Characterization of the metabolic alteration-modulated tumor microenvironment mediated by TP53 mutation and hypoxia.

BACKGROUND: TP53 mutation and hypoxia play an essential role in cancer progression. However, the metabolic reprogramming and tumor microenvironment (TME) heterogeneity mediated by them are still not fully understood. METHODS: The multi-omics data of 32 cancer types and immunotherapy cohorts were acquired to comprehensively characterize the metabolic reprogramming pattern and the TME across cancer types and explore immunotherapy candidates. An assessment model for metabolic reprogramming was established by integration of multiple machine learning methods, including lasso regression, neural network, elastic network, and survival support vector machine (SVM). Pharmacogenomics analysis and in vitro assay were conducted to identify potential therapeutic drugs. RESULTS: First, we identified metabolic subtype A (hypoxia-TP53 mutation subtype) and metabolic subtype B (non-hypoxia-TP53 wildtype subtype) in hepatocellular carcinoma (HCC) and showed that metabolic subtype A had an "immune inflamed" microenvironment. Next, we established an assessment model for metabolic reprogramming, which was more effective compared to the traditional prognostic indicators. Then, we identified a potential targeting drug, teniposide. Finally, we performed the pan-cancer analysis to illustrate the role of metabolic reprogramming in cancer and found that the metabolic alteration (MA) score was positively correlated with tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) across cancer types. Meanwhile, we demonstrated that metabolic reprogramming mediated a potential immunotherapy-sensitive microenvironment in bladder cancer and validated it in an immunotherapy cohort. CONCLUSION: Metabolic alteration mediated by hypoxia and TP53 mutation is associated with TME modulation and tumor progression across cancer types. In this study, we analyzed the role of metabolic alteration in cancer and propose a predictive model for cancer prognosis and immunotherapy responsiveness. We also explored a potential therapeutic drug, teniposide.

FOXD1-dependent RalA-ANXA2-Src complex promotes CTC formation in breast cancer.

BACKGROUND: Early metastasis is a key factor contributing to poor breast cancer (BC) prognosis. Circulating tumor cells (CTCs) are regarded as the precursor cells of metastasis, which are ultimately responsible for the main cause of death in BC. However, to date molecular mechanisms underlying CTC formation in BC have been insufficiently defined. METHODS: RNA-seq was carried out in primary tissues from early-stage BC patients (with CTCs≥5 and CTCs = 0, respectively) and the validation study was conducted in untreated 80 BC patients. Multiple in vitro and in vivo models were used in functional studies. Luciferase reporter, ChIP-seq, CUT&Tag-seq, and GST-pulldown, etc. were utilized in mechanistic studies. CTCs were counted by the CanPatrol™ CTC classification system or LiquidBiospy™ microfluidic chips. ERK1/2 inhibitor SCH772984 was applied to in vivo treatment. RESULTS: Highly expressed FOXD1 of primary BC tissues was observed to be significantly associated with increased CTCs in BC patients, particularly in early BC patients. Overexpressing FOXD1 enhanced the migration capability of BC cells, CTC formation and BC metastasis, via facilitating epithelial-mesenchymal transition of tumor cells. Mechanistically, FOXD1 was discovered to induce RalA expression by directly bound to RalA promotor. Then, RalA formed a complex with ANXA2 and Src, promoting the interaction between ANXA2 and Src, thus increasing the phosphorylation (Tyr23) of ANXA2. Inhibiting RalA-GTP form attenuated the interaction between ANXA2 and Src. This cascade culminated in the activation of ERK1/2 signal that enhanced metastatic ability of BC cells. In addition, in vivo treatment with SCH772984, a specific inhibitor of ERK1/2, was used to dramatically inhibit the CTC formation and BC metastasis. CONCLUSION: Here, we report a FOXD1-dependent RalA-ANXA2-Src complex that promotes CTC formation via activating ERK1/2 signal in BC. FOXD1 may serve as a prognostic factor in evaluation of BC metastasis risks. This signaling cascade is druggable and effective for overcoming CTC formation from the early stages of BC.

Role of copper ionophore-induced death in immune microenvironment and clinical prognosis of ccRCC: An integrated analysis.

Background: Clear cell renal cell carcinoma (ccRCC) is a malignancy with a high incidence rate and poor prognosis worldwide. Copper ionophore-induced death (CID) plays an important role in cancer progression. Methods: One training and three validation datasets were acquired from TCGA, GEO and ArrayExpress. K-means clustering was conducted to identify the CID subtypes. The ESTIMATE and CIBERSORT algorithms were employed to illustrate the immune microenvironment of ccRCC. LASSO Cox regression was applied to construct the CID feature-based prognostic model. The immunotherapy cohort was acquired from the literature to explore the potential risk scores for predicting immunotherapy responsiveness. Results: Two CID-related cancer subtypes of ccRCC were identified that performed different immune microenvironment characteristics and prognosis. Based on the identified subtypes, we analyzed the biological heterogeneity and constructed a gene prognostic model. The prognostic model performed well in one training dataset, three validation datasets, and different clinical pathological groups. The prognostic model has a good potential for predicting cancer immune features and immunotherapy responsiveness. Conclusion: CID plays an important role in the tumor microenvironment progression of ccRCC. The robust gene prognostic model developed can help predict cancer prognosis, immune features, and immunotherapy responsiveness.

Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. METHODS: The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. RESULTS: A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones. CONCLUSION: The hypoxia-immune-based gene signature has prognostic potential in OSCC.

Heterogeneity analysis of the immune microenvironment in laryngeal carcinoma revealed potential prognostic biomarkers.

Laryngeal squamous cell cancer (LSCC) is the second most prevalent malignancy occurring in the head and neck with a high incidence and mortality rate. Immunotherapy has recently become an emerging treatment for cancer. It is therefore essential to explore the role of tumour immunity in laryngeal cancer. Our study first delineated and evaluated the comprehensive immune infiltration landscapes of the tumour microenvironment in LSCC. A hierarchical clustering method was applied to classify the LSCC samples into two groups (high- and low-infiltration groups). We found that individuals with low immune infiltration characteristics had significantly better survival than those in the high-infiltration group, possibly because of the elevated infiltration of immune suppressive cells, such as regulatory T cells and myeloid-derived suppressor cells, in the high-infiltration group. Differentially expressed genes between two groups were involved in some immune-related terms, such as antigen processing and presentation. A univariate Cox analysis and least absolute shrinkage and selection operator analysis were performed to identify an immune gene-set-based prognostic signature (IBPS) to assess the risk of LSCC. The prognostic model comprising six IBPSs was successfully verified to be robust in different cohorts. The expression of the six IBPSs was detected by immunohistochemistry in 110 cases of LSCC. In addition, different inflammatory profiles and immune checkpoint landscape of LSCC were found between two groups. Hence, our model could serve as a candidate immunotherapeutic biomarker and potential therapeutic target for laryngeal cancer.

Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non-small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ('high infiltration' cluster). The immune cells were responsible for a significantly favourable prognosis for the 'high infiltration' community. Cluster 1 had the lowest cytotoxic activity, tumour-infiltrating lymphocytes and interferon-gamma (IFN-γ), as well as immune checkpoint molecules expressions. In addition, MHC-I and immune co-stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune-related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.

Construction and Analysis of Human Diseases and Metabolites Network.

The relationship between aberrant metabolism and the initiation and progression of diseases has gained considerable attention in recent years. To gain insights into the global relationship between diseases and metabolites, here we constructed a human diseases-metabolites network (HDMN). Through analyses based on network biology, the metabolites associated with the same disorder tend to participate in the same metabolic pathway or cascade. In addition, the shortest distance between disease-related metabolites was shorter than that of all metabolites in the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic network. Both disease and metabolite nodes in the HDMN displayed slight clustering phenomenon, resulting in functional modules. Furthermore, a significant positive correlation was observed between the degree of metabolites and the proportion of disease-related metabolites in the KEGG metabolic network. We also found that the average degree of disease metabolites is larger than that of all metabolites. Depicting a comprehensive characteristic of HDMN could provide great insights into understanding the global relationship between disease and metabolites.

Hes1 is associated with long non-coding RNAs in colorectal cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) play important roles in the development and pathophysiology of colorectal cancer (CRC). Our previous study showed that Hes1 was involved in the self-renewal and tumorigenicity of stem-like cancer cells in CRC. METHODS: ArrayStar Human LncRNA/mRNA Expression Microarray Version 3.0 was used to detect lncRNA expression in CRC tissues compared with their matched non-tumoral tissues. RNA-binding protein immunoprecipitation and sequencing (RIP-seq) assay was used to detect lncRNAs binding to Hes1. Real-time qPCR was used to detect expression of specific lncRNAs in CRC tissues. RESULTS: We found significantly up-regulated as well as down-regulated lncRNAs in CRC tissues compared with their matched non-tumoral tissues. We also screened a number of lncRNAs interacting with Hes1 in CRC cells. Interestingly, we found several lncRNAs binding to Hes1 (such as, GNAS-AS1, RP11-89K10.1, and RP11-465L10.10) were up-regulated in CRC tissues showed by the tissue microarray. Next, we confirmed that Hes1 directly interacted with these lncRNAs using RIP-qPCR and RNA pulldown assay. Finally, we verified the expression of these lncRNAs in 32 CRC samples as well as the adjacent non-tumoral tissues using real-time qPCR. CONCLUSIONS: Based on these, we speculate that Hes1 interacts with one or more lncRNAs which contribute to the development and progression of CRC.