Integrated analysis of the M2 macrophage-related signature associated with prognosis in ovarian cancer

A new perspective on the therapeutic potential of tumor metastasis: targeting the metabolic interactions between TAMs and tumor cells

Tumor-associated macrophages (TAMs) undergo metabolic reprogramming, encompassing glucose, amino acid, fatty acid metabolism, tricarboxylic acid (TCA) cycle, purine metabolism, and autophagy, within the tumor microenvironment (TME). The metabolic interdependencies between TAMs and tumor cells critically influence macrophage recruitment, differentiation, M2 polarization, and secretion of epithelial-mesenchymal transition (EMT)-related factors, thereby activating intratumoral EMT pathways and enhancing tumor cell invasion and metastasis. Tumor cell metabolic alterations, including hypoxia, metabolite secretion, aerobic metabolism, and autophagy, affect the TME's metabolic landscape, driving macrophage recruitment, differentiation, M2 polarization, and metabolic reprogramming, ultimately facilitating EMT, invasion, and metastasis. Additionally, macrophages can induce tumor cell EMT by reprogramming their aerobic glycolysis. Recent experimental and clinical studies have focused on the metabolic interactions between macrophages and tumor cells to control metastasis and inhibit tumor progression. This review highlights the regulatory role of TAM-tumor cell metabolic codependencies in EMT, offering valuable insights for TAM-targeted therapies in highly metastatic tumors. Modulating the metabolic interplay between tumors and TAMs represents a promising therapeutic strategy for treating patients with metastatic cancers.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy

BACKGROUND

Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored.

METHOD

Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models.

RESULTS

We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1.

CONCLUSIONS

We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Immune Evasion in Cancer Is Regulated by Tumor-Asociated Macrophages (TAMs): Targeting TAMs

Recent advancements in cancer treatment have explored a variety of approaches to address the needs of patients. Recently, immunotherapy has evolved as an efficacious treatment for various cancers resistant to conventional therapies. Hence, significant milestones in immunotherapy were achieved clinically in a large subset of cancer patients. Unfortunately, some cancer types do not respond to treatment, and among the responsive cancers, some patients remain unresponsive to treatment. Consequently, there is a critical need to examine the mechanisms of immune resistance and devise strategies to target immune suppressor cells or factors, thereby allowing for tumor sensitivity to immune cytotoxic cells. M2 macrophages, also known as tumor-associated macrophages (TAMs), are of interest due to their role in suppressing the immune system and influencing antitumor immune responses through modulating T cell activity and immune checkpoint expression. TAMs are associated with signaling pathways that modulate the tumor microenvironment (TME), contributing to immune evasion. One approach targets TAMs, focusing on preventing the polarization of M1 macrophages into the protumoral M2 phenotype. Other strategies focus on direct or indirect targeting of M2 macrophages through understanding the interaction of TAMs with immune factors or signaling pathways. Clinically, biomarkers associated with TAMs' immune resistance in cancer patients have been identified, opening avenues for intervention using pharmacological agents or immunotherapeutic approaches. Ultimately, these multifaceted approaches are promising in overcoming immune resistance and improving cancer treatment outcomes.

Identification of a New m6A Regulator-Related Methylation Signature for Predicting the Prognosis and Immune Microenvironment of Patients with Pancreatic Cancer

Pancreatic cancer (PC) is a malignant tumor of the digestive system that has a bad prognosis. N6-methyladenosine (m6A) is involved in a wide variety of biological activities due to the fact that it is the most common form of mRNA modification in mammals. Numerous research has accumulated evidence suggesting that a malfunction in the regulation of m6A RNA modification is associated with various illnesses, including cancers. However, its implications in PC remain poorly characterized. The methylation data, level 3 RNA sequencing data, and clinical information of PC patients were all retrieved from the TCGA datasets. Genes associated with m6A RNA methylation were compiled from the existing body of research and made available for download from the m6Avar database. The LASSO Cox regression method was used to construct a 4-gene methylation signature, which was then used to classify all PC patients included in the TCGA dataset into either a low- or high-risk group. In this study, based on the set criteria of |cor| > 0.4 and p value < 0.05. A total of 3507 gene methylation were identified to be regulated by m6A regulators. Based on the univariate Cox regression analysis and identified 3507 gene methylation, 858 gene methylation was significantly associated with the patient's prognosis. The multivariate Cox regression analysis identified four gene methylation (PCSK6, HSP90AA1, TPM3, and TTLL6) to construct a prognosis model. Survival assays indicated that the patients in the high-risk group tend to have a worse prognosis. ROC curves showed that our prognosis signature had a good prediction ability on patient survival. Immune assays suggested a different immune infiltration pattern in patients with high- and low-risk scores. Moreover, we found that two immune-related genes, CTLA4 and TIGIT, were downregulated in high-risk patients. We generated a unique methylation signature that is related to m6A regulators and is capable of accurately predicting the prognosis for patients with PC. The findings might prove useful for therapeutic customization and the process of making medical decisions.

Identification of the Characteristic Genes and their Roles in Lung Adenocarcinoma Lymph Node Metastasis through Machine Learning Algorithm

Background. Lymph node metastasis is an important route of lung cancer metastasis and can significantly affect the survival of lung cancer. Methods. All the analysis was conducted out in the R software. Expression profile and clinical information of lung adenocarcinoma (LUAD) patients were downloaded from The Cancer Genome Atlas database. Results. In our study, we firstly identified the characteristic genes of lymph node metastasis in LUAD through two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression, and SVM-RFE algorithms. Ten characteristic genes were finally identified, including CRHR2, ITIH1, PRSS48, MAS1L, CYP4Z1, LMO1, TCP10L2, KRT78, IGFBP1, and PITX3. Next, we performed univariate Cox regression, LASSO regression, and multivariate Cox regression sequentially to construct a prognosis model based on MAS1L, TCP10L2, and CRHR2, which had a good prognosis prediction efficiency in both training and validation cohorts. Univariate and multivariate analysis indicated that our model is a risk factor independent of other clinical features. Pathway enrichment analysis showed that in the high-risk patients, the pathway of MYC target, unfolded protein response, interferon alpha response, DNA repair, reactive oxygen species pathway, and glycolysis were significantly enriched. Among three model genes, MAS1L aroused our interest and therefore was selected for further analysis. KM survival curves showed that the patients with higher MAS1L might have better disease-free survival and progression-free survival. Further, pathway enrichment, genomic instability, immune infiltration, and drug sensitivity analysis were performed to in-deep explore the role of MAS1L in LUAD. Conclusions. Results showed that the signature based on MAS1L, TCP10L2, and CRHR2 is a useful tool to predict prognosis and lung cancer lymph node metastasis.