Inhibition of neuroactive ligand-receptor interaction pathway can enhance immunotherapy response in colon cancer: an in silico study

Identification of novel biomarkers associated with immune infiltration in major depression disorder and atopic dermatitis

Major depression disorder (MDD) and atopic dermatitis (AD) are distinct disorders involving immune inflammatory responses. This study aimed to investigate the comorbid relationship between AD and MDD and to identify possible common mechanisms. We obtained AD and MDD data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and the Genecard database were employed to identify shared genes associated with inflammatory diseases. These shared genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub genes were selected based on the protein-protein interactions using CytoHubba, and key regulatory genes were identified through enrichment analysis. Subsequently, we conducted immune infiltration and correlation analyses of the shared genes in AD. Finally, we employed three machine learning models to predict the significance of shared genes. A total of 17 shared genes were identified in the AD_Inflammatory_MDD dataset (S100A9, PTGER2, PI3, SNCA, DAB2, PDGFA, FSTL1, CALD1, XK, UTS2, DHRS9, PARD3, NFIB, TMEM158, LIPH, RAB27B, and SH3BRL2). These genes were associated with biological processes such as the regulation of mesenchymal cell proliferation, cellular ketone metabolic processes, and glial cell differentiation. The neuroactive ligand-receptor interaction, IL-17 signaling, and Rap1 signaling pathways were significantly enriched in KEGG analysis. SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as hub genes contributing to comorbid AD and MDD development. The three machine learning models consistently identified SNCA and PARD3 as important biomarkers.SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as significant genes contributing to the development of AD and MDD comorbidities. Immune infiltration analysis showed a notable increase in the infiltration of various subtypes of CD4 + T cells, suggesting a potential association between the development of skin inflammation and the immune response. Across different machine learning models, SNCA and PARD3 consistently emerged as important biomarkers, providing a new direction for clinical diagnosis and treatment.

Calcium-dependent adhesion protein CDH18, a potential biomarker for prognosis in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is one of the most common cancers in women, yet lacks specific and sensitive tumor markers for diagnosis, as traditional markers like CA125 show limited specificity. This study investigates the clinical significance and prognostic value of CDH18, a calcium-dependent adhesion protein linked to tumor progression, in UCEC.

Methods

Clinical data from UCEC patients were sourced from The Cancer Genome Atlas (TCGA) database. Pan-cancer analysis, differential expression examination, and survival analysis were conducted to investigate the differential expression of the calcium associated protein-CDH18 and its prognostic relevance. CDH18 mutations in UCEC were examined using the cBioPortal database. Additional analyses included functional enrichment, tumor mutational burden, tumor microenvironment (TME) estimates via ESTIMATE, and immune infiltration assessment to clarify CDH18's potential mechanisms in UCEC. Drug sensitivity testing was utilized to identify more suitable therapeutic options for patients. Immunofluorescence staining (IF) and Real-Time Polymerase Chain Reaction techniques (RT-PCR) confirmed CDH18 expression in UCEC tumor.

Results

CDH18 expression was markedly increased in UCEC and showed a significant association with poorer prognosis, which was confirmed by our IF and RT-PCR results. Thirteen mutation sites were identified, and survival analysis showed that patients with higher CDH18 expression had shorter overall survival. The expression of CDH18 was confirmed to be an independent predictor of overall survival by multivariate COX regression analysis. Additionally, a predictive nomogram model was developed to accurately forecast outcomes for individuals with UCEC. Correlation analysis revealed that CDH18 expression exhibited a negative correlation with CD8 T cell levels and a positive correlation with resting NK cell and macrophage M2 levels. In the group with high CDH18 expression, the IC50 values for (5Z)-7-Oxozeaenol, AG-014699, CEP-701, Mitomycin C, PD-0325901, PD-0332991, PHA-665752, SL 0101-1, and SN-38 were notably elevated.

Conclusion

CDH18 is a novel promising biomarker in UCEC, uniquely associating tumor progression, immune modulation, and chemotherapy resistance, offering enhanced prognostic accuracy and guiding individualized therapeutic strategies for improved patient outcomes.

Crotonylation-Related Prognostic Model of Esophageal Squamous Cell Carcinoma Based on Transcriptome Analysis and Single-Cell Sequencing Analysis

Background

Crotonylation is an emerging lysine acylation modification implicated in various diseases, yet its role in esophageal squamous cell cancer (ESCC) is unexplored. This study aimed to investigate the role of crotonylation-related genes (CRGs) in ESCC using bioinformatics approaches.

Methods

We included three ESCC datasets and 24 CRGs. Differentially expressed genes (DEGs) from TCGA-ESCA were intersected with key module genes related to CRGs to identify candidate genes. Univariate and LASSO regression analyses were conducted to select prognostic genes, which were then used to construct risk models. Independent prognostic analysis and nomogram construction followed. Functional enrichment and immune infiltration analyses were performed using the prognostic genes. Single-cell analysis was conducted to assess cell communication and pseudotemporal dynamics in key cells.

Results

Intersection of 1529 DEGs with 1,048 key module genes yielded 55 candidate genes. OSM, FABP3, MICB, and FAM189A2 were identified as prognostic genes. These genes were used to classify ESCA patients into different risk groups and construct a nomogram. FABP3 and FAM189A2 were enriched in neuroactive ligand-receptor interaction and ribosome terms. MICB and FABP3 showed strong positive correlations with natural killer T (NKT) cells, while FAM189A2 negatively correlated with gamma delta T (γδT) cells. Single-cell analysis identified mast cells and neutrophils as key cells, differentiating into seven and three states, respectively.

Conclusion

Four genes (OSM, FABP3, MICB, and FAM189A2) were identified as prognostic crotonylation-related genes in ESCC, potentially involved in its pathogenesis. OSM was negatively correlated with ESCC, while FABP3 and MICB were positively correlated.

Expression and role of methyltransferase 3 in oral malignant transformation

OBJECTIVE

To investigate the expression and role of methyltransferase 3 (METTL3) in oral malignant transformation.

MATERIALS AND METHODS

Immunohistochemical method was used to investigate the expression of METTL3 in the human oral malignant transformation. Bioinformatics analysis was used to explore the role of METTL3 in oral malignant transformation. Oral cancer animal model was used to verify the expression trend of METTL3 in oral malignant transformation. Knockdown of METTL3 expression in human oral mucosal precancerous lesion cells was performed to explore the METTL3 effect on proliferation, migration, apoptosis, and cell cycle.

RESULTS

METTL3 expression was significantly up-regulated in the human oral malignant transformation. Moreover, METTL3 was related to the pathway of "Neuroactive ligand-receptor interaction." In addition, METTL3 expression was also significantly up-regulated in the hamster oral malignant transformation. Finally, the proliferation and migration abilities of human oral mucosal precancerous lesion cells were inhibited after METTL3 knockdown.

CONCLUSIONS

In conclusion, we found that METTL3 was up-regulated in oral malignant transformation, and the role may relate to the pathway of "Neuroactive ligand-receptor interaction."

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4

BACKGROUND

Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 (BEX4) in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, BEX4's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.

METHODS

Patients from The Cancer Genome Atlas (TCGA) were stratified into high-score (training) and low-score (control) groups based on the aneuploidy score. Mfuzz expression pattern clustering and functional enrichment were applied to genes with BEX4 as the core to explore their regulatory mechanisms. Various machine learning techniques were employed to screen aneuploidy-associated genes, after which aneuploidy characteristic subtypes were established in cancers. Moreover, the aneuploidy characteristics across multiple cancer types were investigated by integrating the extent of tumor cell stemness acquisition and a series of immune traits. Immunohistochemistry and proliferation assay mainly verified the anti-tumor effect of different BEX4 level.

RESULTS

Functional clustering results showed that aneuploidy and stemness were significantly associated in kidney chromophobe (KICH) and thyroid carcinoma (THCA). And cell metabolism and cell cycle had key effects. Residual analysis indicates superior screening performance by random forest (RF). An aneuploid feature gene set with BEX4 as the core was screened to construct a Nomogram model. BEX4, calmodulin regulated spectrin associated protein 2 (CAMSAP2), and myristoylated alanine rich protein kinase C substrate (MARCKS) were identified as aneuploidy characteristic hub genes. Molecular subtypes in thymoma (THYM), thyroid carcinoma (THCA), and kidney chromophobe (KICH) showed significant differences in tumor cell stemness among different subtypes. The competitive endogenous RNA (ceRNA)-Genes network revealed that hub genes, co-regulated by hsa-miR-425-5p, hsa-miR-200c-3p, and others, regulate microtubules, centrosomes, and microtubule cytoskeleton. Furthermore, elevated BEX4 emerged as a significant protective factor in Pancreatic adenocarcinoma (PAAD), KICH, kidney renal papillary cell carcinoma (KIRP), and kidney renal clear cell carcinoma (KIRC).

CONCLUSIONS

BEX4, CAMSAP2, and MARCKS specifically express in microtubules, centrioles, and cytoskeletons, influencing tumor chromosome division and inducing aneuploidy. Additionally, the relationship between the acquisition of tumor cell stemness and the severity of aneuploidy varies significantly across tumor types, displaying positive and negative correlations.

Qualitative Transcriptional Signature for Predicting the Pathological Response of Colorectal Cancer to FOLFIRI Therapy

FOLFIRI (5-FU, leucovorin, irinotecan) is the first-line chemotherapy for metastatic colorectal cancer (mCRC), but response rates are under 50%. This study aimed to develop a predictive signature for FOLFIRI response in mCRC patients. Firstly, Spearman's rank correlation and Wilcoxon rank-sum test were used to select chemotherapy response genes and gene pairs, respectively. Then, an optimization procedure was used to determine the final signature. A predictive signature consisting of three gene pairs (3-GPS) was identified. In the training set, 3-GPS achieved an accuracy of 0.94. In a validation set of 60 samples, predicted responders had significantly better progression-free survival than the predicted non-responders (HR = 0.47, p = 0.01). A comparable result was observed in an additional validation set of 27 samples (HR = 0.06, p = 0.02). The co-expressed genes of the signature were enriched in pathways associated with the immunotherapy response, and they interacted extensively with FOLFIRI-related genes. Notably, the expression of signature genes significantly correlated with various immune cell types, including plasma cells and memory-resting CD4+ T cells. In conclusion, the REO-based signature effectively identifies mCRC patients likely to benefit from FOLFIRI. Furthermore, these signature genes may play a crucial role in the chemotherapy.

Comparative transcriptomic study on the ovarian cancer between chicken and human

The laying hen is the spontaneous model of ovarian tumor. A comprehensive comparison based on RNA-seq from hens and women may shed light on the molecular mechanisms of ovarian cancer. We performed next-generation sequencing of microRNA and mRNA expression profiles in 9 chicken ovarian cancers and 4 normal ovaries, which has been deposited in GSE246604. Together with 6 public datasets (GSE21706, GSE40376, GSE18520, GSE27651, GSE66957, TCGA-OV), we conducted a comparative transcriptomics study between chicken and human. In the present study, miR-451, miR-2188-5p, and miR-10b-5p were differentially expressed in normal ovaries, early- and late-stage ovarian cancers. We also disclosed 499 up-regulated genes and 1,061 down-regulated genes in chicken ovarian cancer. The molecular signals from 9 cancer hallmarks, 25 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and 369 Gene Ontology (GO) pathways exhibited abnormalities in ovarian cancer compared to normal ovaries via Gene Set Enrichment Analysis (GSEA). In the comparative analysis across species, we have uncovered the conservation of 5 KEGG and 76 GO pathways between chicken and human including the mismatch repair and ECM receptor interaction pathways. Moreover, a total of 174 genes contributed to the core enrichment for these KEGG and GO pathways were identified. Among these genes, the 22 genes were found to be associated with overall survival in patients with ovarian cancer. In general, we revealed the microRNA profiles of ovarian cancers in hens and updated the mRNA profiles previously derived from microarrays. And we also disclosed the molecular pathways and core genes of ovarian cancer shared between hens and women, which informs model animal studies and gene-targeted drug development.

Unveiling the unique role of TSPAN7 across tumors: a pan-cancer study incorporating retrospective clinical research and bioinformatic analysis

BACKGROUND

TSPAN7 is an important factor in tumor progression. However, the precise function of TSPAN7 and its role in pan-cancer are not clear.

METHODS

Based on Xinhua cohort incorporating 370 patients with kidney neoplasm, we conducted differential expression analysis by immunohistochemistry between tumor and normal tissues, and explored correlations of TSPAN7 with patients' survival. Subsequently, we conducted a pan-cancer study, and successively employed differential expression analysis, competing endogenous RNA (ceRNA) analysis, protein-protein interaction (PPI) analysis, correlation analysis of TSPAN7 with clinical characteristics, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. Last but not least, gene set enrichment analysis was applied to identify enriched pathways of TSPAN7.

RESULTS

In Xinhua cohort, TSPAN7 expression was significantly up-regulated (P-value = 0.0019) in tumor tissues of kidney neoplasm patients. High TSPAN7 expression was associated with decreases in overall survival (OS) (P-value = 0.009) and progression-free survival (P-value = 0.009), and it was further revealed as an independent risk factor for OS (P-value = 0.0326, HR = 5.66, 95%CI = 1.155-27.8). In pan-cancer analysis, TSPAN7 expression was down-regulated in most tumors, and it was associated with patients' survival, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. The ceRNA network and PPI network of TSPAN7 were also constructed. Last but not least, the top five enriched pathways of TSPAN7 in various tumors were identified.

CONCLUSION

TSPAN7 served as a promising biomarker of various tumors, especially kidney neoplasms, and it was closely associated with tumor purity, tumor genomics, tumor immunology, and drug sensitivity in pan-cancer level.

Changes in oral, skin, and gut microbiota in children with atopic dermatitis: a case-control study

Introduction

Atopic dermatitis (AD) is a common clinical recurrent atopic disease in dermatology, most seen in children and adolescents. In recent years, AD has been found to be closely associated with microbial communities.

Methods

To explore the synergistic effects between colonizing bacteria from different sites and AD, we comparatively analyzed the skin, oral, and gut microbiota of children with AD (50 individuals) and healthy children (50 individuals) by 16S rRNA gene sequencing. Twenty samples were also randomly selected from both groups for metabolic and macrogenomic sequencing.

Results

The results of our sequencing study showed reduced microbiota diversity in the oral, skin, and gut of children with AD (P < 0.05). Metabolomics analysis showed that serotonergic synapse, arachidonic acid metabolism, and steroid biosynthesis were downregulated at all three loci in the oral, skin, and gut of children with AD (P < 0.05). Macrogenomic sequencing analysis showed that KEGG functional pathways of the three site flora were involved in oxidative phosphorylation, ubiquitin-mediated proteolysis, mRNA surveillance pathway, ribosome biogenesis in eukaryotes, proteasome, basal transcription factors, peroxisome, MAPK signaling pathway, mitophagy, fatty acid elongation, and so on (P < 0.05).

Discussion

The combined microbial, metabolic, and macrogenetic analyses identified key bacteria, metabolites, and pathogenic pathways that may be associated with AD development. We provides a more comprehensive and in-depth understanding of the role of the microbiota at different sites in AD patients, pointing to new directions for future diagnosis, treatment and prognosis.

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma

Background

Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients.

Methods

The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy.

Results

We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm.

Conclusion

Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions

Computed tomography (CT) imaging has the potential to assist in predicting the prognosis and treatment strategies for pancreatic cancer (PC). This study aimed to develop and validate a radio-clinical model based on preoperative multiphase CT assessments to predict the overall survival (OS) of PC and identify differentially expressed genes associated with OS.

METHODS

Patients with PC who had undergone radical pancreatectomy (R0 resection) were divided into development and external validation sets. Independent predictors of OS were identified using Cox regression analyses and included in the nomogram, which was externally validated. The area under the curve was used to measure the model's accuracy in estimating OS probability. RNA sequencing data from The Cancer Genome Atlas were used for gene expression analysis.

RESULTS

In the development and external validation sets, survival was estimated respectively for 132 and 27 patients. Multivariate Cox regression analysis identified 5 independent OS predictors: age (P = .049), sex (P = .001), bilirubin level (P = .005), tumor size (P = .020), and venous invasion (P = .041). These variables were incorporated into the nomogram. Patients were divided into high- and low-risk groups for OS and survival curves showed that all patients in the low-risk group had better OS than that of those in the high-risk group (P < .001). Differentially expressed genes in patients with a poor prognosis were involved in neuroactive ligand-receptor interaction.

CONCLUSION

The radio-clinical model may be clinically useful for successfully predicting PC prognosis.

Quality markers of Guchang Zhixie pills based on multicomponent qualitative and quantitative analysis combined with network pharmacology and chemometric analysis

Traditional Chinese medicine Guchang Zhixie pills(GCZX) is one of the famous varieties of "Qin medicine" that has been extensively applied to treating irritable bowel syndrome(IBS). However, despite the acknowledged clinical advantages of GCZX there are significant constraints on its quality control and evaluation. The present study utilized UHPLC-Q-Exactive-Orbitrap-MS to analyze the chemical composition of GCZX. Additionally, network pharmacology approaches were utilized to explore the underlying mechanism by which blood components exert therapeutic effects in the treatment of IBS. Furthermore, the GCZX samples were evaluated for their quality on the basis of the qualitative results obtained from 25 batches of GCZX samples using fingerprinting; subsequently, multivariate statistical analysis methods were employed for further analysis. The results indicated the presence of 198 individual components. Among them, 17 prototype compounds were detected in the serum of rats that were administered with GCZX. The potential therapeutic mechanism of GCZX in the treatment of IBS may be associated with the modulation of the neurological system, the immunological system, and the inflammatory response. Moreover, a total of seven prominent peaks were identified after fingerprint analysis. The range of fingerprint similarity among the 25 batches of samples varied from 0.843 to 1.000. The application of chemometrics analysis successfully facilitated the categorical classification of 25 batches of GCZX into three distinct groups. Seven components hold significant importance and should be duly considered during the quality control process of GCZX. The present study can establish the Q-Markers of GCZX for IBS, thereby providing a foundation for investigating the theoretical underpinnings and elucidating the mechanisms underlying the therapeutic effects of GCZX in the treatment of IBS.