Single-Cell Transcriptome Analysis Reveals Intratumoral Heterogeneity in ccRCC, which Results in Different Clinical Outcomes

Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis

Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC). Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features. Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients' prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability. Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

CYP2J2 Is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Cytochrome P450 family 2 subfamily J member 2 (CYP2J2), a member of the monooxygenase cytochrome P450 (CYP) family and the only member of the human CYP2J subfamily, has many functions, including regulation of oxidative stress, inflammation, apoptosis, and immune responses. However, its role in cancer development has not been clearly elucidated. In this study, expression levels of CYP2J2 in various cancer types were determined using the Oncomine, the Gene Expression Profiling Interactive Analysis (GEIPA), DriverDBv3, UALCAN, and Tumor Immune Estimation Resource (TIMER) databases. The prognostic value of CYP2J2 for KIRC was analyzed using GEPIA, UALCAN, OSkirc, and DriverDBv3 databases. We evaluated the expression levels of CYP2J2 transcript, protein, and promoter methylation at different clinical characteristics in KIRC through the UALCAN database. Simultaneously, CYP2J2 network-related functions were evaluated using the GeneMANIA interactive tool while the biological processes involved in CYP2J2 and its interactive genes were investigated through Metascape and FunRich. Then, we used TIMER to determine the correlation between CYP2J2 expression levels and immune infiltration levels in KIRC. In KIRC, the CYP2J2 gene, RNA, and protein were found to be overexpressed. However, the methylation level of CYP2J2 promoter in KIRC was lower than in normal tissues. Surprisingly, elevated expression levels of CYP2J2 exhibited better prognostic outcomes in KIRC. Evaluation of protein-protein interaction networks and biological processes revealed that CYP2J2 was principally involved in immune responses, apoptosis, and other metabolic processes. Moreover, we found that the expression levels of CYP2J2 were positively correlated with infiltration levels of B cells, CD8 + T cells, neutrophils, and dendritic cells in KIRC. Therefore, we speculated that the overexpression of CYP2J2 prolonged the survival outcome of KIRC patients, which may be related to the change of tumor immune microenvironment. Moreover, all these new understandings of CYP2J2 may provide important value for the early diagnosis and new targeted drug therapy of KIRC.

Single-Cell Transcriptome Analysis Reveals the M2 Macrophages and Exhausted T Cells and Intratumoral Heterogeneity in Triple-Negative Breast Cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly heterogeneous and invasive malignancy that is characterized by high recurrence and mortality rates as well as extremely poor prognosis.

OBJECTIVE

This study aimed to analyze T cells and macrophages in the tumor microenvironment with the aim of identifying targets with therapeutic potential.

METHOD

Single-cell sequencing data of TNBC patients from the GSE118389 dataset were analyzed to examine the immune environment and intratumoral heterogeneity of TNBC patients.

RESULT

Polarized alternatively activated macrophages (M2) and exhausted CD8+ T cells were identified in TNBC patients. Immunosuppressive checkpoint analysis revealed that levels of lymphocyte-activation gene 3 (LAG3) and T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) of exhausted T cells were significantly higher than levels of programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). This indicates that these markers are potential immunotherapy targets. Furthermore, analysis of significantly altered immune cell markers showed that several markers are associated with the prognosis of TNBC.

CONCLUSION

Overall, these findings demonstrate inter-tissue heterogeneity of TNBC and provide novel therapeutic targets for the treatment of TNBC.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Single-Cell Profiling Reveals Transcriptional Signatures and Cell-Cell Crosstalk in Anti-PLA2R Positive Idiopathic Membranous Nephropathy Patients

Idiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease of the kidney glomerulus. It may gradually progress to end-stage renal disease (ESRD) characterized by increased proteinuria, which leads to serious consequences. Although substantial advances have been made in the understanding of the molecular bases of IMN in the last 10 years, certain questions remain largely unanswered. To define the transcriptomic landscape at single-cell resolution, we analyzed kidney samples from 6 patients with anti-PLA2R positive IMN and 2 healthy control subjects using single-cell RNA sequencing. We then identified distinct cell clusters through unsupervised clustering analysis of kidney specimens. Identification of the differentially expressed genes (DEGs) and enrichment analysis as well as the interaction between cells were also performed. Based on transcriptional expression patterns, we identified all previously described cell types in the kidney. The DEGs in most kidney parenchymal cells were primarily enriched in genes involved in the regulation of inflammation and immune response including IL-17 signaling, TNF signaling, NOD-like receptor signaling, and MAPK signaling. Moreover, cell-cell crosstalk highlighted the extensive communication of mesangial cells, which infers great importance in IMN. IMN with massive proteinuria displayed elevated expression of genes participating in inflammatory signaling pathways that may be involved in the pathogenesis of the progression of IMN. Overall, we applied single-cell RNA sequencing to IMN to uncover intercellular interactions, elucidate key pathways underlying the pathogenesis, and identify novel therapeutic targets of anti-PLA2R positive IMN.

Complement C1s and C4d as Prognostic Biomarkers in Renal Cancer: Emergence of Noncanonical Functions of C1s

The complement system plays a complex role in cancer. In clear cell renal cell carcinoma (ccRCC), local production of complement proteins drives tumor progression, but the mechanisms by which they do this are poorly understood. We found that complement activation, as reflected by high plasma C4d or as C4d deposits at the tumor site, was associated with poor prognosis in two cohorts of patients with ccRCC. High expression of the C4-activating enzyme C1s by tumor cells was associated with poor prognosis in three cohorts. Multivariate Cox analysis revealed that the prognostic value of C1s was independent from complement deposits, suggesting the possibility of complement cascade-unrelated, protumoral functions for C1s. Silencing of C1s in cancer cell lines resulted in decreased proliferation and viability of the cells and in increased activation of T cells in in vitro cocultures. Tumors expressing high levels of C1s showed high infiltration of macrophages and T cells. Modification of the tumor cell phenotype and T-cell activation were independent of extracellular C1s levels, suggesting that C1s was acting in an intracellular, noncanonical manner. In conclusion, our data point to C1s playing a dual role in promoting ccRCC progression by triggering complement activation and by modulating the tumor cell phenotype and tumor microenvironment in a complement cascade-independent, noncanonical manner. Overexpression of C1s by tumor cells could be a new escape mechanism to promote tumor progression.See related Spotlight by Magrini and Garlanda, p. 855. See article by Daugan et al., p. 909 (40).

Construction of a Myc-associated ceRNA network reveals a prognostic signature in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains an extremely lethal disease worldwide. High-throughput methods have revealed global transcriptome dysregulation; however, a comprehensive investigation of the complexity and behavioral characteristics of the competing endogenous RNA (ceRNA) network in HCC is lacking. In this study, we extracted the transcriptome (RNA) sequencing data of 371 HCC patients from The Cancer Genome Atlas platform. With the comparison of the high Myc expression (Myc^high) tumor and low Myc expression (Myc^low) tumor groups in HCC, we identified 1,125 differentially expressed (DE) mRNAs, 589 long non-coding RNAs (lncRNAs), and 93 microRNAs (miRNAs). DE RNAs predicted the interactions necessary to construct an associated Myc ceRNA network, including 19 DE lncRNAs, 5 miRNAs, and 72 mRNAs. We identified a significant signature (long intergenic non-protein-coding [LINC] RNA 2691 [LINC02691] and LINC02499) that effectively predicted overall survival and had protective effects. The target genes of microRNA (miR)-212-3p predicted to intersect with DE mRNAs included SEC14-like protein 2 (SEC14L2) and solute carrier family 6 member 1 (SLC6A1), which were strongly correlated with survival and prognosis. With the use of the lncRNA-miRNA-mRNA axis, we constructed a ceRNA network containing four lncRNAs (LINC02691, LINC02499, LINC01354, and NAV2 antisense RNA 4), one miRNA (miR-212-3p), and two mRNAs (SEC14L2 and SLC6A1). Overall, we successfully constructed a mutually regulated ceRNA network and identified potential precision-targeted therapies and prognostic biomarkers.

Targeting Long Non-coding RNA to Therapeutically Regulate Gene Expression in Cancer

Long-chain non-coding RNAs (lncRNAs) are RNA molecules with a length greater than 200 nt and no function of encoding proteins. lncRNAs play a precise regulatory function at different levels of transcription and post-transcription, and they interact with various regulatory factors to regulate gene expression, and then participate in cell growth, differentiation, apoptosis, and other life processes. In recent years, studies have shown that the abnormal expression of lncRNAs is closely related to the occurrence and development of tumors, which is expected to become an effective biomarker in tumor diagnosis. The sequencing analysis of mutations in the whole tumor genome suggests that mutations in non-coding regions may play an important role in the occurrence and development of tumors. Therefore, in-depth study of lncRNAs is helpful to clarify the molecular mechanism of tumor occurrence and development and to provide new targets for tumor diagnosis and treatment. This review introduces the molecular mechanism and clinical application prospect of lncRNAs affecting tumor development from the perspective of gene expression and regulation.

Identification and Validation of the Immune Subtypes of Lung Adenocarcinoma: Implications for Immunotherapy

Lung adenocarcinoma (LUAD) is a devastating disease with poor patient survival. Cancer immunotherapy has revolutionized the treatment of LUAD, but only a limited number of patients effectively respond to this treatment. Thus, the work to elucidate the LUAD immune heterogeneity could be crucial in developing new immunotherapeutic strategies with better efficacy. Non-negative matrix factorization-based deconvolution was performed to identify robust clusters of 489 LUAD patients in The Cancer Genome Atlas (TCGA) and verify their reproducibility and stability in an independent LUAD cohort of 439 patients from the Gene Expression Omnibus (GEO). We used the graph learning-based dimensionality reduction to visualize the distribution of individual patients. In this study, four reproducible immune subtypes, Clusters 1-4 (C1-C4) associated with distinct gene module signatures, clinicopathological features, molecular and cellular characteristics were identified and validated. The immune-cold subtype, C3, was associated with the Dead event, the most advanced T stage, N stage, TNM stage and the worst prognosis for LUAD patients. Moreover, C3 exhibited the lowest infiltrating levels of B cells, T cell receptor (TCR) repertoire diversity and the highest level of neoantigen and mutation rate among C1-C4. On the other hand, the immune-hot subtype (C4) exhibited the highest infiltration of six types of infiltrating immune cells as well as the greatest leukocyte fraction, TCR and B cell receptor (BCR) repertoire diversity. C1 and C2 subtypes showed diverse clinicopathological and immunological features. Finally, our investigations discovered a complex immune landscape with a scattered immune subtype profile. This work may help inform immunotherapeutic decision-making and design advanced immunotherapy strategies for the treatment of lung cancer.