Single-cell analysis of multiple cancer types reveals differences in endothelial cells between tumors and normal tissues.

Endothelial cells (ECs) play an important role in tumor progression. Currently, the main target of anti-angiogenic therapy is the vascular endothelial growth factor (VEGF) pathway. Some patients do benefit from anti-VEGF/VEGFR therapy; however, a large number of patients do not have response or acquire drug resistance after treatment. Moreover, anti-VEGF/VEGFR therapy may lead to nephrotoxicity and cardiovascular-related side effects due to its action on normal ECs. Therefore, it is necessary to identify targets that are specific to tumor ECs and could be applied to various cancer types. We integrated single-cell RNA sequencing data from six cancer types and constructed a multi-cancer EC atlas to decode the characteristic of tumor ECs. We found that tip-like ECs mainly exist in tumor tissues but barely exist in normal tissues. Tip-like ECs are involved in the promotion of tumor angiogenesis and inhibition on anti-tumor immune responses. Moreover, tumor cells, myeloid cells, and pericytes are the main sources of pro-angiogenic factors. High proportion of tip-like ECs is associated with poor prognosis in multiple cancer types. We also identified that prostate-specific membrane antigen (PSMA) is a specific marker for tip-like ECs in all the cancer types we studied. In summary, we demonstrate that tip-like ECs are the main differential EC subcluster between tumors and normal tissues. Tip-like ECs may promote tumor progression through promoting angiogenesis while inhibiting anti-tumor immune responses. PSMA was a specific marker for tip-like ECs, which could be used as a potential target for the diagnosis and treatment of non-prostate cancers.

Comprehensive analysis of tumor necrosis factor-α-inducible protein 8-like 2 (TIPE2): A potential novel pan-cancer immune checkpoint.

Tumor necrosis factor-α-inducible protein 8-like 2 (TIPE2) is encoded by TNFAIP8L2 and is a newly identified negative regulator of natural and acquired immunity that plays a critical function in maintaining immune homeostasis. Recently, CAR-NK immune cell therapy has been a focus of major research efforts as a novel cancer therapeutic strategy. TIPE2 is a potential checkpoint molecule for immune cell maturation and antitumor immunity that could be used as a novel NK cell-based immunotherapeutic approach. In this study, we explored the expression of TNFAIP8L2 across various tumor types and found that TNFAIP8L2 was highly expressed in most tumor types and correlated with prognosis. Survival analysis showed that TNFAIP8L2 expression was predictive of improved survival in cervical-squamous-cell-carcinoma (CESC), sarcoma (SARC) and skin-cutaneous-melanoma (SKCM). Conversely, TNFAIP8L2 expression predicted poorer survival in acute myeloid leukemia (LAML), lower-grade-glioma (LGG), kidney-renal-clear-cell-carcinoma (KIRC) and uveal-melanoma (UVM). Analysis of stemness features and immune cell infiltration indicated that TNFAIP8L2 was significantly associated with cancer stem cell index and increased macrophage and dendritic cell infiltration. Our data suggest that TNFAIP8L2 may be a novel immune checkpoint biomarker across different tumor types, particularly in LAML, LGG, KIRC and UVM, and may have further utility as a potential target for immunotherapy.

Violations of proportional hazard assumption in Cox regression model of transcriptomic data in TCGA pan-cancer cohorts.

BACKGROUND: Cox proportional hazard regression (CPH) model relies on the proportional hazard (PH) assumption: the hazard of variables is independent of time. CPH has been widely used to identify prognostic markers of the transcriptome. However, the comprehensive investigation on PH assumption in transcriptomic data has lacked. RESULTS: The whole transcriptomic data of the 9,056 patients from 32 cohorts of The Cancer Genome Atlas and the 3 lung cancer cohorts from Gene Expression Omnibus were collected to construct CPH model for each gene separately for fitting the overall survival. An average of 8.5% gene CPH models violated the PH assumption in TCGA pan-cancer cohorts. In the gene interaction networks, both hub and non-hub genes in CPH models were likely to have non-proportional hazards. Violations of PH assumption for the same gene models were not consistent in 5 non-small cell lung cancer datasets (all kappa coefficients < 0.2), indicating that the non-proportionality of gene CPH models depended on the datasets. Furthermore, the introduction of log(t) or sqrt(t) time-functions into CPH improved the performance of gene models on overall survival fitting in most tumors. The time-dependent CPH changed the significance of log hazard ratio of the 31.9% gene variables. CONCLUSIONS: Our analysis resulted that non-proportional hazards should not be ignored in transcriptomic data. Introducing time interaction term ameliorated performance and interpretability of non-proportional hazards of transcriptome data in CPH.

Identification of SHCBP1 as a potential biomarker involving diagnosis, prognosis, and tumor immune microenvironment across multiple cancers.

Shc SH2-domain binding protein 1 (SHCBP1), a protein specific binding to SH2 domain of Src homolog and collagen homolog (Shc), takes part in the regulation of various signal transduction pathways, which has been reported to be associated with tumorigenesis and progression. However, the pathological mechanisms are not completely investigated. Thus, this study aimed to comprehensively elucidate the potential functions of SHCBP1 in multiple cancer types. The comprehensive analyses for SHCBP1 in various tumors, including gene expression, diagnosis, prognosis, immune-related features, genetic alteration, and function enrichment, were conducted based on multiple databases and analysis tools. SHCBP1 was upregulated in most types of cancers. The results of qRT-PCR had confirmed that SHCBP1 mRNA was significantly upregulated in lung adenocarcinoma (LUAD) and liver hepatocellular carcinoma (LIHC) cell lines. Based on the receiver operating characteristic (ROC) and survival analysis, SHCBP1 was considered as a potential diagnostic and prognostic biomarker. Furthermore, SHCBP1 expression was linked with tumor immunity and immunosuppressive microenvironment according to the correlation analysis of SHCBP1 expression with immune cells infiltration, immune checkpoint genes, and immune-related genes (MHC genes, chemokines, and chemokines receptors). Moreover, SHCBP1 expression correlated with tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens. The feature of SHCBP1 mutational landscape in pan-cancer was identified. Finally, we focused on investigating the clinical significance and the potential biological role of SHCBP1 in LUAD. Our study comprehensively uncovered that SHCBP1 could be identified as an immune-related biomarker for cancer diagnosis and prognosis, and a potential therapeutic target for tumor immunotherapy.

Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment.

Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.

Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach.

BACKGROUND: The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation. RESULTS: Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm. CONCLUSIONS: Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients.

BACKGROUND: Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. METHODS: Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. RESULTS: Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. CONCLUSION: Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

Integrative omics analysis reveals relationships of genes with synthetic lethal interactions through a pan-cancer analysis.

Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2-10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA-mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.

Rewired functional regulatory networks among miRNA isoforms (isomiRs) from let-7 and miR-10 gene families in cancer.

Classical microRNA (miRNA) has been so far believed as a single sequence, but it indeed contains multiple miRNA isoforms (isomiR) with various sequences and expression patterns. It is not clear whether these diverse isomiRs have potential relationships and whether they contribute to miRNA:mRNA interactions. Here, we aimed to reveal the potential evolutionary and functional relationships of multiple isomiRs based on let-7 and miR-10 gene families that are prone to clustering together on chromosomes. Multiple isomiRs within gene families showed similar functions to their canonical miRNAs, indicating selection of the predominant sequence. IsomiRs containing novel seed regions showed increased/decreased biological function depending on whether they had more/less specific target mRNAs than their annotated seed. Few gene ontology(GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were shared among the target genes of the annotated seeds and the novel seeds. Various let-7 isomiRs with novel seed regions may cause opposing drug responses despite the fact that they are generated from the same miRNA locus and have highly similar sequences. IsomiRs, especially the dominant isomiRs with shifted seeds, may disturb the coding-non-coding RNA regulatory network. These findings provide insight into the multiple isomiRs and isomiR-mediated control of gene expression in the pathogenesis of cancer.

PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer.

Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.