Renal cell carcinoma (RCC) is characterized by its heterogeneity and the complexity of its tumor microenvironment. This study addresses the need to understand RCC at a cellular level, with a focus on its three main subtypes: clear cell (ccRCC), chromophobe (chRCC), and papillary renal cell carcinoma (pRCC). This study aims to comprehensively characterize the cellular diversity and intercellular communication networks of RCC subtypes using scRNA-seq technology. By focusing on macrophages and cancer-associated fibroblasts (CAFs), we seek to reveal their functional states, developmental trajectories, and signaling pathways. We utilized single-cell RNA sequencing (scRNA-seq) data from various kidney cancer subtypes. Advanced analytical techniques, including Uniform Manifold Approximation and Projection (UMAP) and Reactome Gene Set Variation Analysis (ReactomeGSA), were employed to assess cellular heterogeneity and pathway activities. The developmental dynamics of macrophages were studied using CytoTRACE, and cell-to-cell communication was analyzed to identify subtype-specific interaction networks. Our comprehensive analysis revealed significant cellular diversity within RCC. Distinct macrophage and CAF subpopulations were identified, each exhibiting unique gene expression profiles and pathway activities. Notably, ccRCC showed prominent bidirectional communication between macrophages and CAFs, while chRCC and pRCC displayed disrupted signaling pathways. Metabolic pathway analysis reflected the adaptability of macrophages and CAFs to the tumor microenvironment, and the MIF signaling pathway was identified as a key mediator of cellular interactions. The study highlights the cellular heterogeneity and the intricate communication networks within RCC subtypes, underscoring the complexity of the tumor microenvironment. Our findings suggest that targeting specific cellular interactions and pathways may offer new avenues for therapeutic intervention in RCC. The unique macrophage and CAF profiles across RCC subtypes provide valuable insights for the development of personalized and targeted treatment strategies.

The heterogeneity of nasopharyngeal carcinoma (NPC) leads to mixed clinical outcomes. We collected 92 regions of interest from 41 biopsies of patients with untreated NPC and obtained their transcripts using GeoMx Digital Spatial Profiling (DSP) technology. Spatial heterogeneity was determined by measuring the expression of marker genes in tumor cell-enriched (PanCK-expressing), immune cell-enriched (CD45-expressing), and normal epithelial (Endo) regions. We screened 16 prognostic markers in tumor cell-enriched regions and 4 prognostic markers in immune cell-enriched regions. The levels of CD8⁺ T follicular helper T cells, activated NK cells, and M0 macrophage contents were higher in tumor cell-enriched regions than in immune cell-enriched regions. Conversely, plasma cell and M2 macrophage levels were lower. The follicular helper T cells in tumor cell-enriched regions were negatively correlated with resting NK cells and positively correlated with activated NK cells. In immune cell-enriched regions, this relationship was reversed. We also explored the heterogeneity of HLA gene families, immune checkpoints, and metabolism-related genes in the three regions. In tumor cell-enriched regions, we obtained 19 prognosis-related metabolism genes via univariate cox analysis. We used multiplex immunofluorescence to verify the elevated expression of SLC8A1 and MDH1 in immune cell-enriched regions and tumor cell-enriched regions, respectively, both of which were associated with prognosis of NPC. In conclusion, we explored the spatial heterogeneity of the NPC tumor environment and found specific diagnostic and prognostic markers that can be used to differentiate tumor cell-enriched regions from immune cell-enriched regions in NPC.

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.

The kidney renal papillary cell carcinoma (KIRP) is a relatively rare type of renal cell carcinoma (RCC). Currently, most kidney cancer studies primarily focus on RCC, and there has been no investigation to find a robust signature to predict the survival outcome of KIRP patients. In this study, we constructed a competing endogenous RNA (ceRNA) network, including 1,251 lncRNA–miRNA–mRNA interactions. Eight differentially expressed genes (IGF2BP3, PLK1, LINC00200, NCAPG, CENPF, miR-217, GAS6-As1, and LRRC4) based on the TCGA database were selected. The prognostic signature was established by combining the univariate Cox regression method and a stepwise regression method, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. In conclusion, we identified eight prognostic signatures with using ceRNA networks. Our study provided a global view and a systematic dissection on KIRP prognosis biomarkers, and the eight identified genes might be used as new and important prognostic factors involved in KIRP pathogenesis.

Background. Head and neck squamous cell carcinoma (HNSCC) is a significant health problem and related to poor long-term outcomes, indicating more research to be done to deeply understand the underlying pathways. Objective. This current study aimed in the assessment of the viral- (especially human papilloma virus [HPV]) and carcinogen-driven head and neck squamous cell carcinoma (HNSCC) microenvironment based on single-cell sequencing analysis. Methods. Data were downloaded from GEO database (GSE139324), including 131224 cells from 18 HP- HNSCC patients and 8 HPV+ HNSCC patients. Following data normalization, all highly variable genes in single cells were identified, and batch correction was applied. Differentially expressed genes were identified using Wilcoxon rank sum test. A gene enrichment analysis was performed in each cell cluster using KEGG analysis. Single-cell pseudotime trajectories were constructed with MONOCLE (version 2.6.4). Cell-cell interactions were analyzed with CellChat R package. Additionally, cell-cell communication patterns in key signal pathways were compared in different tissue groups. A hidden Markov model (HMM) was used to predict gene expression states (on or off) throughout pseudotime. Five-year overall survival outcomes were compared in both HPV+ and HPV- subsets. Results. 20,978 high-quality individual cells passed quality control. RNA-seq data were used from 522 HNSCC primary tumor samples. 1,137 differentially expressed genes between HPV+ and HPV- HNSCC patients were investigated. 96 differentially expressed genes were associated with overall survival and highly enriched in B cell associated biological process. Cell composition differed between types of samples. MHC-I, MHC-II, and MIF signaling pathways were found to be most relevant. Within these pathways, some cells were either signal receiver or signal sender, depending on sample type, respectively. Six genes were obtained, AREG and TGFBI (upregulation), CD27, CXCR3, MS4A1, and CD19 (downregulation), whose expression and HPV types were highly associated with worse overall survival. AREG and TGFBI were pDC marker genes, CXCR3 and CD27 were significantly expressed in T cell-related cells, while MS4A1 and CD19 were mainly expressed in B nave cells. Conclusions. This study revealed dynamic changes in cell percentage and heterogeneity of cell subtypes of HNSCC. AREG, TGFBI, CD27, CXCR3, MS4A1, and CD19 were associated with worse overall survival in HPV-related HNSCC. Especially B-cell related pathways were revealed as particularly relevant in HPV-related HNSCC. These findings are a basis for the development of biomarkers and therapeutic targets in respective patients.

The CRISPR/Cas9 system-based gene therapy can fundamentally address the issues of cancer occurrence, development, progression, and metastasis. However, the lack of targeting and effectiveness hinders gene therapy from entering clinical application. Herein, a somatostatin receptor-targeted polymeric nanoplatform is developed for the delivery of a PD-L1-targeted CRISPR/Cas9 system and synergistic treatment of hepatocellular carcinoma. This nanoplatform can effectively incorporate the CRISPR/Cas9 system and the chemotherapeutic drug paclitaxel to simultaneously address the biological safety and packaging capacity issues of viral vectors. After the octreotide-modified polymer (LNA-PEG-OCT) guided the nanoparticle into hepatoma carcinoma cells, the nanoparticle protected the CRISPR/Cas9 ribonucleoprotein complex (RNP) and achieved lysosomal escape. Then, the RNP reached the target gene (PD-L1) under the guidance of the single guide RNA (sgRNA) in the RNP. The PD-L1 gene editing efficiency reached up to 55.8% for HepG2 cells in vitro and 46.0% for tumor tissues in vivo, leading to effective suppression of PD-L1 protein expression. Substantial inhibition of hepatocellular carcinoma cell proliferation and further 79.45% growth repression against subcutaneous xenograft tumors were achieved. Overall, this somatostatin receptor-targeted polymeric nanoplatform system not only provides a promising nanocarrier for CRISPR/Cas9 system delivery, but also expands the potential of combining gene editing and chemotherapy.