expression of aberrantly activated glycolytic proteins includingGLUT1, LDHA, HK2, and PKM2. Besides, we found that CT inhibited mitochondrial ComplexⅠ activity, decreased the ratio of NAD+/NADH, and suppressed the generation of ATP and induced activation of AMPK, which controls energy-reducing processes. These in vitro findings were further validated using xenograft model. The findings of in vivo studies were in line with in vitro studies. Taken together, CT effectively suppressed glycolysis and OXPHOS, inhibited growth and induced apoptosis in ovarian cancer cells both in vitro and in vivo study models.

Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?

High-grade serous tubo-ovarian cancer (HGSTOC) is the most lethal tumor of the female genital tract. The foregoing therapy consists of cytoreduction followed by standard platinum/taxane chemotherapy; alternatively, for primary unresectable tumors, neo-adjuvant platinum/taxane chemotherapy followed by delayed interval cytoreduction. In patients with suboptimal surgery or advanced disease, different forms of targeted therapy have been accepted or tested in clinical trials. Studies on HGSTOC discovered its genetic and proteomic heterogeneity, epigenetic regulation, and the role of the tumor microenvironment. These findings turned attention to the fact that there are several distinct primary tumor subtypes of HGSTOC and the unique biology of primary, metastatic, and recurrent tumors may result in a differential drug response. This results in both chemo-refractoriness of some primary tumors and, what is significantly more frequent and destructive, secondary chemo-resistance of metastatic and recurrent HGSTOC tumors. Treatment possibilities for platinum-resistant disease include several chemotherapeutics with moderate activity and different targeted drugs with difficult tolerable effects. Therefore, the question appears as to why different subtypes of ovarian cancer are predominantly treated based on the same therapeutic schemes and not in an individualized way, adjusted to the biology of a specific tumor subtype and temporal moment of the disease. The paper reviews the genomic, mutational, and epigenetic signatures of HGSTOC subtypes and the tumor microenvironment. The clinical trials on personalized therapy and the overall results of a new, comprehensive approach to personalized therapy for ovarian cancer have been presented and discussed.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer

BACKGROUND

The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.

METHODS

Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.

RESULTS

Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.

CONCLUSIONS

The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

Identification of M2-like macrophage-related signature for predicting the prognosis, ecosystem and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma is one of the most common malignancies worldwide, representing a big health-care challenge globally. M2-like macrophages are significantly correlated with tumor progression, metastasis and treatment resistance.

METHODS

Integrative 10 machine learning algorithms were performed to developed a M2-like macrophage related prognostic signature (MRPS). Single-cell RNA-sequencing analysis was performed to dissect the ecosystem of HCC. Several approaches, including TIDE score, immunophenoscore, TMB score and tumor escape score were used to evaluate the predictive role of MRPS in immunology response.

RESULTS

The optimal MRPS constructed by the combination of stepCox + superPC algorithm served as an independent risk factor and showed stable and powerful performances in predicting the overall survival rate of HCC patients with 2-, 3-, and 4-year AUCs of 0. 763, 0.751, and 0.699 in TCGA cohort. HCC patients with low risk score possessed a more interaction of immunoactivated cells, including NK, CD8+ cytotoxic T, and activated B, and a less interaction of immunosuppressive cells, including Treg, CD4+ exhauster T, and M2-like macrophage. Low risk score indicated a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score in HCC, suggesting a better immunotherapy response. The IC50 value of docetaxel, gemcitabine, crizotinib and Osimertinib in HCC with high risk score were lower versus that with low risk score. HCC patients with high risk score had a higher score of cancer-related hallmarks, including angiogenesis, DNA repair, EMT, glycolysis, and NOTCH signaling.

CONCLUSION

Our study proposed a novel MRPS for predicting the prognosis, ecosystem and immunotherapy response in HCC.

A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer

Prostate cancer (PCa) is one of the most common male cancers worldwide and one of the deadliest if unsuccessfully treated. Τhe need for reliable, easily accessible immune-related molecular biomarkers that could be combined with clinically defined criteria, including PSA and Gleason score, to accurately predict PCa patients' clinical outcomes is emerging. Herein, we describe for the first time a blood-identified immune-related gene signature comprising eight upregulated multi-functional genes associated with poor prognosis. Next-generation sequencing (NGS) analysis of PCa patients' peripheral blood samples revealed a more than three-fold upregulation of each of the eight genes as compared to samples originating from healthy donors. The construction of gene and protein interaction networks revealed different extents of the functional implications of these genes in the regulation of cell proliferation and immune responses. Analysis of the available data from The Cancer Genome Atlas (TCGA) regarding gene expression and survival of prostate adenocarcinoma (PRAD) and pan-cancer (PANCAN) patients revealed that intra-tumoral upregulation of this eight-gene signature (8-GS) was associated with poor 5-year progression-free intervals in PCa patients, even in those with high Gleason scores, and also with an unfavorable prognosis for cancer patients irrespective of the cancer type and even in the early stages. These observations suggest that further investigation of the 8-GS prospectively in randomized clinical trials, in which clinical benefit in terms of evaluating time to disease progression can be assessed, is warranted.

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis.

METHODS

STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC).

RESULTS

STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients.

CONCLUSION

In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

"DEPHENCE" system-a novel regimen of therapy that is urgently needed in the high-grade serous ovarian cancer-a focus on anti-cancer stem cell and anti-tumor microenvironment targeted therapies

Ovarian cancer, especially high-grade serous type, is the most lethal gynecological malignancy. The lack of screening programs and the scarcity of symptomatology result in the late diagnosis in about 75% of affected women. Despite very demanding and aggressive surgical treatment, multiple-line chemotherapy regimens and both approved and clinically tested targeted therapies, the overall survival of patients is still unsatisfactory and disappointing. Research studies have recently brought some more understanding of the molecular diversity of the ovarian cancer, its unique intraperitoneal biology, the role of cancer stem cells, and the complexity of tumor microenvironment. There is a growing body of evidence that individualization of the treatment adjusted to the molecular and biochemical signature of the tumor as well as to the medical status of the patient should replace or supplement the foregoing therapy. In this review, we have proposed the principles of the novel regimen of the therapy that we called the "DEPHENCE" system, and we have extensively discussed the results of the studies focused on the ovarian cancer stem cells, other components of cancer metastatic niche, and, finally, clinical trials targeting these two environments. Through this, we have tried to present the evolving landscape of treatment options and put flesh on the experimental approach to attack the high-grade serous ovarian cancer multidirectionally, corresponding to the "DEPHENCE" system postulates.

Crosstalk of Immune Cells and Platelets in an Ovarian Cancer Microenvironment and Their Prognostic Significance

Ovarian cancer (OC) is one of the deadliest gynecological cancers, largely due to the fast development of metastasis and drug resistance. The immune system is a critical component of the OC tumor microenvironment (TME) and immune cells such as T cells, NK cells, and dendritic cells (DC) play a key role in anti-tumor immunity. However, OC tumor cells are well known for evading immune surveillance by modulating the immune response through various mechanisms. Recruiting immune-suppressive cells such as regulatory T cells (Treg cells), macrophages, or myeloid-derived suppressor cells (MDSC) inhibit the anti-tumor immune response and promote the development and progression of OC. Platelets are also involved in immune evasion by interaction with tumor cells or through the secretion of a variety of growth factors and cytokines to promote tumor growth and angiogenesis. In this review, we discuss the role and contribution of immune cells and platelets in TME. Furthermore, we discuss their potential prognostic significance to help in the early detection of OC and to predict disease outcome.

Cancer Immunology: Immune Escape of Tumors-Expression and Regulation of HLA Class I Molecules and Its Role in Immunotherapies

The addition of "avoiding immune destruction" to the hallmarks of cancer demonstrated the importance of cancer immunology and in particular the role of immune surveillance and escape from malignancies. However, the underlying mechanisms contributing to immune impairment and immune responses are diverse. Loss or reduced expression of the HLA class I molecules are major characteristics of human cancers resulting in an impaired recognition of tumor cells by CD8 + cytotoxic T lymphocytes. This is of clinical relevance and associated with worse patients outcome and limited efficacy of T-cell-based immunotherapies. Here, we summarize the role of HLA class I antigens in cancers by focusing on the underlying molecular mechanisms responsible for HLA class I defects, which are caused by either structural alterations or deregulation at the transcriptional, posttranscriptional, and posttranslational levels. In addition, the influence of HLA class I abnormalities to adaptive and acquired immunotherapy resistances will be described. The in-depth knowledge of the different strategies of malignancies leading to HLA class I defects can be applied to design more effective cancer immunotherapies.

Role of tumor-associated macrophages in common digestive system malignant tumors

Many digestive system malignant tumors are characterized by high incidence and mortality rate. Increasing evidence has revealed that the tumor microenvironment (TME) is involved in cancer initiation and tumor progression. Tumor-associated macrophages (TAMs) are a predominant constituent of the TME, and participate in the regulation of various biological behaviors and influence the prognosis of digestive system cancer. TAMs can be mainly classified into the antitumor M1 phenotype and protumor M2 phenotype. The latter especially are crucial drivers of tumor invasion, growth, angiogenesis, metastasis, immunosuppression, and resistance to therapy. TAMs are of importance in the occurrence, development, diagnosis, prognosis, and treatment of common digestive system malignant tumors. In this review, we summarize the role of TAMs in common digestive system malignant tumors, including esophageal, gastric, colorectal, pancreatic and liver cancers. How TAMs promote the development of tumors, and how they act as potential therapeutic targets and their clinical applications are also described.

Model for predicting immunotherapy based on M2 macrophage infiltration in TNBC

Introduction

Compared to other types of breast cancer, triple-negative breast cancer (TNBC) does not effectively respond to hormone therapy and HER2 targeted therapy, showing a poor prognosis. There are currently a limited number of immunotherapeutic drugs available for TNBC, a field that requires additional development.

Methods

Co-expressing genes with M2 macrophages were analyzed based on the infiltration of M2 macrophages in TNBC and the sequencing data in The Cancer Genome Atlas (TCGA) database. Consequently, the influence of these genes on the prognoses of TNBC patients was analyzed. GO analysis and KEGG analysis were performed for exploring potential signal pathways. Lasso regression analysis was conducted for model construction. The TNBC patients were scored by the model, and patients were divided into high- and low-risk groups. Subsequently, the accuracy of model was further verified using GEO database and patients information from the Cancer Center of Sun Yat-sen University. On this basis, we analyzed the accuracy of prognosis prediction, correlation with immune checkpoint, and immunotherapy drug sensitivity in different groups.

Results

Our findings revealed that OLFML2B, MS4A7, SPARC, POSTN, THY1, and CD300C genes significantly influenced the prognosis of TNBC. Moreover, MS4A7, SPARC, and CD300C were finally determined for model construction, and the model showed good accuracy in prognosis prediction. And 50 immunotherapy drugs with therapeutic significance in different groups were screened, which were assessed possible immunotherapeutics that have potential application and demonstrated the high precision of our prognostic model for predictive analysis.

Conclusion

MS4A7, SPARC, and CD300C, the three main genes used in our prognostic model, offer good precision and clinical application potential. Fifty immune medications were assessed for their ability to predict immunotherapy drugs, providing a novel approach to immunotherapy for TNBC patients and a more reliable foundation for applying drugs in subsequent treatments.

Targeting tumor-associated macrophages for successful immunotherapy of ovarian carcinoma

Epithelial ovarian cancer (EOC) is among the top five causes of cancer-related death in women, largely reflecting early, prediagnosis dissemination of malignant cells to the peritoneum. Despite improvements in medical therapies, particularly with the implementation of novel drugs targeting homologous recombination deficiency, the survival rates of patients with EOC remain low. Unlike other neoplasms, EOC remains relatively insensitive to immune checkpoint inhibitors, which is correlated with a tumor microenvironment (TME) characterized by poor infiltration by immune cells and active immunosuppression dominated by immune components with tumor-promoting properties, especially tumor-associated macrophages (TAMs). In recent years, TAMs have attracted interest as potential therapeutic targets by seeking to reverse the immunosuppression in the TME and enhance the clinical efficacy of immunotherapy. Here, we review the key biological features of TAMs that affect tumor progression and their relevance as potential targets for treating EOC. We especially focus on the therapies that might modulate the recruitment, polarization, survival, and functional properties of TAMs in the TME of EOC that can be harnessed to develop superior combinatorial regimens with immunotherapy for the clinical care of patients with EOC.

IL2RA+VSIG4+ tumor-associated macrophage is a key subpopulation of the immunosuppressive microenvironment in anaplastic thyroid cancer

Extensive infiltration of tumor-associated macrophages was correlated poor prognosis in anaplastic thyroid cancer (ATC). However, the heterogeneity and characteristics of the ATC-associated macrophages (ATAMs) in ATC remain far from clear. We combined single-cell RNA-sequencing analysis and gene expression microarray datasets to assess the molecular signature of ATAMs. Compared with normal thyroid-associated macrophages (NTAMs), 778 differentially expressed genes (DEGs) significantly changed in ATAMs compared with NTAMs. These DEGs were correlated with oxidative phosphorylation (M2 phenotype) and phagocytosis (M1 phenotype). Moreover, ATAMs highly expressed pro-tumor genes associated with angiogenesis, fibrosis, metalloprotease activity, and metastasis. Notably, we identified one ATC-specific subset, IL2RA⁺ VSIG4⁺ ATAMs, co-expressed M1 and M2 markers. The infiltration of IL2RA⁺ VSIG4⁺ ATAMs showed strong correlation with BRAF and RAS signaling, and its high infiltration was associated with favorable prognosis in thyroid-cancer patients. IL2RA⁺ VSIG4⁺ ATAMs were associated with increased tumor-infiltrating lymphocytes (B cells, CD8⁺ T cells, Tregs). IL2RA⁺ VSIG4⁺ ATAMs interacted with CD8⁺ T cells and Tregs through immune checkpoints (such as LGALS9_HAVCR2), cytokines (such as CXCL10_CXCR3), and receptors (such as CSF1R_CSF1), thereby forming an immunosuppressive microenvironment. Multiplex immunohistochemistry staining and coculture experiment confirmed that ATC cancer cells were able to induce the polarization of IL2RA⁺ VSIG4⁺ ATAMs. Besides, we identified several novel ATC-specific immune checkpoint genes including the immunosuppressive molecule VSIG4, LAIR1, and LILRB2. Expression of VSIG4 was also significantly correlated with tumor-infiltrating lymphocytes (B cells, CD8⁺ T cells, Tregs). In conclusion, our study revealed an ATC-specific ATAM subset with bifunctional phenotype, which provided a comprehensive insight to delineate the molecular characteristics of ATC-associated macrophages.

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most widely distributed tumor in the world, and its immunotherapy is not practical. Neutrophil is one of a tumor’s most abundant immune cell groups. This research aimed to investigate the complex communication network in the immune microenvironment (TIME) of NSCLC tumors to clarify the interaction between immune cells and tumors and establish a prognostic risk model that can predict immune response and prognosis of patients by analyzing the characteristics of Neutrophil differentiation. Integrated Single-cell RNA sequencing (scRNA-seq) data from NSCLC samples and Bulk RNA-seq were used for analysis. Twenty-eight main cell clusters were identified, and their interactions were clarified. Next, four subsets of Neutrophils with different differentiation states were found, closely related to immune regulation and metabolic pathways. Based on the ratio of four housekeeping genes (ACTB, GAPDH, TFRC, TUBB), six Neutrophil differentiation-related genes (NDRGs) prognostic risk models, including MS4A7, CXCR2, CSRNP1, RETN, CD177, and LUCAT1, were constructed by Elastic Net and Multivariate Cox regression, and patients’ total survival time and immunotherapy response were successfully predicted and validated in three large cohorts. Finally, the causes of the unfavorable prognosis of NSCLC caused by six prognostic genes were explored, and the small molecular compounds targeted at the anti-tumor effect of prognostic genes were screened. This study clarifies the TIME regulation network in NSCLC and emphasizes the critical role of NDRGs in predicting the prognosis of patients with NSCLC and their potential response to immunotherapy, thus providing a promising therapeutic target for NSCLC.

Identification of Tumor Microenvironment Scoring Scheme Based on Bioinformatics Analysis of Immune Cell Infiltration Pattern of Ovarian Cancer

Background

Tumor microenvironment (TME) is the crucial mediator of tumor progression, and the TME model based on immune cell infiltration to characterize ovarian cancer is considered to be a promising strategy.

Methods

Sample data of three ovarian cancer cohorts were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The scores of 22 kinds of immune cells were calculated based on CIBERSORT, and the TME clusters (TMECs) of ovarian cancer was determined by ConsensusClusterPlus. Genomic subtype was identified by non-negative matrix factorization (NMF). A TME scoring scheme was constructed using k-means algorithm and principal component analysis (PCA) to quantify the TME infiltration pattern of individuals.

Results

Four TME subtypes of ovarian cancer samples were defined: TMEC1, TMEC2, TMEC3, and TMEC4. There were also significant differences in overall survival (OS) among the four TMEC, and the OS of TMEC3 was the longest. The difference analysis of TMEC3 and the other three TMECs respectively identified the DEGs and took the intersection, and 585 DEGs were obtained. Two genomic subtypes were identified by NMF based on the expression of 585 genes, which were called GeneC1 and GeneC2. The TME scoring scheme constructed by k-means and PCA algorithm was used to calculate the TME score of ovarian cancer in TCGA. High-TME score was significantly correlated with shorter survival time, older age, lower immunoactivated molecules, and immune checkpoint gene expression.

Conclusions

This study highlighted the complexity and diversity of TME infiltration patterns in ovarian cancer and constructed a set of TME scoring scheme to reveal TME infiltration patterns and provided new insights into the landscape of TME.

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

Transcriptome-wide N6-methyladenine methylation in granulosa cells of women with decreased ovarian reserve

Background

The emerging epitranscriptome plays an essential role in female fertility. As the most prevalent internal mRNA modification, N6-methyladenine (m6A) methylation regulate mRNA fate and translational efficiency. However, whether m6A methylation was involved in the aging-related ovarian reserve decline has not been investigated. Herein, we performed m6A transcriptome-wide profiling in the ovarian granulosa cells of younger women (younger group) and older women (older group).

Results

m6A methylation distribution was highly conserved and enriched in the CDS and 3’UTR region. Besides, an increased number of m6A methylated genes were identified in the older group. Bioinformatics analysis indicated that m6A methylated genes were enriched in the FoxO signaling pathway, adherens junction, and regulation of actin cytoskeleton. A total of 435 genes were differently expressed in the older group, moreover, 58 of them were modified by m6A. Several specific genes, including BUB1B, PHC2, TOP2A, DDR2, KLF13, and RYR2 which were differently expressed and modified by m6A, were validated using qRT-PCR and might be involved in the decreased ovarian functions in the aging ovary.

Conclusions

Hence, our finding revealed the transcriptional significance of m6A modifications and provide potential therapeutic targets to promote fertility reservation for aging women.