RAB11FIP1: An Indicator for Tumor Immune Microenvironment and Prognosis of Lung Adenocarcinoma from a Comprehensive Analysis of Bioinformatics

Single-nuclei transcriptome analysis of IgM+ cells isolated from channel catfish (Ictalurus punctatus) spleen

Catfish production is the primary aquaculture sector in the United States, and the key cultured species is channel catfish (Ictalurus punctatus). The major causes of production losses are pathogenic diseases, and the spleen, an important site of adaptive immunity, is implicated in these diseases. To examine the channel catfish immune system, single-nuclei transcriptomes of sorted and captured IgM+ cells were produced from adult channel catfish. Three channel catfish (~1 kg) were euthanized, the spleen dissected, and the tissue dissociated. The lymphocytes were isolated using a Ficoll gradient and IgM+ cells were then sorted with flow cytometry. The IgM+ cells were lysed and single-nuclei libraries generated using a Chromium Next GEM Single Cell 3' GEM Kit and the Chromium X Instrument (10x Genomics) and sequenced with the Illumina NovaSeq X Plus sequencer. The reads were aligned to the I. punctatus reference assembly (Coco_2.0) using Cell Ranger, and normalization, cluster analysis, and differential gene expression analysis were carried out with Seurat. Across the three samples, approximately 753.5 million reads were generated for 18,686 cells. After filtering, 10,637 cells remained for the cluster analysis. The cluster analysis identified 16 clusters which were classified as B cells (10,276), natural killer-like (NK-like) cells (178), T cells or natural killer cells (45), hematopoietic stem and progenitor cells (HSPC)/megakaryocytes (MK) (66), myeloid/epithelial cells (40), and plasma cells (32). The B cell clusters were further defined as different populations of mature B cells, cycling B cells, and plasma cells. The plasma cells highly expressed ighm and we demonstrated that the secreted form of the transcript was largely being expressed by these cells. This atlas provides insight into the gene expression of IgM+ immune cells in channel catfish. The atlas is publicly available and could be used garner more important information regarding the gene expression of splenic immune cells.

Identification and study of mood-related biomarkers and potential molecular mechanisms in type 2 diabetes mellitus

A significant correlation between type 2 diabetes mellitus (T2DM) and mood has been reported. However, the specific mechanism of mood's role in T2DM is unclear. This study aims to discover mood-related biomarkers in T2DM and further elucidate their underlying molecular mechanisms. The GSE81965 and GSE55650 datasets were sourced from public databases, and mood-related genes (MRGs) were retrieved from previous literature. Initially, differentially expressed MRGs (DE-MRGs) were obtained by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, the DE-MRGs were incorporated into the LASSO and SVM to identify diagnostic biomarkers for T2DM. Four machine learning methods were utilized to construct the diagnostic models in T2DM, and the model with the optimal algorithm was screened. Further, based on biomarkers, functional enrichment, immune infiltration, and regulatory network analyses were conducted to excavate deeper into the pathogenesis of T2DM. In vivo experiments were used to validate the expression of the biomarkers. A total of 23 DE-MRGs were identified by overlapping 723 DEGs and 64 key modules, and there were strong positive correlations between these DE-MRGs. Afterward, KCTD16, SLC8A1, RAB11FIP1, and RASGEF1B were identified as biomarkers associated with mood in T2DM, and they had favorable diagnostic performance. Meanwhile, the RF diagnostic model constructed based on biomarkers was performed optimally and had high diagnostic accuracy for T2DM patients. Animal experiments indicated that expression levels of SLC8A1, RAB11FIP1, and RASGEF1B in T2DM were consistent with the microarray results. In conclusion, KCTD16, SLC8A1, RAB11FIP1, and RASGEF1B were identified as biomarkers related to mood in T2DM.

Dipeptidase‑2 is a prognostic marker in lung adenocarcinoma that is correlated with its sensitivity to cisplatin

Lung cancer accounts for the highest percentage of cancer morbidity and mortality worldwide, and lung adenocarcinoma (LUAD) is the most prevalent subtype. Although numerous therapies have been developed for lung cancer, patient prognosis is limited by tumor metastasis and more effective treatment targets are urgently required. In the present study, gene expression profiles were extracted from the Gene Expression Omnibus database and mRNA expression data were downloaded from The Cancer Genome Atlas database. In addition, TIMER 2.0 database was used to analyze the expression of genes in normal and multiple tumor tissues. Protein expression was confirmed using the Human Protein Atlas database and LUAD cell lines, sphere formation assay, western blotting, and a xenograft mouse model were used to confirm the bioinformatics analysis. Dipeptidase‑2 (DPEP2) expression was significantly decreased in LUAD and was negatively associated with prognosis. DPEP2 overexpression substantially inhibited epithelial‑mesenchymal transition (EMT) as well as LUAD cell metastasis, and limited the expression of the cancer stem cell transformation markers, CD44 and CD133. In addition, DPEP2 improved LUAD sensitivity to cisplatin by inhibiting EMT; this was verified in vitro and in vivo. These data indicated that DPEP2 upregulates E‑cadherin, thereby regulating cell migration, cancer stem cell transformation, and cisplatin resistance, ultimately affecting the survival of patients with LUAD. Overall, the findings of the present suggest that DPEP2 is important in the development of LUAD and can be used both as a prognostic marker and a target for future therapeutic research.

The Prognostic Signature of Head and Neck Squamous Cell Carcinoma Constructed by Immune-Related RNA-Binding Proteins

Purpose

This study aimed to construct a prognostic signature consisting of immune-related RNA-binding proteins (RBPs) to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) effectively.

Methods

The transcriptome and clinical data of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. First, we ascertained the immunological differences in HNSCC, through single-sample gene set enrichment analysis, stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) deconvolution algorithm. Then we used univariate proportional hazards (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to screen immune-related RBPs and acquire the risk score of each sample. Subsequently, we further investigated the difference in prognosis, immune status, and tumor mutation burden in high- and low-risk groups. Finally, the efficacy of immunotherapy was measured by the tumor immune dysfunction and exclusion (TIDE) score.

Results

We derived 15 immune-related RBPs, including FRMD4A, ASNS, RAB11FIP1, FAM120C, CFLAR, CTTN, PLEKHO1, SELENBP1, CHCHD2, NPM3, ATP2A3, CFDP1, IGF2BP2, NQO1, and DENND2D. There were significant differences in the prognoses of patients in the high- and low-risk groups in the training set (p < 0.001) and the validation set (p < 0.01). Furthermore, there were statistical differences between the high-risk group and low-risk group in immune cell infiltration and pathway and tumor mutation load (p < 0.001). In the end, we found that patients in the low-risk group were more sensitive to immunotherapy (p < 0.001), and then we screened 14 small-molecule chemotherapeutics with higher sensitivity to the high-risk group (p < 0.001).

Conclusion

The study constructed a prognostic signature of HNSCC, which might guide clinical immunotherapy in the future.