Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

BACKGROUND

Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.

METHODS

We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.

RESULTS

We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.

CONCLUSION

Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.

Comprehensive Multi-Omics Analysis of Copper Metabolism Related Molecular Subtypes and Prognostic Risk Stratification in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is the most common subtype of colorectal cancer, originating from glandular cells in the colon. Despite diagnostic and therapeutic advances, its prognosis remains poor. Copper, an essential micronutrient, is involved in tumorigenesis and other biological processes. In this study, we identified copper metabolism-related genes (CMRG) associated with COAD prognosis from TCGA and GEO databases and constructed a CMRG-based risk model. We assessed its clinical relevance through analyses of immune infiltration, immunotherapy response, and drug sensitivity. Single-cell sequencing revealed the spatial and cellular distribution of CMRG in COAD tissues, providing insight into their roles in the tumour microenvironment. COX19 was selected for further validation, and in vitro experiments (western blot, PCR, siRNA, colony formation, and Transwell assays) confirmed its role in promoting COAD cell invasion and proliferation. These findings highlight the involvement of copper metabolism in COAD progression and suggest potential targets for therapy.

Gene clusters-based pathway enrichment analysis identifies four pan-cancer subtypes with distinct molecular and clinical features

Pathways-based clustering methods have been proposed to explore tumor heterogeneity. However, such methods are currently disadvantageous in that specific pathways need to be explicitly claimed. We developed the PathClustNet algorithm, a pathway-based clustering method designed to identify cancer subtypes. This method first detects gene clusters and identifies overrepresented pathways associated with them. Based on the pathway enrichment scores, it reveals cancer subtypes by clustering analysis. We applied the method to TCGA pan-cancer data and identified four pan-cancer subtypes, termed C1, C2, C3 and C4. C1 exhibited high metabolic activity, favorable survival, and the lowest TP53 mutation rate. C2 had high immune, developmental, and stromal pathway activities, the lowest tumor purity, and intratumor heterogeneity. C3, which overexpressed cell cycle and DNA repair pathways, was the most genomically unstable and had the highest TP53 mutation rate. C4 overrepresented neuronal pathways, with the lowest response rate to chemotherapy, but the highest tumor purity and genomic stability. Furthermore, age showed positive correlations with most pathways but a negative correlation with neuronal pathways. Smoking, viral infections, and alcohol use were found to affect the activities of neuron, cell cycle, immune, stromal, developmental, and metabolic pathway in varying degrees. The PathClustNet algorithm unveils a novel classification of pan-cancer based on metabolic, immune, stromal, developmental, cell cycle, and neuronal pathways. These subtypes display different molecular and clinical features to warrant the investigation of precision oncology.

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine-learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C-index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single-cell RNA-seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high-risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.

Clinical signature and associated immune metabolism of NLRP1 in pan-cancer

Inflammations have been linked to tumours, suggesting a potential association between NLRP1 and cancer. Nevertheless, a systematic assessment of NLRP1's role across various cancer types currently absent. A comprehensive bioinformatic analysis was conducted to determine whether NLRP1 exhibits prognostic relevance linked to immune metabolism across various cancers. The study leveraged data from the TCGA and GTEx databases to explore the clinical significance, metabolic features, and immunological characteristics of NLRP1, employing various tools such as R, GEPIA, STRING and TISIDB. NLRP1 exhibited differential expression patterns across various cancers, with elevated expression correlating with a more favourable prognosis in lung adenocarcinoma (LUAD) and pancreatic adenocarcinoma (PAAD). Downregulation of NLRP1 reduced tumour metabolic activity in LUAD. Moreover, the mutational signature of NLRP1 was linked to a favourable prognosis. Interestingly, high NLRP1 expression inversely correlated with tumour stemness while positively correlating with tumour immune infiltration in various cancers including LUAD and PAAD. Through extensive big data analysis, we delved into the role of NLRP1 across various tumour types, constructing a comprehensive role map of its involvement in pan-cancer scenarios. Our findings highlight the potential of NLRP1 as a promising therapeutic target specifically in LUAD and PAAD.

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor-infiltrating immune cells in modulating the antitumor therapy response. However, the prognostic value of using these features in combination, and their potential for guiding individualized treatment for breast cancer, remains vague. To address this challenge, we recently developed the metastatic and immunogenomic risk score (MIRS), a comprehensive and user-friendly scoring system that leverages advanced bioinformatics methods to facilitate transcriptomics data analysis. To help users become familiar with the MIRS tool and apply it effectively in analyzing new breast cancer datasets, we describe detailed protocols that require no advanced programming skills. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Calculating a MIRS score from transcriptomics data Basic Protocol 2: Predicting clinical outcomes from MIRS scores Basic Protocol 3: Evaluating treatment responses and guiding therapeutic strategies in breast cancer patients Basic Protocol 4: Guidelines for utilizing the MIRS webtool.

Identification of immune-related tumor antigens and immune subtypes in osteosarcoma

Purpose

The development of tumor vaccines has become a hot topic in immunotherapy for osteosarcoma (OS); however, more tumor antigens with stronger immunogenicity need to be identified.

Methods

We downloaded six sets of gene expression profile data from online databases. The overexpressed genes were analyzed, intersected, and used to calculate the immune infiltration abundance in the TARGET OS dataset based on their expression matrix. Potential tumor antigen genes were identified based on whether they exhibited a high correlation with the antigen-presenting cells (APCs). A total of 1330 immune-related genes (IRGs) from the ImmPort website were retrieved based on their expression, and the Consensus Cluster method was used to obtain immune subtypes of the OS samples. Prognosis, immune microenvironment, and sensitivity to drugs were compared among the immune subtypes.

Results

In total, 680 genes were overexpressed in at least two datasets, of which TREM2, TNFRSF12A, and THY1 were positively correlated with different APCs. Based on the expression matrix of 1330 IRGs in TARGET-OS, two immune subtypes, IS1 and IS2, were identified. The prognosis of the IS1 subtype was better than that of IS2, the expression of immune checkpoint (ICP)-related genes was higher in patients with the IS1 subtype, and immune cell infiltration and sensitivity to 16 drugs were generally higher in IS1 subtype patients.

Conclusion

We identified three APC-correlated genes that can be considered to code for potential novel tumor antigens for OS vaccines. Two immune subtypes in patients with OS were identified to implement personalized treatments using mRNA vaccines.

Immune Cytolytic Activity and Strategies for Therapeutic Treatment

Intratumoral immune cytolytic activity (CYT), calculated as the geometric mean of granzyme-A (GZMA) and perforin-1 (PRF1) expression, has emerged as a critical factor in cancer immunotherapy, with significant implications for patient prognosis and treatment outcomes. Immune checkpoint pathways, the composition of the tumor microenvironment (TME), antigen presentation, and metabolic pathways regulate CYT. Here, we describe the various methods with which we can assess CYT. The detection and analysis of tumor-infiltrating lymphocytes (TILs) using flow cytometry or immunohistochemistry provide important information about immune cell populations within the TME. Gene expression profiling and spatial analysis techniques, such as multiplex immunofluorescence and imaging mass cytometry allow the study of CYT in the context of the TME. We discuss the significant clinical implications that CYT has, as its increased levels are associated with positive clinical outcomes and a favorable prognosis. Moreover, CYT can be used as a prognostic biomarker and aid in patient stratification. Altering CYT through the different methods targeting it, offers promising paths for improving treatment responses. Overall, understanding and modulating CYT is critical for improving cancer immunotherapy. Research into CYT and the factors that influence it has the potential to transform cancer treatment and improve patient outcomes.

Multiomics-based classifier to decipher immune landscape of uveal melanoma and predict patient outcomes

Uveal melanoma (UVM) prognosis and the possibilities for targeted therapy depend on a thorough understanding of immune infiltration features and the analysis of genomic and immune signatures. Leveraging multi-omics data from The Cancer Genome Atlas and GEO datasets, we employed an unsupervised clustering algorithm to categorize UVM into immune-related subgroups. Subsequent multi-omics analysis revealed two distinct UVM subtypes, each characterized by unique genomic mutations and immune microenvironment disparities. The aggressive UMCS2 subtype exhibited higher TNM stage and poorer survival, marked by elevated metabolism and increased immune infiltration. However, UMCS2 displayed heightened tumor mutational burden and immune dysfunction, leading to reduced responsiveness to immunotherapy. Importantly, these subtypes demonstrated differential sensitivity to targeted drugs due to significant variances in metabolic and immune environments, with UMCS2 displaying lower sensitivity. We developed a robust, subtype-specific marker-based risk scoring system. This system's diagnostic accuracy was validated through ROC curves, decision curve analysis, and calibration curves, all yielding satisfactory results. Additionally, cell experiments identified the pivotal function of HTR2B, the most crucial factor in this risk model. Knocking down HTR2B significantly reduced the activity, proliferation, and invasion ability of the UVM cell line. These findings underscored the impact of gene and immune microenvironment alterations in driving distinct molecular subtypes, emphasizing the need for precise treatment strategies. The molecular subtyping-based risk assessment system not only aids in predicting patient prognosis but also guides the identification of populations suitable for combined treatment. Molecules represented by HTR2B in the model may serve as effective therapeutic targets for UVM.

Identification of genes and pathways associated with menopausal status in breast cancer patients using two algorithms

BACKGROUND

Menopausal status has a known relationship with the levels of estrogen, progesterone, and other sex hormones, potentially influencing the activity of ER, PR, and many other signaling pathways involved in the initiation and progression of breast cancer. However, the differences between premenopausal and postmenopausal breast cancer patients at the molecular level are unclear.

METHODS

We retrieved eight datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with menopausal status in breast cancer patients were identified using the MAMA and LIMMA methods. Based on these validated DEGs, we performed Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) networks were constructed. We used DrugBank data to investigate which of these validated DEGs are targetable. Survival analysis was performed to explore the influence of these genes on breast cancer patient prognosis.

RESULTS

We identified 762 DEGs associated with menopausal status in breast cancer patients. PPI network analysis indicated that these genes are primarily involved in pathways such as the cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation pathways. Notably, several genes played roles in multiple signaling pathways and were associated with patient survival. These genes were also observed to be targetable according to the DrugBank database.

CONCLUSION

We identified DEGs associated with menopausal status in breast cancer patients. The association of these genes with several key pathways may promote understanding of the complex characterizations of breast cancer. Our findings offer valuable insights for developing new therapeutic strategies tailored to the menopausal status of breast cancer patients.

MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Tumor-infiltrating immune cells (TIICs) and metastasis are crucial characteristics for tumorigenesis. However, the potential role of their combination in breast cancer (BRCA) remains elusive. Herein, on the basis of quantifying TIICs and tumor metastasis together, we established a precise prognostic scoring system named metastatic and immunogenomic risk score (MIRS) using a neural network model. MIRS showed better performance when compared with other published signatures. MIRS stratifies patients into a high risk subtype (MIRShigh) and a low risk subtype (MIRSlow). The MIRShigh patients exhibit significantly lower survival rate compared with MIRSlow patients (P < 0.0001 ), higher response to chemotherapy, but lower response to immunotherapy. Conversely, higher infiltration level of TIICs and significantly prolonged survival (P = 0.029 ) are observed in MIRSlow patients, indicating sensitive response in immunotherapy. This work presents a promising indicator to guide treatment options of the BRCA population and provides a predicted webtool that is almost universally applicable to BRCA patients.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in colorectal cancer

BACKGROUND

Colorectal cancer is the second leading cause of cancer-related deaths, which imposes a significant societal burden. Regular screening and emerging molecular tumor markers have important implications for detecting the progression and development of colorectal cancer. Disulfidptosis is a newly defined type of programmed cell death triggered by abnormal accumulation of disulfide compounds in cells that stimulate disulfide stress. Currently, there is no relevant discussion on this mechanism and colorectal cancer.

METHODS

We classified the disulfidptosis-related subtypes of colorectal cancer using bioinformatics methods. Through secondary clustering of differentially expressed genes between subtypes, we identified characteristic genes of the disulfidptosis subtype, constructed a prognostic model, and searched for potential biomarkers through clinical validation.

RESULTS

Using disulfidptosis-related genes collected from the literature, we classified colorectal cancer patients from public databases into three subtypes. The differentially expressed genes between subtypes were clustered into three gene subtypes, and eight characteristic genes were screened to construct a prognostic model.

CONCLUSION

The disulfidptosis mechanism has important value in the classification of colorectal cancer patients, and characteristic genes selected based on this mechanism can serve as a new potential biological marker for colorectal cancer.

Identification of a novel competing endogenous RNA network and candidate drugs associated with ferroptosis in aldosterone-producing adenomas

Aldosterone-producing adenoma (APA), characterized by unilaterally excessive aldosterone production, is a common cause of primary aldosteronism. Ferroptosis, a recently raised iron-dependent mode of programmed cell death, has been involved in the development and therapy of various diseases. This study obtained datasets of the mRNA and lncRNA expression profiles for APA and adjacent adrenal gland (AAG) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and lncRNAs (DE lncRNAs) associated with ferroptosis were identified. Enrichment analyses indicated 89 ferroptosis-related DEGs were primarily enriched in ROS related processes and ferroptosis. Two physical cores, and one combined core were identified in the protein-protein interaction (PPI). DEGs and clinical traits were used in conjunction to screen eight hub genes from two hub modules and 89 DEGs. A competitive endogenous RNA (ceRNA) network was constructed via co-express analysis. Thereafter, molecular docking was used to identify potential targets. Two active compounds, QL-X-138 and MK-1775, bound to AURKA and DUOX1, respectively, with the lowest binding energies. Molecular dynamics simulation verified the stability of the two complexes. In summary, our studies identified eight hub genes and a novel ceRNA regulatory network associated with ferroptosis, wherein QL-X-138 and MK-1775 were considered to be potential drugs for treating APA.

High Tumor-Infiltrating Lymphocyte Count Is Associated with Distinct Gene Expression Profile and Longer Patient Survival in Advanced Ovarian Cancer

Cancer-related immunity plays a significant role in the outcome of ovarian cancer, but the exact mechanisms are not fully explored. A retrospective, real-life observational study was conducted including 57 advanced ovarian cancer patients. Immunohistochemistry for CD4+, CD8+, and CD45+ was used for assessing tumor-infiltrating immune cells. Furthermore, an immune-related gene expression assay was performed on 12-10 samples from patients with less than and more than 1-year overall survival (OS), respectively. A higher number of CD4+ (p = 0.0028) and CD45+ (p = 0.0221) immune cells within the tumor microenvironment were associated with longer OS of patients. In a multivariate setting, higher CD4+ T cell infiltration predicted longer OS (p = 0.0392). Twenty-three differentially expressed genes-involved in antigen presentation, costimulatory signaling, matrix remodeling, metastasis formation, and myeloid cell activity-were found when comparing the prognostic groups. It was found that tumor-infiltrating immune cell counts are associated with peculiar gene expression patterns and bear prognostic information in ovarian cancer. SOX11 expression emerged and was validated as a predictive marker for OS.

Bioinformatics-Based Identification of CircRNA-MicroRNA-mRNA Network for Calcific Aortic Valve Disease

Background

Calcific aortic valve disease (CAVD) is the most common native valve disease. Valvular interstitial cell (VIC) osteogenic differentiation and valvular endothelial cell (VEC) dysfunction are key steps in CAVD progression. Circular RNA (circRNAs) is involved in regulating osteogenic differentiation with mesenchymal cells and is associated with multiple disease progression, but the function of circRNAs in CAVD remains unknown. Here, we aimed to investigate the effect and potential significance of circRNA-miRNA-mRNA networks in CAVD.

Methods

Two mRNA datasets, one miRNA dataset, and one circRNA dataset of CAVD downloaded from GEO were used to identify DE-circRNAs, DE-miRNAs, and DE-mRNAs. Based on the online website prediction function, the common mRNAs (FmRNAs) for constructing circRNA-miRNA-mRNA networks were identified. GO and KEGG enrichment analyses were performed on FmRNAs. In addition, hub genes were identified by PPI networks. Based on the expression of each data set, the circRNA-miRNA-hub gene network was constructed by Cytoscape (version 3.6.1).

Results

32 DE-circRNAs, 206 DE-miRNAs, and 2170 DE-mRNAs were identified. Fifty-nine FmRNAs were obtained by intersection. The KEGG pathway analysis of FmRNAs was enriched in pathways in cancer, JAK-STAT signaling pathway, cell cycle, and MAPK signaling pathway. Meanwhile, transcription, nucleolus, and protein homodimerization activity were significantly enriched in GO analysis. Eight hub genes were identified based on the PPI network. Three possible regulatory networks in CAVD disease were obtained based on the biological functions of circRNAs including: hsa_circ_0026817-hsa-miR-211-5p-CACNA1C, hsa_circ_0007215-hsa-miR-1252-5p-MECP2, and hsa_circ_0007215-hsa-miR-1343-3p- RBL1.

Conclusion

The present bionformatics analysis suggests the functional effect for the circRNA-miRNA-mRNA network in CAVD pathogenesis and provides new targets for therapeutics.

The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer

Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

Detection and Genotyping of Human Papillomavirus (HPV16/18), Epstein–Barr Virus (EBV), and Human Cytomegalovirus (HCMV) in Endometrial Endometroid and Ovarian Cancers

The purpose of this study was to evaluate the relationship between human papillomavirus (HPV16/18), Epstein–Barr virus (EBV), and human cytomegalovirus (HCMV) infections and the occurrence of ovarian cancer in 48 women, of whom 36 underwent surgery and chemotherapy (group A), 12 in whom surgery was sufficient (group B), and 60 with endometroid endometrial cancer stage G1-G3 (group C), compared to patients in whom the uterus and its appendages were removed for nononcological reasons (control group). The detection of HPV, EBV, and HCMV in tumor tissue and normal tissue was performed using the real-time polymerase chain reaction (RT-PCR) technique. A statistically significantly higher risk of endometrial cancer was noted in patients infected only with HCMV (OR > 1; p < 0.05). In contrast, a significantly higher risk of ovarian cancer in group A was associated with HPV16, HPV18, and EBV (OR > 1; p < 0.05); a significantly higher risk of ovarian cancer in group B was associated with HPV18 and HMCV (OR > 1; p < 0.05). The obtained results suggest that HCMV infection is associated with the development of a stage of ovarian cancer when treatment can be completed with surgery alone. Meanwhile, EBV appears to be responsible for the development of ovarian cancer in more advanced stages.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining

BACKGROUND

Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications.

METHODS

We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes.

RESULTS

We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC_1-year = 0.714, ROC_3-year = 0.750, ROC_5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response.

CONCLUSION

The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

Prognostic Significance of the CXCLs and Its Impact on the Immune Microenvironment in Ovarian Cancer

The chemokine (C-X-C motif) ligand (CXCL) family in tumor tissue is closely related to tumor growth, metastasis, and survival. However, the differential expression profile and prognostic value of the CXCLs in ovarian cancer (OC) have not been elucidated. Therefore, we studied the expression levels and mutations of CXCLs in OC patient in TCGA and various public databases. The expression differences of CXCLs in OC cancer tissues and normal tissues were compared through the Gene Expression Profiling Interactive Analysis (GEPIA) database. The effect of CXCLs on OC prognosis was analyzed using the Kaplan-Meier curves in GEPIA database. The impact of CXCLs on immune infiltration and clinicopathological outcomes in OC was assessed using the TIMER algorithm. Compared with normal tissues, we found that eight CXCLs were significantly differentially expressed in OC. The expression levels of CXCL9 (P = 0.0201), CXCL11 (P = 0.0385), and CXCL13 (P = 0.0288) were significantly associated with tumor stage. CXCL13 was the only gene that significantly affected both disease-free survival (DFS) and overall survival (OS) in OC, and higher CXCL13 transcript levels implied longer DFS and OS. Although there was no significant impact on DFS, CXCL10 (P = 0.0079) and CXCL11 (P = 0.0011) expression levels had a significant effect on OS in OC. At the same time, CXCLs were significantly associated with several immune-infiltrating cells in OC tissues. The CXCLs were significantly associated with one or more immune-infiltrating cells in OC tissue. CXCL13 was differentially expressed in OC and significantly affected the prognosis of patients and was a potential marker of OC prognosis.

The role of MEOX1 in non-neoplastic and neoplastic diseases

Targeted gene therapy has shown durable efficacy in non-neoplastic and neoplastic patients. Therefore, finding a suitable target has become a key area of research. Mesenchyme homeobox 1 (MEOX1) is a transcriptional factor that plays a significant role in regulation of somite development. Evidence indicates that abnormalities in MEOX1 expression and function are associated with a variety of pathologies, including non-neoplastic and neoplastic diseases. MEOX1 expression is upregulated during progression of most diseases and plays a critical role in maintenance of the cellular phenotypes such as cell differentiation, cell cycle arrest and senescence, migration, and proliferation. Therefore, MEOX1 may become an important molecular target and therapeutic target. This review will discuss the current state of knowledge on the role of MEOX1 in different diseases.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

Tumor-Infiltrating Lymphocytes (TILs) in Epithelial Ovarian Cancer: Heterogeneity, Prognostic Impact, and Relationship with Immune Checkpoints

Epithelial ovarian cancers (EOC) are often diagnosed at an advanced stage with carcinomatosis and a poor prognosis. First-line treatment is based on a chemotherapy regimen combining a platinum-based drug and a taxane-based drug along with surgery. More than half of the patients will have concern about a recurrence. To improve the outcomes, new therapeutics are needed, and diverse strategies, such as immunotherapy, are currently being tested in EOC. To better understand the global immune contexture in EOC, several studies have been performed to decipher the landscape of tumor-infiltrating lymphocytes (TILs). CD8+ TILs are usually considered effective antitumor immune effectors that immune checkpoint inhibitors can potentially activate to reject tumor cells. To synthesize the knowledge of TILs in EOC, we conducted a review of studies published in MEDLINE or EMBASE in the last 10 years according to the PRISMA guidelines. The description and role of TILs in EOC prognosis are reviewed from the published data. The links between TILs, DNA repair deficiency, and ICs have been studied. Finally, this review describes the role of TILs in future immunotherapy for EOC.

High expression of CD8 in the tumor microenvironment is associated with PD-1 expression and patient survival in high-grade serous ovarian cancer

Objective:

The current study assesses programmed death-1 (PD-1) receptor expression and CD3, CD4, and CD8 tumor-infiltrating lymphocytes (TILs) in high-grade serous ovarian cancer (HGSOC) and associates our results with neoadjuvant chemotherapy history and disease prognosis.

Materials and Methods:

We included cases diagnosed with primary HGSOC with biopsy or surgical resection materials in this study. The immunoreactivity of CD3, CD4, CD8, and PD1 was assessed immunohistochemically in tumor tissue. We analyzed TILs in two predetermined groups of high and low TIL. The relationships between clinical characteristics, PD-1, and TIL were assessed. by the χ⁽²⁾ test or Fisher’s Exact test. We used Kaplan-Meier survival analysis and Cox proportional hazards regression model to the connection between survival and the amounts of TIL, and PD1.

Results:

Univariate analysis demonstrated that optimal debulking (p<0.001), early International Federation of Gynecology and Obstetrics stage (p=0.046), and higher scores of stromal CD8+ TIL expression (p=0.028) in tumor cells were all substantially correlated with longer disease-free survival (DFS), whereas the remaining variables analyzed, including PD-1 positivity, stromal CD3+, and CD4+ TILs, and intraepithelial CD3+, CD4+, and CD8+ TILs, were not correlated with DFS. Also, univariate analysis revealed that optimal debulking (p=0.010), and higher scores of stromal CD8+ TIL expression (p=0.021) in tumor cells were all substantially correlated with longer overall survival (OS).

Conclusion:

Higher scores of stromal CD8+ TILs are substantially correlated with DFS and OS in univariate analyses, whereas scores of stromal CD3+ and CD4+ TILs, and intraepithelial CD3+, CD4+, and CD8+ TILs are not correlated with DFS and OS in both univariate and multivariate analyses. Also, we found a significant association between PD-1 positivity and the scores of stromal CD3+ TILs and intraepithelial CD8+ TILs. However, no remarkable relationship was revealed between PD-1 positivity and the survival of HGSOC cases.

The Immune Subtypes and Landscape of Advanced-Stage Ovarian Cancer

Immunotherapy has played a significant role in the treatment of a variety of hematological and solid tumors, but its application in ovarian cancer (OC) remains unclear. This study aimed to identify immune subtypes of OC and delineate an immune landscape for selecting suitable patients for immunotherapy, thereby providing potent therapeutic targets for immunotherapy drug development. Three immune subtypes (IS1–IS3) with distinctive molecular, cellular, and clinical characteristics were identified from the TCGA and GSE32062 cohorts. Compared to IS1, IS3 has a better prognosis and exhibits an immunological “hot”. IS3, in contrast, exhibits an immunological “cold” and has a worse prognosis in OC patients. Moreover, gene mutations, immune modulators, CA125, CA199, and HE4 expression, along with sensitivity either to immunotherapy or chemotherapy, were significantly different among the three immune subtypes. The OC immune landscape was highly heterogeneous between individual patients. Poor prognosis was correlated with low expression of the hub genes CD2, CD3D, and CD3E, which could act not only as biomarkers for predicting prognosis, but also as potential immunotherapy targets. Our study elucidates the immunotyping and molecular characteristics of the immune microenvironment in OC, which could provide an effective immunotherapy stratification method for optimally selecting patients, and also has clinical significance for the development of new immunotherapy as well as rational combination strategies for the treatment of OC patients.

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 construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data

BACKGROUND

Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.

METHODS

The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method.

RESULTS

The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients.

CONCLUSIONS

Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

Identification of Potential Biomarkers of Platelet RNA in Glioblastoma by Bioinformatics Analysis

Objective

Glioblastoma is one of the most common and fatal malignancies in adults. Current treatment is still not optimistic. Glioblastoma (GBM) transports RNA to platelets in the blood system via microvesicles, suggesting that platelet RNA can be a potential diagnostic and therapeutic target. The roles of specific platelet RNAs in treatment of GBM are not well understood.

Methods

Platelet RNA profiling of 8 GBM and 12 normal samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified between tumors and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of up- and downregulated genes. miRNA was predicted by miRTarBase, TargetScan, and miRDB databases. circBase and circBank were used for circRNA prediction. ceRNA (circRNA-mRNA-miRNA) network was constructed to investigate the potential interactions.

Results

22 genes were upregulated and 9 genes were downregulated. There are only two genes (CCR7 and FAM102A) that connect to miRNAs (hsa-let-7a-5p, hsa-miR-1-3p). We assessed the overall survival rates by Kaplan-Meier plotter, and relative expression of GBM and subtypes for overlapped mRNA (CCR7 and FAM102A) were evaluated, and further, we obtained circRNAs (has-circ-0015164, hsa-circ-0003243) by circBank and circBase and bind sites through the CSCD database. Finally, a ceRNA network (circRNA-mRNA-miRNA) was constructed based on 2 miRNAs, 2 mRNAs, and 2 circRNAs by Cytoscape. This study focused on potential mRNA and ceRNA biomarkers to targeted treatment of GBM and provided ideas for clinical treatment through the combination of hematology and oncology.

Conclusion

The findings of this study contribute to better understand the relationship between GBM and the blood system (platelets) and might lay a solid foundation for improving GBM molecule and gene diagnosis and prognosis.

Clinical and molecular evaluation of patients with ovarian cancer in the context of drug resistance to chemotherapy

The present study aimed to evaluate changes in the expression patterns at the gene and protein levels associated with drug resistance. The study group included 48 women who had a histopathologically confirmed diagnosis of stage I-IV ovarian cancer, they were divided into two subgroups (groups A and B). In group A, there were 36 patients in whom surgical treatment was supplemented with first-line chemotherapy according to current standards. Within this patient group, 5 had stage I (14%), 5 had stage II (14%), 25 had stage III (69%), and 1 had stage IV ovarian cancer (3%). Drug resistance was found after the third cycle of chemotherapy in 17 patients (71%) and after the sixth cycle in 7 patients (29%). Group B included 12 women with type I ovarian cancer, including 11 with stage I and 1 patient with stage IV ovarian cancer. The oncological treatment required only surgery. The control group (C) included 50 women in whom the uterus and adnexa were surgically removed for non-oncological reasons. Significantly higher levels of carcinoma antigen 125 CA-125 and human epididymis protein 4 HE4 were observed in group A and in menopausal women. Moreover, drug resistance was associated with significantly higher levels of CA-125 (p < 0.05). The genes UBA2, GLO1, STATH, and TUFT1 were differentiated in test samples from control samples. Moreover, drug resistance was associated with significantly higher expression of GLO1. The results of these assessments indicated the strong link between UBA2 and hsa-miR-133a-3p and hsa-miR-133b; GLO1 and hsa-miR-561-5p; STATH and hsa-miR-137-3p and hsa-miR-580-3p; and TUFT1 and hsa-miR-1233-3p and hsa-miR-2052. Correlation analysis showed a significant correlation between CA-125 and HE4 levels. Moreover, a significant correlation between TUFT1 mRNA and UBA2, GLO1, STATH (negative correlation), and TUFT1 in relation to CA-125 and HE4 (p < 0.05) was noted in all patients. In view of the lack of screening tests for ovarian cancer, the occurrence of the described correlation may be inscribed as an attempt to establish an assay that meets the criteria of a screening test and thus increase the early diagnosis of ovarian cancer.

The Multi-Omics Landscape and Clinical Relevance of the Immunological Signature of Phagocytosis Regulators: Implications for Risk Classification and Frontline Therapies in Skin Cutaneous Melanoma

Simple Summary

In this study, we focused on exploring phagocytosis regulators’ expression and mutational characteristics in skin cutaneous melanoma samples and delineating two molecular subtypes based on expression characteristics. We determined the relationship between phagocytosis regulators and survival by survival analysis of molecular subtypes. We then constructed a survival model (PRRS) to further quantify the criteria. Moreover, we combined pathway analysis, immune infiltration analysis, and mutation analysis to deeply explore the effects of phagocytosis regulators on skin cutaneous melanoma samples.

Abstract

Tumor-associated macrophages (TAMs) have gained considerable attention as therapeutic targets. Monoclonal antibody treatments directed against tumor antigens contribute significantly to cancer cell clearance by activating macrophages to phagocytose tumor cells. Due to its complicated genetic and molecular pathways, skin cutaneous melanoma (SKCM) has not yet attained the expected clinical efficacy and prognosis when compared to other skin cancers. Therefore, we chose TAMs as an entrance point. This study aimed to thoroughly assess the dysregulation and regulatory role of phagocytosis regulators in SKCM, as well as to understand their regulatory patterns in SKCM. This study subtyped prognosis-related phagocytosis regulators to investigate prognostic differences between subtypes. Then, we screened prognostic factors and constructed phagocytosis-related scoring models for survival prediction using differentially expressed genes (DEGs) between subtypes. Additionally, we investigated alternative treatment options using chemotherapeutic drug response data and clinical cohort treatment data. We first characterized and generalized phagocytosis regulators in SKCM and extensively examined the tumor immune cell infiltration. We created two phagocytosis regulator-related system (PRRS) phenotypes and derived PRRS scores using a principal component analysis (PCA) technique. We discovered that subtypes with low PRRS scores had a poor prognosis and decreased immune checkpoint-associated gene expression levels. We observed significant therapeutic and clinical improvements in patients with higher PRRS scores. Our findings imply that the PRRS scoring system can be employed as an independent and robust prognostic biomarker, serving as a critical reference point for developing novel immunotherapeutic methods.

A Framework to Predict the Molecular Classification and Prognosis of Breast Cancer Patients and Characterize the Landscape of Immune Cell Infiltration

It is known that all current cancer therapies can only benefit a limited proportion of patients; thus, molecular classification and prognosis evaluation are critical for correctly classifying breast cancer patients and selecting the best treatment strategy. These processes usually involve the disclosure of molecular information like mutation, expression, and immune microenvironment of a breast cancer patient, which are not been fully studied until now. Therefore, there is an urgent clinical need to identify potential markers to enhance molecular classification, precision prognosis, and therapy stratification for breast cancer patients. In this study, we explored the gene expression profiles of 1,721 breast cancer patients through CIBERSORT and ESTIMATE algorithms; then, we obtained a comprehensive intratumoral immune landscape. The immune cell infiltration (ICI) patterns of breast cancer were classified into 3 separate subtypes according to the infiltration levels of 22 immune cells. The differentially expressed genes between these subtypes were further identified, and ICI scores were calculated to assess the immune landscape of BRCA patients. Importantly, we demonstrated that ICI scores correlate with patients' survival, tumor mutation burden, neoantigens, and sensitivity to specific drugs. Based on these ICI scores, we were able to predict the prognosis of patients and their response to immunotherapy. Together, these findings provide a realistic scenario to stratify breast cancer patients for precision medicine.

High-throughput and label-free enrichment of malignant tumor cells and clusters from pleural and peritoneal effusions using inertial microfluidics

Accurate and rapid diagnosis of malignant pleural and peritoneal effusions is critical due to potential association with advanced disease stages or progression. Traditional cytodiagnosis suffers from low efficiency and has difficulties in finding malignant tumor cells (MTCs) from a mass of exfoliated cells. Hence, a polymer microfluidic chip with a slanted spiral channel was employed for high-throughput and label-free enrichment of MTCs and MTC clusters from clinical malignant pleural and peritoneal effusions. The slanted spiral channel with trapezoidal cross-sections was fabricated by assembling two patterned polymer films of different thicknesses within one flow channel layer. After systematically exploring the effects of the particle size, effusion concentration, and flow rate on separation performance of the device, we realized the enrichment of MTCs from abundant blood cells in 2-fold diluted effusions. The results indicated that approximately 85% of the spiked tumor cells (A549 and MCF-7 cell lines) were recovered with high purities of over 37% at a high throughput of 2000 μL min^-1. In clinical applications, we successfully enriched 24-2691 MTCs per mL from the diluted malignant pleural and peritoneal effusions collected from four types of cancer patients (n = 22). More importantly, the MTC clusters were further purified from single MTCs using a higher flow rate of 3000 μL min^-1. Finally, we performed the rapid drug sensitivity test by coupling the microfluidic enrichment with CCK-8 assay. Our approach may serve as valuable assistance to accelerate cancer diagnosis and guide the selection of treatment medications.

Landscape of Infiltrated Immune Cell Characterization in Uveal Melanoma to Improve Immune Checkpoint Blockade Therapy

Background

Numerous studies indicated that tumor-infiltrated immune cells (TIC) in the microenvironment are substantially linked to immunotherapy response and cancer prognosis. However, systematic studies of infiltrated immune cell characterization in uveal melanoma (UM) for prognosis and immune checkpoint blockade therapy are lacking.

Methods

UM datasets were extracted from open access resources (TCGA and GEO databases). The tumor-infiltrated immune cells in the microenvironment were decoded by using the CIBERSORT algorithm, which was further applied to classify UM patients into subgroups using an unsupervised clustering method. The Boruta algorithm and principal component analysis were used to calculate the TIC scores for UM patients. Kaplan–Meier curves were plotted to prove the prognostic value of TIC scores. Besides, the correlations of the TIC score with clinical features, mutated characteristics, and the immune therapeutic response were subsequently investigated.

Results

As a result, we defined three subtypes among 171 UM patients according to the TIC profiles and then calculated the TIC score to characterize the immune patterns for all patients. We discovered that high-TIC score patients with low BAP1 and high EIF1AX mutations have a better prognosis than low-TIC score patients. Activation of immune inflammatory response and increase in immune checkpoint-related genes in high-TIC score patients may account for good prognosis and immunotherapy response. Three melanoma cohorts received immunotherapy, proving that high-TIC score patients have substantial clinical and immune therapeutic improvements. Besides, several potential therapeutic agents were identified in the low-TIC score group.

Conclusion

Our study afforded a comprehensive view of infiltrated immune cell characterization to elucidate different immune patterns of UM. We also established a robust TIC-score signature, which may work as a prognostic biomarker and immune therapeutic predictor.

The Impact of Surgical Treatment with Adjuvant Chemotherapy for Ovarian Cancer on Disorders in the Urinary System and Quality of Life in Women

Ovarian cancer is the fourth-most-common cause of death among all malignant cancers in women in Poland. This study aimed to compare the functioning of the urinary system and quality of life in women in the 12-month period following the completion of surgery or adjuvant treatment for ovarian cancer, with patients who underwent a hysterectomy for non-oncological reasons (control group). The study group consisted of 50 patients diagnosed with stage I−III ovarian cancer. Among 38 patients with type II ovarian cancer (group A), surgery followed by first-line chemotherapy was performed. Within this group of patients, 20 had stage I ovarian cancer, while 18 had stage II ovarian cancer. The study was performed at least 6 months after the final chemotherapy cycle, with no clinical, marker or radiological recurrence determined. On the other hand, in 12 patients with stage I type I ovarian cancer, oncological treatment consisted of only surgery, without the need for adjuvant chemotherapy, due to the low stage of the lesions (group B). In turn, the control group consisted of 50 women who underwent uterine removal for non-oncological reasons (group C). The assessment of quality of life was conducted using the questionnaires: Satisfaction with Life Scale (SWLS); Incontinence Impact Questionnaire, short form (IIQ-7); Urogenital Distress Inventory (UDI-6); and the Sexual Satisfaction Scale for 3, 6, 9, and 12 months after the conclusion of oncological treatment. During the follow-up, a significant reduction in the quality of everyday life and sexual life was noted among patients with ovarian cancer, more pronounced in group B, compared to the control group (p < 0.05). The risk of urinary incontinence is independent of the treatment regimen chosen for ovarian cancer. It is necessary to consider comprehensive psychological care and sexual therapy in patients with ovarian cancer and their families.

Immune-related Gene-based Prognostic Signature for the Risk Stratification Analysis of Breast Cancer

Background:

Breast cancer has plagued women for many years and caused many deaths around the world.

Method:

In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study.

Results:

Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion:

Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.

Two novel prognostic models for ovarian cancer respectively based on ferroptosis and necroptosis

Background

Platinum-resistant cases account for 25% of ovarian cancer patients. Our aim was to construct two novel prognostic models based on gene expression data respectively from ferroptosis and necroptosis, for predicting the prognosis of advanced ovarian cancer patients with platinum treatment.

Methods

According to the different overall survivals, we screened differentially expressed genes (DEGs) from 85 ferroptosis-related and 159 necroptosis-related gene expression data in the GSE32062 cohort, to establish two ovarian cancer prognostic models based on calculating risk factors of DEGs, and log-rank test was used for statistical significance test of survival data. Subsequently, we validated the two models in the GSE26712 cohort and the GSE17260 cohort. In addition, we took gene enrichment and microenvironment analyses respectively using limma package and GSVA software to compare the differences between high- and low-risk ovarian cancer patients.

Results

We constructed two ovarian cancer prognostic models: a ferroptosis-related model based on eight-gene expression signature and a necroptosis-related model based on ten-gene expression signature. The two models performed well in the GSE26712 cohort, but the performance of necroptosis-related model was not well in the GSE17260 cohort. Gene enrichment and microenvironment analyses indicated that the main differences between high- and low- risk ovarian cancer patients occurred in the immune-related indexes, including the specific immune cells abundance and overall immune indexes.

Conclusion

In this study, ovarian cancer prognostic models based on ferroptosis and necroptosis have been preliminarily validated in predicting prognosis of advanced patients treated with platinum drugs. And the risk score calculated by these two models reflected immune microenvironment. Future work is needed to find out other gene signatures and clinical characteristics to affect the accuracy and applicability of the two ovarian cancer prognostic models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09166-9.

HIF-1α Is a Rational Target for Future Ovarian Cancer Therapies

Ovarian cancer is the eighth most commonly diagnosed cancer among women worldwide. Even with the development of novel drugs, nearly one-half of the patients with ovarian cancer die within five years of diagnosis. These situations indicate the need for novel therapeutic agents for ovarian cancer. Increasing evidence has shown that hypoxia-inducible factor-1α(HIF-1α) plays an important role in promoting malignant cell chemoresistance, tumour metastasis, angiogenesis, immunosuppression and intercellular interactions. The unique microenvironment, crosstalk and/or interaction between cells and other characteristics of ovarian cancer can influence therapeutic efficiency or promote the disease progression. Inhibition of the expression or activity of HIF-1α can directly or indirectly enhance the therapeutic responsiveness of tumour cells. Therefore, it is reasonable to consider HIF-1α as a potential therapeutic target for ovarian cancer. In this paper, we summarize the latest research on the role of HIF-1α and molecules which can inhibit HIF-1α expression directly or indirectly in ovarian cancer, and drug clinical trials about the HIF-1α inhibitors in ovarian cancer or other solid malignant tumours.

Identification of an Immune-Related Prognostic Gene CLEC5A Based on Immune Microenvironment and Risk Modeling of Ovarian Cancer

Objective: To understand the immune characteristics of the ovarian cancer (OC) microenvironment and explore the differences of immune-related molecules and cells to establish an effective risk model and identify the molecules that significantly affected the immune response of OC, to help guide the diagnosis. Methods: First, we calculate the TMEscore which reflects the immune microenvironment, and then analyze the molecular differences between patients with different immune characteristics, and determine the prognostic genes. Then, the risk model was established by least absolute shrinkage and selection operator (LASSO) analysis and combined with clinical data into a nomogram for diagnosis and prediction. Subsequently, the potential gene CLEC5A influencing the immune response of OC was identified from the prognostic genes by integrative immune-stromal analysis. The genomic alteration was explored based on copy number variant (CNV) and somatic mutation data. Results: TMEscore was a prognostic indicator of OC. The prognosis of patients with high TMEscore was better. The risk model based on immune characteristics was a reliable index to predict the prognosis of patients, and the nomogram could comprehensively evaluate the prognosis of patients. Besides, CLEC5A was closely related to the abundance of immune cells, immune response, and the expression of immune checkpoints in the OC microenvironment. OC cells with high expression of CLEC5A increased the polarization of M2 macrophages. CLEC5A expression was significantly associated with TTN and CDK12 mutations and affected the copy number of tumor progression and immune-related genes. Conclusion: The study of immune characteristics in the OC microenvironment and the risk model can reveal the factors affecting the prognosis and guide the clinical hierarchical treatment. CLEC5A can be used as a potential key gene affecting the immune microenvironment remodeling of OC, which provides a new perspective for improving the effect of OC immunotherapy.

Bone-Forming Peptide-4 Induces Osteogenic Differentiation and VEGF Expression on Multipotent Bone Marrow Stromal Cells

Bone morphogenetic proteins (BMPs) have been widely used as treatment for bone repair. However, clinical trials on fracture repair have challenged the effectiveness of BMPs and suggested that delivery of multipotent bone marrow stromal cells (BMSCs) might be beneficial. During bone remodeling and bone fracture repair, multipotent BMSCs differentiate into osteoblasts or chondrocytes to stimulate bone formation and regeneration. Stem cell-based therapies provide a promising approach for bone formation. Extensive research has attempted to develop adjuvants as specific stimulators of bone formation for therapeutic use in patients with bone resorption. We previously reported for the first time bone-forming peptides (BFPs) that induce osteogenesis and bone formation. BFPs are also a promising osteogenic factor for prompting bone regeneration and formation. Thus, the aim of the present study was to investigate the underlying mechanism of a new BFP-4 (FFKATEVHFRSIRST) in osteogenic differentiation and bone formation. This study reports that BFP-4 induces stronger osteogenic differentiation of BMSCs than BMP-7. BFP-4 also induces ALP activity, calcium concentration, and osteogenic factors (Runx2 and osteocalcin) in a dose dependent manner in BMSCs. Therefore, these results indicate that BFP-4 can induce osteogenic differentiation and bone formation. Thus, treatment of multipotent BMSCs with BFP-4 enhanced osteoblastic differentiation and displayed greater bone-forming ability than BMP-7 treatment. These results suggest that BFP-4-stimulated cell therapy may be an efficient and cost-effective complement to BMP-7-based clinical therapy for bone regeneration and formation.

TAM-derived extracellular vesicles containing microRNA-29a-3p explain the deterioration of ovarian cancer

Extracellular vesicles (EVs) secreted from tumor-associated macrophages (TAMs) are known to generate an immune-suppressive environment conducive to the development of ovarian cancer (OC). We tried to elucidate the role of TAM-derived exosomal microRNA (miR)-29a-3p in OC. miR-29a-3p, forkhead box protein O3 (FOXO3), and programmed death ligand-1 (PD-L1) expression was determined and their interactions evaluated. EVs were isolated, followed by determination of the uptake of EVs by OC cells, after which the proliferation and immune escape facilities of the OC cells were determined. OC xenograft models were constructed with EVs in correspondence with in vivo experiments. Overexpressed miR-29a-3p was detected in OC, and miR-29a-3p promoted OC cell proliferation and immune escape. EVs derived from TAMs enhanced the proliferation of OC cells. miR-29a-3p was enriched in TAM-EVs, and TAM-EVs delivered miR-29a-3p into OC cells. Downregulated FOXO3 was identified in OC, whereas miR-29a-3p targeted FOXO3 to suppress glycogen synthase kinase 3β (GSK3β) activity via the serine/threonine protein kinase (AKT)/GSK3β pathway. Inhibition of TAM-derived exosomal miR-29a-3p decreased PD-L1 to inhibit OC progression through the FOXO3-AKT/GSK3β pathway in vitro and in vivo. Taken together, the current studies highlight the FOXO3-AKT/GSK3β pathway and the mechanism by which TAM-derived exosomal miR-29a-3p enhances the expression of PD-L1 to facilitate OC cell proliferation and immune escape.

Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance

Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.

Identification of prognostic genes and construction of a novel gene signature in the skin melanoma based on the tumor microenvironment

Skin melanoma remains a highly prevalent and yet deadly form of cancer, with the exact degree of melanoma-associated mortality being strongly dependent upon the local tumor microenvironment. The exact composition of stromal and immune cells within this microenvironmental region has the potential to profoundly impact melanoma progression and prognosis. As such, the present study was designed with the goal of clarifying the predictive relevance of stromal and immune cell-related genes in melanoma patients through comprehensive bioinformatics analyses. We therefore analyzed melanoma sample gene expression within The Cancer Genome Atlas database and employed the ESTIMATE algorithm as a means of calculating both stromal and immune scores that were in turn used for identifying differentially expressed genes (DEGs). Subsequently, univariate analyses were used to detect DEGs associated with melanoma patient survival, and through additional functional enrichment analyses, we determined that these survival-related DEGs are largely related to inflammatory and immune responses. A prognostic signature comprised of 10 genes (IL15, CCL8, CLIC2, SAMD9L, TLR2, HLA.DQB1, IGHV1-18, RARRES3, GBP4, APOBEC3G) was generated. This 10-gene signature effectively separated melanoma patients into low- and high-risk groups based upon their survival. These low- and high-risk groups also exhibited distinct immune statuses and differing degrees of immune cell infiltration. In conclusion, our results offer novel insights into a number of microenvironment-associated genes that impact survival outcomes in melanoma patients, potentially highlighting these genes as viable therapeutic targets.

ProTICS reveals prognostic impact of tumor infiltrating immune cells in different molecular subtypes

Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS.

Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis

Backgrounds

The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

Methods

We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

Results

A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

Conclusion

: This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.