GSEA-InContext: identifying novel and common patterns in expression experiments

Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis

BACKGROUND

Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.

METHODS

To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls.

RESULTS

A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls.

CONCLUSIONS

To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy

BACKGROUND

Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored.

METHOD

Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models.

RESULTS

We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1.

CONCLUSIONS

We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

Neuropilin-1high monocytes protect against neonatal inflammation

Neonates are susceptible to inflammatory disorders such as necrotizing enterocolitis (NEC) due to their immature immune system. The timely appearance of regulatory immune cells in early life contributes to the control of inflammation in neonates, yet the underlying mechanisms of which remain poorly understood. In this study, we identified a subset of neonatal monocytes characterized by high levels of neuropilin-1 (Nrp1), termed Nrp1high monocytes. Compared with their Nrp1low counterparts, Nrp1high monocytes displayed potent immunosuppressive activity. Nrp1 deficiency in myeloid cells aggravated the severity of NEC, whereas adoptive transfer of Nrp1high monocytes led to remission of NEC. Mechanistic studies showed that Nrp1, by binding to its ligand Sema4a, induced intracellular p38-MAPK/mTOR signaling and activated the transcription factor KLF4. KLF4 transactivated Nos2 and enhanced the production of nitric oxide (NO), a key mediator of immunosuppression in monocytes. These findings reveal an important immunosuppressive axis in neonatal monocytes and provide a potential therapeutic strategy for treating inflammatory disorders in neonates.

YTHDC1-dependent m6A modification modulated FOXM1 promotes glycolysis and tumor progression through CENPA in triple-negative breast cancer

Triple-negative breast cancer (TNBC) exhibits heightened aggressiveness compared with other breast cancer (BC) subtypes, with earlier relapse, a higher risk of distant metastasis, and a worse prognosis. Transcription factors play a pivotal role in various cancers. Here, we found that factor forkhead box M1 (FOXM1) expression was significantly higher in TNBC than in other BC subtypes and normal tissues. Combining the findings of Gene Ontology (GO) enrichment analysis and a series of experiments, we found that knockdown of the FOXM1 gene attenuated the ability of TNBC cells to proliferate and metastasize both in vivo and in vitro. In addition, Spearman's test showed that FOXM1 significantly correlated with glycolysis-related genes, especially centromere protein A (CENPA) in datasets (GSE76250, GSE76124, GSE206912, and GSE103091). The effect of silencing FOXM1 on the inhibition of CENPA expression, TNBC proliferation, migration, and glycolysis could be recovered by overexpression of CENPA. According to MeRIP, the level of m6A modification on FOMX1 decreased in cells treated with cycloleucine (a m6A inhibitor) compared with that in the control group. The increase in FOXM1 expression caused by YTHDC1 overexpression could be reversed by the m6A inhibitor, which indicated that YTHDC1 enhanced FOXM1 expression depending on m6A modification. Therefore, we concluded that the YTHDC1-m6A modification/FOXM1/CENPA axis plays an important role in TNBC progression and glycolysis.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Interferon-induced transmembrane protein 2 is a prognostic marker in colorectal cancer and promotes its progression by activating the PI3K/AKT pathway

BACKGROUND

Interferon-induced transmembrane protein 2 (IFITM2) is involved in repressing viral infection. This study aim to investigate the expression of IFITM2 in colorectal cancer (CRC) and explore its effect on cell proliferation, migration, and invasion.

METHODS

We analyzed The Cancer Genome Atlas (TCGA) database for IFITM2 expression in colorectal cancer and used western blots to detect IFITM2 protein in specimens and cell lines of colorectal cancers. To assess the association between IFITM2 and clinical features, both univariate and multivariate cox regression analysis were conducted. Kaplan-Meier plots were used in the TCGA database to assess IFITM2 gene expression's prognostic significance. Silencing IFITM2 in SW480 and HCT116 cells was achieved by transient transfection with siRNA. Proliferation of CRCs was examined using Cell Counting Kit-8. The effect of IFITM2 on the migration and invasion of CRC cells was studied using wound healing and transwell assays. Gene set enrichment analysis (GSEA) was used to examine IFITM2-associated pathways and Western blotting was used to confirm it.

RESULTS

IFITM2 was over-expressed in the CRC tissues and cells, with high IFITM2 expression related to the tumor N, M, and pathologic stages. The presence of IFITM2 significantly impacted patient survival in CRC. The proliferation of SW480 and HCT116 cells was suppressed when IFITM2 was silenced, resulting in weakened migration and invasion of CRC cells. GSEA analysis showed that IFITM2 was positively related to the phosphoinositide 3-kinase (PI3K)/AKT pathway, and western blot results confirmed that IFITM2 activated it.

CONCLUSIONS

IFITM2 was over-expressed in CRC and modulated the PI3K/AKT pathway to promote CRC cells proliferation and metastasis.

Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer

BACKGROUND

Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms.

METHODS

Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database.

RESULTS

We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs.

CONCLUSIONS

The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Identification of ferroptosis related genes and pathways in prostate cancer cells under erastin exposure

BACKGROUND

Few studies are focusing on the mechanism of erastin acts on prostate cancer (PCa) cells, and essential ferroptosis-related genes (FRGs) that can be PCa therapeutic targets are rarely known.

METHODS

In this study, in vitro assays were performed and RNA-sequencing was used to measure the expression of differentially expressed genes (DEGs) in erastin-induced PCa cells. A series of bioinformatic analyses were applied to analyze the pathways and DEGs.

RESULTS

Erastin inhibited the expression of SLC7A11 and cell survivability in LNCaP and PC3 cells. After treatment with erastin, the concentrations of malondialdehyde (MDA) and Fe2+ significantly increased, whereas the glutathione (GSH) and the oxidized glutathione (GSSG) significantly decreased in both cells. A total of 295 overlapping DEGs were identified under erastin exposure and significantly enriched in several pathways, including DNA replication and cell cycle. The percentage of LNCaP and PC3 cells in G1 phase was markedly increased in response to erastin treatment. For four hub FRGs, TMEFF2 was higher in PCa tissue and the expression levels of NRXN3, CLU, and UNC5B were lower in PCa tissue. The expression levels of SLC7A11 and cell survivability were inhibited after the knockdown of TMEFF2 in androgen-dependent cell lines (LNCaP and VCaP) but not in androgen-independent cell lines (PC3 and C4-2). The concentration of Fe2+ only significantly increased in TMEFF2 downregulated LNCaP and VCaP cells.

CONCLUSION

TMEFF2 might be likely to develop into a potential ferroptosis target in PCa and this study extends our understanding of the molecular mechanism involved in erastin-affected PCa cells.

Mitochondrial aldehyde dehydrogenase rescues against diabetic cardiomyopathy through GSK3β-mediated preservation of mitochondrial integrity and Parkin-mediated mitophagy

Mitochondrial aldehyde dehydrogenase (ALDH2) offers proven cardiovascular benefit, although its impact on diabetes remains elusive. This study examined the effects of ALDH2 overexpression and knockout on diabetic cardiomyopathy and the mechanism involved with a focus on mitochondrial integrity. Mice challenged with streptozotocin (STZ, 200 mg/kg, via intraperitoneal injection) exhibited pathological alterations, including reduced respiratory exchange ratio, dampened fractional shortening and ejection fraction, increased left ventricular end-systolic and diastolic diameters, cardiac remodeling, cardiomyocyte contractile anomalies, intracellular Ca2+ defects, myocardial ultrastructural injury, oxidative stress, apoptosis, and mitochondrial damage, which were overtly attenuated or accentuated by ALDH2 overexpression or knockout, respectively. Diabetic patients also exhibited reduced plasma ALDH2 activity, cardiac remodeling, and diastolic dysfunction. In addition, STZ challenge altered expression levels of mitochondrial proteins (PGC-1α and UCP2) and Ca2+ regulatory proteins (SERCA, Na+-Ca2+ exchanger, and phospholamban), dampened autophagy and mitophagy (LC3B ratio, TOM20, Parkin, FUNDC1, and BNIP3), disrupted phosphorylation of Akt, GSK3β, and Foxo3a, and elevated PTEN phosphorylation, most of which were reversed or worsened by ALDH2 overexpression or knockout, respectively. Furthermore, the novel ALDH2 activator torezolid, as well as the classical ALDH2 activator Alda-1, protected against STZ- or high glucose-induced in vivo or in vitro cardiac anomalies, which was nullified by inhibition of Akt, GSK3β, Parkin, or mitochondrial coupling. Our data discerned a vital role for ALDH2 in diabetic cardiomyopathy possibly through regulation of Akt and GSK3β activation, Parkin mitophagy, and mitochondrial function.

Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer

BACKGROUND

NSCLC is a lethal cancer that is highly prevalent and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as chemotherapy and radiation, frequently exhibit limited efficacy and notable adverse reactions. Therefore, a drug repurposing method is proposed for effective NSCLC treatment.

AIMS

This study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach.

METHODS

Differentially expressed genes in transcriptomics data were identified using the systems biology and network analysis approaches. A network of gene co-expression was developed with the aim of detecting two modules of gene co-expression. Following that, the Drug-Gene Interaction Database was used to find possible drugs that target important genes within two gene co-expression modules linked to non-small cell lung cancer (NSCLC). The use of Cytoscape facilitated the creation of a drug-gene interaction network. Finally, gene set enrichment analysis was done to validate candidate drugs.

RESULTS

Unlike previous research on repositioning drugs for NSCLC, which uses a gene co-expression network, this project is the first to research both gene co-expression and co-occurrence networks. And the co-occurrence network also accounts for differentially expressed genes in cancer cells and their adjacent normal cells. For effective management of non-small cell lung cancer (NSCLC), drugs that show higher gene regulation and gene affinity within the drug-gene interaction network are thought to be important. According to the discourse, NSCLC genes have a lot of control over medicines like vincristine, fluorouracil, methotrexate, clotrimazole, etoposide, tamoxifen, sorafenib, doxorubicin, and pazopanib.

CONCLUSION

Hence, there is a possibility of repurposing these drugs for the treatment of non-small-cell lung cancer.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Effect of radiotherapy on head and neck cancer tissues in patients receiving radiotherapy: a bioinformatics analysis-based study

Radiotherapy is pivotal in treating head and neck cancers including nasopharyngeal, tongue, hypopharyngeal, larynx, maxillary sinus, parotid gland, and oral cancers. It holds the potential for curative effects and finds application in conjunction with chemotherapy, either as a radical method to preserve organ function or as an adjuvant postoperative treatment. We used bioinformatics analysis to investigate the effects of radiotherapy on head and neck cancer tissues in patients who had received radiotherapy. In this study, the expression and mutation profiles of The Cancer Genome Atlas-Head-Neck Squamous Cell Carcinoma were downloaded from the UCSC-Xena database, categorizing patients into two groups-those receiving radiotherapy and those not receiving radiotherapy. Subsequently, differential expression analysis and gene set enrichment analysis (GSEA) were performed. Following this, single-sample GSEA (ssGSEA) scores related to glucose and lipid metabolism were compared between the two groups. Additionally, immune cell infiltration analysis and single-cell verification were performed. Finally, the mutation profiles of the two groups were compared. The analyses revealed that patients receiving radiotherapy exhibited prolonged survival, enhanced apoptosis in head and neck cancer tissue, and diminished keratinocyte proliferation and migration. A comparison of ssGSEA scores related to glucose and lipid metabolism between the two groups indicated a reduction in glycolysis, tricarboxylic acid cycle activity, and fat synthesis in tissues treated with radiotherapy, suggesting that radiotherapy can effectively inhibit tumour cell energy metabolism. Analyses of immune cell infiltration and single-cell verification suggested decreased infiltration of immune cells post-radiotherapy in head and neck cancer tissues. A comparison of mutation profiles revealed a higher frequency of TP53, TTN, and CDKN2A mutations in patients receiving radiotherapy for head and neck cancer. In conclusion, the bioinformatics analyses delved into the effect of radiotherapy on patients with head and neck carcinoma. This study provides a theoretical framework elucidating the molecular mechanisms underlying radiotherapy's efficacy in treating head and neck cancer and presents scientific recommendations for drug therapy following radiotherapy.

Identification of the novel biomarkers involved in the mitochondrial metabolism-related reactive oxygen species and their role in lung cancer T-cell exhaustion and immunotherapy

Purpose

To study the role of mitochondrial metabolism and obtain novel biomarkers in immunotherapy for non-small cell lung cancer (NSCLC).

Methods

We collected the 188 genes involved in mitochondrial metabolism(MMGs) from the MSIGDB project and then quantified the activity of mitochondrial metabolism. All the NSCLC patients were divided into C1 and C2 clusters based on the 26 prognosis-related MMGs. The differences in biology, differential immune microenvironment, chronic hypoxia and prognosis between C1 and C2 patients were also analyzed. In addition, we validated the results of bioinformatics analysis in lung cancer tissues and cell lines.

Results

Patients in the C2 cluster had a higher level of mitochondrial metabolism. Patients in the C2 cluster responded better to immunotherapy and had a lower level of T-cell exclusion. The markers of T-cell failure were upregulated in the C1 patients. Hypoxia can lead to a high percentage of C1 patients. ADH1C might be involved in mitochondrial metabolism and immunotherapy response, which can be affected by hypoxia, making it an underlying biomarker. The expression levels of ADH1C in BEAS-2B, H1299, A549 and H460 cells were detected, revealing that ADH1C is upregulated in lung cancer cells. We observed that patients with low ADH1C expression had a longer survival time. The enzyme activities of HK, PK, LDH and SDH were significantly reduced in H1299 and H460 cells with ADH1C knockdown, along with more ROS. Furthermore, the expression levels of PD-L1 and HHLA2 in tumor tissues were analyzed, which found that ADH1C was significantly positively correlated with the expression of PD-L1 and HHLA2.

Conclusions

In summary, our study comprehensively explored the molecules involved in mitochondrial metabolism and their role in immunotherapy and T lymphocyte failure.

Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot

The role of transforming growth factor β (TGF-β) in inflammation and immune response is established, but the mechanism of TGF-β signaling pathway-related genes (TRGs) in diabetic foot ulcer (DFU) is not fully understood. We aimed to investigate the contribution of TRGs in the identification, molecular categorization, and immune infiltration of DFU through bioinformatics analysis. TGF-β signaling pathway is activated in DFU. 33 TRGs were upregulated. Regression analysis revealed TGFBR1 and TGFB1 as significant differential expression core genes, validated by quantitative real-time PCR. The diagnostic model with core genes had high clinical validity (AUC = 0.909). Core gene expression was associated with immune cell infiltration. A total of 5672 genes showed differential expression in TGF-related patterns, with differences in biological functions and immune infiltration. TGF-β signaling pathway may be critical in DFU development.

CD48 suppresses proliferation and migration as an immune-related prognostic signature in the cervical cancer immune microenvironment

Cervical cancer (CC) is one of the most common malignant tumors in gynecology. Immunotherapy and targeted therapy are two particularly effective treatments. In this study, weighted gene co-expression network analysis and CIBERSORT algorithm that quantifies the composition of immune cells were used to analyze CC expression data based on the GEO database and identify modules related to T cells. Five candidate hub genes were identified by tumor-infiltrating immune cells estimation and Kaplan-Meier survival analysis according to CC data from The Cancer Genome Atlas (TCGA). Chemotherapeutic response, methylation, and gene mutation analyses were implemented so that the five candidate hub genes identified may be the potential biomarkers and therapeutic targets which were related to T cell infiltration. Moreover, the results of RT-qPCR revealed that CD48 was a tumor suppressor gene, which was negatively correlated with CC stages, lymph node metastasis, and differentiation. Furthermore, the functional study verified that the interference of CD48 was able to boost the proliferation and migration ability in vitro and the growth of transplanted tumors in vivo. Overall, we identified molecular targets related to immune infiltration and prognosis, regarded CD48 as a key molecule involved in the progression of CC, thus providing new insights into the development of molecular therapy and immunotherapeutics against CC.

Dysregulated lipid metabolism is associated with kidney allograft fibrosis

BACKGROUND

Interstitial fibrosis and tubular atrophy (IF/TA), a histologic feature of kidney allograft destruction, is linked to decreased allograft survival. The role of lipid metabolism is well-acknowledged in the area of chronic kidney diseases; however, its role in kidney allograft fibrosis is still unclarified. In this study, how lipid metabolism contributes to kidney allografts fibrosis was examined.

METHODS

A comprehensive bioinformatic comparison between IF/TA and normal kidney allograft in the Gene Expression Omnibus (GEO) database was conducted. Further validations through transcriptome profiling or pathological staining of human recipient biopsy samples and in rat models of kidney transplantation were performed. Additionally, the effects of enhanced lipid metabolism on changes in the fibrotic phenotype induced by TGF-β1 were examined in HK-2 cell.

RESULTS

In-depth analysis of the GEO dataset revealed a notable downregulation of lipid metabolism pathways in human kidney allografts with IF/TA. This decrease was associated with increased level of allograft rejection, inflammatory responses, and epithelial mesenchymal transition (EMT). Pathway enrichment analysis showed the downregulation in mitochondrial LC-fatty acid beta-oxidation, fatty acid beta-oxidation (FAO), and fatty acid biosynthesis. Dysregulated fatty acid metabolism was also observed in biopsy samples from human kidney transplants and in fibrotic rat kidney allografts. Notably, the areas affected by IF/TA had increased immune cell infiltration, during which increased EMT biomarkers and reduced CPT1A expression, a key FAO enzyme, were shown by immunohistochemistry. Moreover, under TGF-β1 induction, activating CPT1A with the compound C75 effectively inhibited migration and EMT process in HK-2 cells.

CONCLUSIONS

This study reveal a critical correlation between dysregulated lipid metabolism and kidney allograft fibrosis. Enhancing lipid metabolism with CPT1A agonists could be a therapeutic approach to mitigate kidney allografts fibrosis.

Comprehensive Analysis and Experimental Validation of the Parkinson's Disease Lysosomal Gene ACP2 and Pan-cancer

The pivotal role of lysosomal function in preserving neuronal homeostasis is recognized, with its dysfunction being implicated in neurodegenerative processes, notably in Parkinson's disease (PD). Yet, the molecular underpinnings of lysosome-related genes (LRGs) in the context of PD remain partially elucidated. We collected RNA-seq data from the brain substantia nigra of 30 PD patients and 20 normal subjects from the GEO database. We obtained molecular classification clusters from the screened lysosomal expression patterns. The lysosome-related diagnostic model of Parkinson's disease was constructed by XGBoost and Random Forest. And we validated the expression patterns of signature LRGs in the diagnostic model by constructing a PD rat model. Finally, the linkage between PD and cancer through signature genes was explored. The expression patterns of the 33 LRGs screened can be divided into two groups of PD samples, enabling exploration of the variance in biological processes and immune elements. Cluster A had a higher disease severity. Subsequently, critical genes were sieved through the application of machine learning methodologies culminating in the identification of two intersecting feature genes (ACP2 and LRP2). A PD risk prediction model was constructed grounded on these signature genes. The model's validity was assessed through nomogram evaluation, which demonstrated robust confidence validity. Then we analyzed the correlation analysis, immune in-filtration, biological function, and rat expression validation of the two genes with common pathogenic genes in Parkinson's disease, indicating that these two genes play an important role in the pathogenesis of PD. We then selected ACP2, which had a significant immune infiltration correlation, as the entry gene for the pan-cancer analysis. The pan-cancer analysis revealed that ACP2 has profound associations with prognostic indicators, immune infiltration, and tumor-related regulatory processes across various neoplasms, suggesting its potential as a therapeutic target in a range of human diseases, including PD and cancers. Our study comprehensively analyzed the molecular grouping of LRGs expression patterns in Parkinson's disease, and the disease progression was more severe in cluster A. And the PD diagnosis model related to LRGs is constructed. Finally, ACP2 is a potential target for the relationship between Parkinson's disease and tumor.

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review

OBJECTIVES

To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies.

METHODS

This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies.

RESULTS

Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall.

CONCLUSION

The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.

Ribosomal protein S6 kinase 2 (RPS6KB2) is a potential immunotherapeutic target for cancer that upregulates proinflammatory cytokines

BACKGROUND

Cancer is still a leading cause of mortality. Over the years, cancer therapy has undergone significant advances driven by advancements in science and technology. A promising area of drug discovery in this field involves the development of therapeutic targets for cancer treatment. The urgent need to identify new pharmacological targets arises from the impact of tumor resistance on the effectiveness of current medications. Specifically, the RPS6KB2 gene on chromosome 11 has been implicated in cell cycle regulation and exhibits higher expression levels in tumor tissue. Given this association, there is a potential for this gene to serve as a target for cancer treatment.

METHODS

We conducted an analysis using the GTEx, TCGA, and CCLE databases to explore the relationship between RPS6KB2 and immune infiltration, the tumor microenvironment (TME), microsatellite instability (MSI), and more. Cell proliferation was assessed using EDU detection, while cell invasion and migration were evaluated via wound healing and Transwell assays. Additionally, western blot analysis was employed to measure expression of Bax, Bcl-2, MMP2, MMP9, PCNA, and proinflammatory factors.

RESULTS

Through data analysis and molecular biology methods, our study carefully examined the potential role of RPS6KB2 in cancer therapy. The data revealed that RPS6KB2 is aberrantly expressed in most cancers and is associated with poor prognosis. Further analysis indicated its involvement in cancer cell apoptosis and migration, as well as its role in cancer immune processes. We validated the significance of RPS6KB2 in hepatocellular carcinoma (HCC), highlighting its capacity to upregulate proinflammatory cytokines.

CONCLUSION

Our research indicates that RPS6KB2 is a prognostic biomarker associated with immune infiltration in cancer that can affect antitumor immunity by increasing secretion of proinflammatory factors, providing a potential drug target for cancer treatment.

Anoikis related genes may be novel markers associated with prognosis for ovarian cancer

The aim of this study was to determine the prognostic significance of anoikis related genes (ARGs) in ovarian cancer (OC) and to develop a prognostic signature based on ARG expression. We analyzed cohorts of OC patients and used nonnegative matrix factorization (NMF) for clustering. Single-sample gene-set enrichment analysis (ssGSEA) was employed to quantify immune infiltration. Survival analyses were performed using the Kaplan-Meier method, and differences in survival were determined using the log-rank test. The extent of anoikis modification was quantified using a risk score generated from ARG expression. The analysis of single-cell sequencing data was performed by the Tumor Immune Single Cell Hub (TISCH). Our analyses revealed two distinct patterns of anoikis modification. The risk score was used to evaluate the anoikis modification patterns in individual tumors. Three hub-genes were screened using the LASSO (Least Absolute Shrinkage and Selection Operator) method and patients were classified into different risk groups based on their individual score and the median score. The low-risk subtype was characterized by decreased expression of hub-genes and better overall survival. The risk score, along with patient age and gender, were considered to identify the prognostic signature, which was visualized using a nomogram. Our findings suggest that ARGs may play a novel role in the prognosis of OC. Based on ARG expression, we have developed a prognostic signature for OC that can aid in patient stratification and treatment decision-making. Further studies are needed to validate these results and to explore the underlying mechanisms.

Peroxisome proliferator-activated receptors signature reveal the head and neck squamous cell carcinoma energy metabolism phenotype and clinical outcome

BACKGROUND

Peroxisome proliferator activating receptors (PPARs) are important regulators of nuclear hormone receptor function, and they play a key role in biological processes such as lipid metabolism, inflammation and cell proliferation. However, their role in head and neck squamous cell carcinoma (HNSC) is unclear.

METHODS

We used multiple datasets, including TCGA-HNSC, GSE41613, GSE139324, PRJEB23709 and IMVigor, to perform a comprehensive analysis of PPAR-related genes in HNSC. Single-cell sequencing data were preprocessed using Seurat packets, and intercellular communication was analyzed using CellChat packets. Functional enrichment analysis of PPAR-related genes was performed using ClusterProfile and GSEA. Prognostic models were constructed using LASSO and Cox regression models, and immunohistochemical analyses were performed using human protein mapping (The Human Protein Atlas).

RESULTS

Our single-cell RNA sequencing analysis revealed distinct cell populations in HNSC, with T cells having the most significant transcriptome differences between tumors and normal tissues. The PPAR features were higher in most cell types in tumor tissues compared with normal tissues. We identified 17 PPAR-associated differentially expressed genes between tumors and normal tissues. A prognostic model based on seven PPAR-associated genes was constructed with high accuracy in predicting 1, 2 and 3 year survival in patients with HNSC. In addition, patients with a low risk score had a higher immune score and a higher proportion of T cells, CD8+ T cells and cytotoxic lymphocytes. They also showed higher immune checkpoint gene expression, suggesting that they might benefit from immunotherapy. PPAR-related genes were found to be closely related to energy metabolism.

CONCLUSIONS

Our study provides a comprehensive understanding of the role of PPAR related genes in HNSC. The identified PPAR features and constructed prognostic models may serve as potential biomarkers for HNSC prognosis and treatment response. In addition, our study found that PPAR-related genes can differentiate energy metabolism and distinguish energy metabolic heterogeneity in HNSC, providing new insights into the molecular mechanisms of HNSC progression and therapeutic response.

C-C Motif Chemokine Ligand 5 (CCL5): A Potential Biomarker and Immunotherapy Target for Osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignant tumor of bone tissue, which has an insidious onset and is difficult to detect early, and few early diagnostic markers with high specificity and sensitivity. Therefore, this study aims to identify potential biomarkers that can help diagnose OS in its early stages and improve the prognosis of patients.

METHODS

The data sets of GSE12789, GSE28424, GSE33382 and GSE36001 were combined and normalized to identify Differentially Expressed Genes (DEGs). The data were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG) and Disease Ontology (DO). The hub gene was selected based on the common DEG that was obtained by applying two regression methods: the Least Absolute Shrinkage and Selection Operator (LASSO) and Support vVector Machine (SVM). Then the diagnostic value of the hub gene was evaluated in the GSE42572 data set. Finally, the correlation between immunocyte infiltration and key genes was analyzed by CIBERSORT.

RESULTS

The regression analysis results of LASSO and SVM are the following three DEGs: FK501 binding protein 51 (FKBP5), C-C motif chemokine ligand 5 (CCL5), complement component 1 Q subcomponent B chain (C1QB). We evaluated the diagnostic performance of three biomarkers (FKBP5, CCL5 and C1QB) for osteosarcoma using receiver operating characteristic (ROC) analysis. In the training group, the area under the curve (AUC) of FKBP5, CCL5 and C1QB was 0.907, 0.874 and 0.676, respectively. In the validation group, the AUC of FKBP5, CCL5 and C1QB was 0.618, 0.932 and 0.895, respectively. It is noteworthy that these genes were more expressed in tumor tissues than in normal tissues by various immune cell types, such as plasma cells, CD8+ T cells, T regulatory cells (Tregs), activated NK cells, activated dendritic cells and activated mast cells. These immune cell types are also associated with the expression levels of the three diagnostic genes that we identified.

CONCLUSION

We found that CCL5 can be considered an early diagnostic gene of osteosarcoma, and CCL5 interacts with immune cells to influence tumor occurrence and development. These findings have important implications for the early detection of osteosarcoma and the identification of novel therapeutic targets.

Exploring the mechanism underlying the therapeutic effects of butein in colorectal cancer using network pharmacology and single-cell RNA sequencing data

BACKGROUND

Butein has shown substantial potential as a cancer treatment, but its precise mechanism of action in colorectal cancer (CRC) remains unclear. This study aimed to uncover the underlying mechanisms through which butein operates in CRC and to identify potential biomarkers through a comprehensive investigation.

METHODS

Target genes associated with butein were sourced from SwissTargetPrediction, CTD, BindingDB and TargetNet. Gene expression data from the GSE38026 dataset and the single-cell dataset (GSE222300) were retrieved from the Gene Expression Omnibus database. The activation of disease-related pathways was assessed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and differential gene analysis. Disease-associated genes were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). The protein-protein interaction network was utilized to pinpoint potential drug targets. Molecular complex detection (MCODE) analysis was employed to uncover relevant genes influenced by butein within key subgroup networks. Machine learning techniques were applied for the screening of potential biomarkers, with receiver operating characteristic curves used to evaluate their clinical significance. Single-cell analysis was conducted to assess the pharmacological targets of butein in CRC, with validation performed using the external dataset GSE40967.

RESULTS

A total of 232 target genes for butein were identified. Functional enrichment analysis revealed significant enrichment of signaling pathways, including mitogen-activated protein kinase, JAK-STAT and NF-κB, among these genes. Differential analysis, in conjunction with WGCNA, yielded 520 disease-related genes. Subsequently, a disease-drug-gene network consisting of 727 targets was established, and a subnetwork containing 56 crucial genes was extracted. Important pathways such as the FoxO signaling pathway exhibited significant enrichment within these key genes. Machine learning applied to the 56 important genes led to the identification of a potential biomarker, UBE2C. Receiver operating characteristic analysis demonstrated the excellent clinical predictive utility of UBE2C. Single-cell analysis suggested that butein's therapeutic effects might be linked to its influence on epithelial and T cells, with UBE2C expression associated with these cell types. Validation using the external dataset GSE40967 further confirmed the exceptional clinical predictive capability of UBE2C.

CONCLUSION

This study combines network pharmacology with single-cell analysis to unravel the mechanisms underlying butein's effects in CRC. Notably, UBE2C emerged as a promising biomarker with superior clinical efficacy. These research findings contribute significantly to our understanding of specific molecular mechanisms, potentially shaping future clinical practices.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Background

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide. Therapeutic failure in lung cancer (LUAD) is heavily influenced by drug resistance. This challenge stems from the diverse cell populations within the tumor, each having unique genetic, epigenetic, and phenotypic profiles. Such variations lead to varied therapeutic responses, thereby contributing to tumor relapse and disease progression.

Methods

The Genomics of Drug Sensitivity in Cancer (GDSC) database was used in this investigation to obtain the mRNA expression dataset, genomic mutation profile, and drug sensitivity information of NSCLS. Machine Learning (ML) methods, including Random Forest (RF), Artificial Neurol Network (ANN), and Support Vector Machine (SVM), were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods. The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods, and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype. Finally, the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.

Results

Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs. Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response (area under the curve [AUC] 0.875) using CIT, GAS2L3, STAG3L3, ATP2B4-mut, and IL15RA-mut as molecular features. Furthermore, the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance (AUC 0.780) in Gefitinib with CCL23-mut.

Conclusion

This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Integrated Bioinformatics and Validation Reveal IFI27 and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis

Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapies is imperative. Mitochondrial dysfunction stands out as a pivotal factor in its development. This study aimed to elucidate the relationship between mitochondria dysfunction and liver cirrhosis using bioinformatic methods to unveil its pathogenesis. Initially, we identified 460 co-expressed differential genes (co-DEGs) from the GSE14323 and GSE25097 datasets, alongside their combined datasets. Functional analysis revealed that these co-DEGs were associated with inflammatory cytokines and cirrhosis-related signaling pathways. Utilizing weighted gene co-expression network analysis (WCGNA), we screened module genes, intersecting them with co-DEGs and oxidative stress-related mitochondrial genes. Two algorithms (least absolute shrinkage and selection operator (LASSO) regression and SVE-RFE) were then employed to further analyze the intersecting genes. Finally, COX7A1 and IFI27 emerged as identifying genes for liver cirrhosis, validated through a receiver operating characteristic (ROC) curve analysis and related experiments. Additionally, immune infiltration highlighted a strong correlation between macrophages and cirrhosis, with the identifying genes (COX7A1 and IFI27) being significantly associated with macrophages. In conclusion, our findings underscore the critical role of oxidative stress-related mitochondrial genes (COX7A1 and IFI27) in liver cirrhosis development, highlighting their association with macrophage infiltration. This study provides novel insights into understanding the pathogenesis of liver cirrhosis.

A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

This study aimed to elucidate the role of anoikis in the progression of prostate cancer (PCa) and to develop a prognostic signature based on anoikis-related genes (ARGs). To achieve this, PCa cases were subjected to nonnegative matrix factorization (NMF) analysis, which allowed for the identification of distinct patterns of anoikis modification. Additionally, immune infiltration was evaluated using single-sample gene-set enrichment analysis (ssGSEA). Survival analysis was performed using the Kaplan-Meier method, and a risk score was generated based on the expression levels of ARGs to quantitatively assess the modification of anoikis in PCa. Using the Least Absolute Shrinkage and Selection Operator (LASSO) method, four hub-genes were identified, and patients were classified into different risk groups based on their individual scores. Importantly, the low-risk subtype was characterized by a significantly improved biochemical recurrence-free survival, underscoring the clinical relevance of the ARG-based prognostic signature. To further improve the prognostic accuracy of the signature, patient age, pathological T stage, Gleason score, and prostate-specific antigen level were incorporated into the analysis, yielding a comprehensive prognostic signature. The clinical relevance of this signature was illustrated through a nomogram, providing a visual representation of the prognostic implications of the ARG-based signature. Taken together, these findings highlight the potential of ARGs in predicting the clinical outcomes of PCa patients and provide a novel and clinically relevant prognostic signature based on the modification of anoikis in PCa.

RNA-seq analysis identifies transcriptomic profiles associated with anal cancer recurrence among people living with HIV

BACKGROUND

Chemoradiation therapy (CRT) is the standard of care for squamous cell carcinoma of the anus (SCCA), the most common type of anal cancer. However, approximately one fourth of patients still relapse after CRT.

METHODS

We used RNA-sequencing technology to characterize coding and non-coding transcripts in tumor tissues from CRT-treated SCCA patients and compare them between 9 non-recurrent and 3 recurrent cases. RNA was extracted from FFPE tissues. Library preparations for RNA-sequencing were created using SMARTer Stranded Total RNA-Seq Kit. All libraries were pooled and sequenced on a NovaSeq 6000. Function and pathway enrichment analysis was performed with Metascape and enrichment of gene ontology (GO) was performed with Gene Set Enrichment Analysis (GSEA).

RESULTS

There were 449 differentially expressed genes (DEGs) observed (390 mRNA, 12 miRNA, 17 lincRNA and 18 snRNA) between the two groups. We identified a core of upregulated genes (IL4, CD40LG, ICAM2, HLA-I (HLA-A, HLA-C) and HLA-II (HLA-DQA1, HLA-DRB5) in the non-recurrent SCCA tissue enriching to the gene ontology term 'allograft rejection', which suggests a CD4+ T cell driven immune response. Conversely, in the recurrent tissues, keratin (KRT1, 10, 12, 20) and hedgehog signaling pathway (PTCH2) genes involved in 'Epidermis Development,', were significantly upregulated. We identified miR-4316, that inhibit tumor proliferation and migration by repressing vascular endothelial growth factors, as being upregulated in non-recurrent SCCA. On the contrary, lncRNA-SOX21-AS1, implicated in the progression of many other cancers, was also found to be more common in our recurrent compared to non-recurrent SCCA.

Our study identified key host factors which may drive the recurrence of SCCA and warrants further studies to understand the mechanism and evaluate their potential use in personalized treatment.Key MessageOur study used RNA sequencing (RNA-seq) to identify pivotal factors in coding and non-coding transcripts which differentiate between patients at risk for recurrent anal cancer after treatment. There were 449 differentially expressed genes (390 mRNA, 12 miRNA, 17 lincRNA and 18 snRNA) between 9 non-recurrent and 3 recurrent squamous cell carcinoma of anus (SCCA) tissues. The enrichment of genes related to allograft rejection was observed in the non-recurrent SCCA tissues, while the enrichment of genes related to epidermis development was positively linked with recurrent SCCA tissues.

Identification of Novel Diagnostic Biomarkers and Classification Patterns for Osteoarthritis by Analyzing a Specific Set of Genes Related to Inflammation

Osteoarthritis (OA) is a prevalent joint disease globally. TNFA is recognized as a crucial inflammatory cytokine that plays a significant role in the pathophysiological mechanisms that occur during the progression of OA. However, the TNFA_SIGNALING_VIA_NFKB (TSVN)-related genes (TRGs) during the progression of OA remain unclear. By conducting a combinatory analysis of OA transcriptome data from three datasets, various differentially expressed TRGs were identified. The logistic regression model was used to mine hub TRGs for OA, and a nomogram prediction model was subsequently constructed using these TRGs. To identify new molecular subgroups, we performed consensus clustering. We then conducted functional analyses, including GO, KEGG, GSVA, and GSEA, to elucidate the underlying mechanisms. To determine the immune microenvironment, we applied xCell. The logistic regression analysis identified three hub TRGs (BHLHE40, BTG2, and CCNL1) as potential biomarkers for OA. Based on these TRGs, we constructed an OA predictive model. This model has demonstrated promising results in enhancing the accuracy of OA diagnosis, as evident from the ROC analysis (AUC merged dataset = 0.937, AUC validating dataset = 0.924). We identified two molecular subtypes, C1 and C2, and found that the C1 subtype showed activation of immune- and inflammation-related pathways. The involvement of TSVN in the development and progression of OA has been established. We identified several hub genes, such as BHLHE40, BTG2, and CCNL1, that may have a significant association with the progression of OA. Furthermore, our logistic regression model based on these genes has shown promising results in accurately diagnosing OA patients.

A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis

Background

Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings.

Methods

A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score.

Results

A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542.

Conclusion

Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BACKGROUND

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

METHODS

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

Five key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.

CONCLUSION

In summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

Introduction

Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.

Methodology

Utilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.

Results

In executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.

Conclusion

The present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis.

RAI2 acts as a tumor suppressor with functional significance in gastric cancer

Metastasis of gastric cancer (GC) is one of the major causes of death among GC patients. GC metastasis involves numerous biological processes, yet the specific molecular biological mechanisms have not been elucidated. Here, we report a novel tumor suppressor, retinoic acid-induced 2 (RAI2), which is located in the Xp22 region of the chromosome and plays a role in inhibiting GC growth and invasion. In this study, integrated analysis of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and immunohistochemistry staining data suggested that RAI2 expression in GC samples was low. Moreover, the immune infiltration analysis indicated that low expression of RAI2 in GC was associated with a higher intensity of tumor-infiltrating lymphocytes (TILs) and an abundance of Programmed death ligand 1 (PD-L1) expression. Gene set enrichment analysis (GSEA) analysis further revealed that RAI2 regulated some pathways including the GAP junction, focal adhesion and ECM receptor interaction pathway, immune regulation, PI3K-Akt signaling, MAPK signaling, cell cycle, and DNA replication. Furthermore, the knockdown of RAI2 promoted GC cell proliferation, migration, and invasion in vitro. Taken together, these results suggest that the tumor suppressor RAI2 could be a potential target for the development of anti-cancer strategies in GC.

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer

Introduction

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

Methods

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

Results

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably CTLA4, LAG3, PDCD1, and CD274, according to an analysis of immune processes. The core gene C5aR1 in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of C5aR1 had lower 10-year overall survival (OS) rates.

Conclusion

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

Liquid-liquid phase separation-related gene in gliomas: FABP5 is a potential prognostic marker

BACKGROUND

The glioma is the most malignant human brain tumor. Early glioma detection and treatment are still difficult. New biomarkers are desperately required to aid in the evaluation of diagnosis and prognosis.

METHODS

The single cell sequencing dataset scRNA-6148 for glioblastoma was obtained from the Chinese Glioma Genome Atlas database. Data were gathered for the transcriptome sequencing project. Genes involved in liquid-liquid phase separation (LLPS) were taken out of the DrLLPS database. To find the modules connected to LLPS, the weighted co-expression network was analyzed. Differential expression analysis was used to identify the differentially expressed genes (DEGs) in gliomas. Pseudo-time series analysis, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were used to investigate the role of important genes in the immunological microenvironment. We examined the function of key glioma genes using polymerase chain reaction (PCR) testing, CCK-8 assays, clone generation assays, transwell assays and wound healing assays.

RESULTS

FABP5 was identified as a key gene in glioblastoma by multiomics research. Pseudo-time series analysis showed that FABP5 was highly linked with the differentiation of many different types of cells. GSEA revealed that FABP5 was strongly linked to several hallmark pathways in glioblastoma. We looked at immune cell infiltration and discovered a significant link between FABP5, macrophages and T cell follicular helpers. The PCR experiment results demonstrated that FABP5 expression was elevated in glioma samples. Cell experiments showed that FABP5 knockdown dramatically decreased the viability, proliferation, invasion and migration of the LN229 and U87 glioma cell lines.

CONCLUSIONS

Our study provides a new biomarker, FABP5, for glioma diagnosis and treatment.

OCLN as a novel biomarker for prognosis and immune infiltrates in kidney renal clear cell carcinoma: an integrative computational and experimental characterization

Background

Occludin (OCLN) is an important tight junction protein and has been reported to be abnormally expressed in the development of malignant tumors. However, its biomarker and carcinogenic roles in kidney renal clear cell carcinoma (KIRC) are less investigated.

Methods

The Cancer Genome Atlas database and Human Protein Atlas database were used to analyze the expression of OCLN in KIRC. UALCAN database and methylation-specific PCR assay were used to evaluate the methylation level of OCLN in KIRC. Univariate and multivariate Cox regression analyses were performed to model the prognostic significance of OCLN in KIRC patient cohorts. The correlation between OCLN expression and the immune cell infiltration, immune-related function and immune checkpoints were explored. Finally, EdU, scratch assay and transwell experiments were conducted to validate the role of OCLN in KIRC development.

Results

The expression of OCLN was significantly downregulated in KIRC, compared with normal renal tissues (p<0.001). Patients with low OCLN expression showed a worse prognosis and poorer clinicopathological characteristics. Functional enrichment analysis revealed that OCLN was mainly involved in biological processes such as immune response, immunoglobulin complex circulating and cytokine and chemokine receptor to mediate KIRC development. Immune-related analysis indicated that OCLN could potentially serve as a candidate target for KIRC immunotherapy. OCLN overexpression inhibited proliferation, migration and invasion of KIRC cells in vitro.

Conclusion

OCLN was validated as a candidate prognostic biomarker and therapeutic target of KIRC based both on computational and experimental approaches. More in vivo experiments will be conducted to decode its molecular mechanism in KIRC carcinogenesis in the future work.

Elucidating Cuproptosis-Associated Genes in the Progression from Nash to HCC Using Bulk and Single-Cell RNA Sequencing Analyses and Experimental Validation

Background and Objectives: Non-alcoholic steatohepatitis (NASH) is a significant risk factor for hepatocellular carcinoma (HCC) development. Timely treatment during the NASH stage is essential to minimize the possibility of disease progression to HCC. Cuproptosis is a newly identified form of cellular death that could impact the progression of various diseases and cancers. Materials and Methods: Transcriptome and single-cell sequencing datasets were utilized to investigate the role of cuproptosis-related genes (CRGs) in NASH progression to HCC. FDX1, LIPT1, and PDHP were identified as CRGs in NASH patients, and FDX1, DBT, GCSH, SLC31A1, and DLAT were identified as CRGs in patients with NASH progressing to HCC. FDX1 was found to play a significant role in both NASH patients and patients with NASH progressing to HCC. This study constructed cuproptosis-related clusters (CRCs) using the Nonnegative Matrix Factorization algorithm, and they were linked to fatty acid metabolism and the PPAR signaling pathway in both NASH CRCs and HCC CRCs. The Weighted Correlation Network Analysis algorithm identified CRP, CRC, TAT, CXCL10, and ACTA1 as highly relevant genes in NASH CRCs and HCC CRCs. The expression of FDX1 was validated in both mouse models and human NASH samples. Results: The investigation highlights FDX1 as a pivotal CRG in both NASH and NASH progression to HCC. The comprehensive characterization of CRGs sheds light on their potential biofunctional importance in the context of NASH and HCC. Our experimental results show that FDX1 expression was significantly increased in NASH patients. Conclusions: The present study identified key CRGs, revealing their potential impact on NASH and HCC. Meanwhile, targeting FDX1 may prevent the progression of NASH to HCC.

Phase 2 Trial of Stereotactic Ablative Radiotherapy for Patients with Primary Renal Cancer

BACKGROUND

Most renal cell carcinomas (RCCs) are localized and managed by active surveillance, surgery, or minimally invasive techniques. Stereotactic ablative radiation (SAbR) may provide an innovative non-invasive alternative although prospective data are limited.

OBJECTIVE

To investigate whether SAbR is effective in the management of primary RCCs.

DESIGN, SETTING, AND PARTICIPANTS

Patients with biopsy-confirmed radiographically enlarging primary RCC (≤5 cm) were enrolled. SAbR was delivered in either three (12 Gy) or five (8 Gy) fractions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was local control (LC) defined as a reduction in tumor growth rate (compared with a benchmark of 4 mm/yr on active surveillance) and pathologic evidence of tumor response at 1 yr. Secondary endpoints included LC by the Response Evaluation Criteria in Solid Tumors (RECIST 1.1), safety, and preservation of kidney function. Exploratory tumor cell-enriched spatial protein and gene expression analysis were conducted on pre- and post-treatment biopsy samples.

RESULTS AND LIMITATIONS

Target accrual was reached with the enrollment of 16 ethnically diverse patients. Radiographic LC at 1 yr was observed in 94% of patients (15/16; 95% confidence interval: 70, 100), and this was accompanied by pathologic evidence of tumor response (hyalinization, necrosis, and reduced tumor cellularity) in all patients. By RECIST, 100% of the sites remained without progression at 1 yr. The median pretreatment growth rate was 0.8 cm/yr (interquartile range [IQR]: 0.3, 1.4), and the median post-treatment growth rate was 0.0 cm/yr (IQR: -0.4, 0.1, p < 0.002). Tumor cell viability decreased from 4.6% to 0.7% at 1 yr (p = 0.004). With a median follow-up of 36 mo for censored patients, the disease control rate was 94%. SAbR was well tolerated with no grade ≥2 (acute or late) toxicities. The average glomerular filtration rate declined from a baseline of 65.6 to 55.4 ml/min at 1 yr (p = 0.003). Spatial protein and gene expression analyses were consistent with the induction of cellular senescence by radiation.

CONCLUSIONS

This clinical trial adds to the growing body of evidence suggesting that SAbR is effective for primary RCC supporting its evaluation in comparative phase 3 clinical trials.

PATIENT SUMMARY

In this clinical trial, we investigated a noninvasive treatment option of stereotactic radiation therapy for the treatment of primary kidney cancer and found that it was safe and effective.

Tregs depletion aggravates activation of astrocytes by modulating IL-10/GXP4 following cerebral infarction

Background

Tregs plays a critical role in the development of secondary injuries in diseases. Accumulating evidence suggests an association between ischemic stroke and renal dysfunction; however, the underlying mechanisms remain unclear. This study aimed to investigate the potential of Tregs in inhibiting the activation of astrocytes after focal cerebral infarction.

Methods

This study aimed to investigate the renal consequences of focal cerebral ischemia by subjecting a mouse model to transient middle cerebral artery occlusion (tMCAO). Subsequently, we assessed renal fibrosis, renal ferroptosis, Treg infiltration, astrocyte activation, as well as the expression levels of active GPX4, FSP1, IL-10, IL-6, and IL-2 after a 2-week period.

Results

In the tMCAO mouse model, depletion of tregs protected against activation of astrocyte and significantly decreased FSP1, IL-6, IL-2, and NLRP3 expression levels, while partially reversing the changes in Tregs. Mechanistically, tregs depletion attenuates renal fibrosis by modulating IL-10/GPX4 following cerebral infarction.

Conclusion

Tregs depletion attenuates renal fibrosis by modulating IL-10/GPX4 following cerebral infarction.

Predicting key gene related to immune infiltration and myofibroblast-like valve interstitial cells in patients with calcified aortic valve disease based on bioinformatics analysis

BACKGROUND

Calcified aortic valve disease (CAVD) is the most prevalent valvular disease that can be treated only through valve replacement. We aimed to explore potential biomarkers and the role of immune cell infiltration in CAVD progression through bioinformatics analysis.

METHODS

Differentially ex-pressed genes (DEGs) were screened out based on three microarray datasets: GSE12644, GSE51472 and GSE83453. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to evaluate gene expression differences. Machine learning algorithms and DEGs were used to screen key gene. We used CIBERSORT to evaluate the immune cell infiltration of CAVD and evaluated the correlation between the biomarkers and infiltrating immune cells. We also compared bioinformatics analysis results with the valve interstitial cells (VICs) gene expression in single-cell RNA sequencing.

RESULTS

Collagen triple helix repeat containing 1 (CTHRC1) was identified as the key gene of CAVD. We identified a cell subtype valve interstitial cells-fibroblast, which was closely associated with fibro-calcific progress of aortic valve. CTHRC1 highly expressed in the VIC subpopulation. Immune infiltration analysis demonstrated that mast cells, B cells, dendritic cells and eosinophils were involved in pathogenesis of CAVD. Correlation analysis demonstrated that CTHRC1 was correlated with mast cells mostly.

CONCLUSIONS

In summary, the study suggested that CTHRC1 was a key gene of CAVD and CTHRC1 might participate in the potential molecular pathways involved in the connection between infiltrating immune cells and myofibroblast phenotype VICs.

Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder

Introduction

Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however.

Methods

In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified.

Results

We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88.

Discussion

Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning

Background and aim

Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers.

Methods

We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC).

Results

The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.

Conclusion

We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

Heterogeneity of tumor immune microenvironment in malignant and metastatic change in LUAD is revealed by single-cell RNA sequencing

Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer and accounts for approximately 40% of all lung cancer cases. Multiple distant metastases are the major cause of mortality in lung cancer. In this study, single-cell sequencing datasets of LUAD were utilized to depict the transcriptome characteristic of LUAD based on the bioinformatic method. Firstly, the transcriptome landscape of heterogeneous cell types in LUAD was analyzed and memory T cells, NK cells, and helper T cells were revealed to be the common immune cells in tumor, normal, and metastasis tissue, respectively. Then, marker genes were calculated and 709 genes were identified to play a vital role in the microenvironment of LUAD. While macrophages were reported to act as one of the cells in LUAD, enrichment analysis of macrophage marker genes revealed the important role of macrophages in the activation of neutrophils. Next, the results of cell-cell communication analysis suggested that pericytes interact with broad immune cells via MDK-NCL pathways in metastasis samples, MIF-(CD74+CXCR4) and MIF-(CD74+CC44) interaction especially occurred between different cell types in tumor and normal samples. Finally, bulk RNA-seq was integrated to validate the prognosis effect of the marker gene and the maker gene of M2 macrophage, CCL20, showed the most related to LUAD prognosis. Besides, ZNF90 (Helper T cells), FKBP4 (memory T, helper T, Cytotoxic T, and B cells), CD79A (B cells), TPI1 (pericyte), and HOPX (epithelial cells, pericytes) were also pivotal in the pathology of LUAD, helping researchers understand the molecular insight of microenvironment in LUAD.

Machine learning immune-related gene based on KLRB1 model for predicting the prognosis and immune cell infiltration of breast cancer

Objective

Breast cancer is a prevalent malignancy that predominantly affects women. The development and progression of this disease are strongly influenced by the tumor microenvironment and immune infiltration. Therefore, investigating immune-related genes associated with breast cancer prognosis is a crucial approach to enhance the diagnosis and treatment of breast cancer.

Methods

We analyzed data from the TCGA database to determine the proportion of invasive immune cells, immune components, and matrix components in breast cancer patients. Using this data, we constructed a risk prediction model to predict breast cancer prognosis and evaluated the correlation between KLRB1 expression and clinicopathological features and immune invasion. Additionally, we investigated the role of KLRB1 in breast cancer using various experimental techniques including real-time quantitative PCR, MTT assays, Transwell assays, Wound healing assays, EdU assays, and flow cytometry.

Results

The functional enrichment analysis of immune and stromal components in breast cancer revealed that T cell activation, differentiation, and regulation, as well as lymphocyte differentiation and regulation, play critical roles in determining the status of the tumor microenvironment. These DEGs are therefore considered key factors affecting TME status. Additionally, immune-related gene risk models were constructed and found to be effective predictors of breast cancer prognosis. Further analysis through KM survival analysis and univariate and multivariate Cox regression analysis demonstrated that KLRB1 is an independent prognostic factor for breast cancer. KLRB1 is closely associated with immunoinfiltrating cells. Finally, in vitro experiments confirmed that overexpression of KLRB1 inhibits breast cancer cell proliferation, migration, invasion, and DNA replication ability. KLRB1 was also found to inhibit the proliferation of breast cancer cells by blocking cell division in the G1/M phase.

Conclusion

KLRB1 may be a potential prognostic marker and therapeutic target associated with the microenzymic environment of breast cancer tumors, providing a new direction for breast cancer treatment.

Identification of immune-related gene signature for predicting prognosis in uterine corpus endometrial carcinoma

The objective of this study is to develop a gene signature related to the immune system that can be used to create personalized immunotherapy for Uterine Corpus Endometrial Carcinoma (UCEC). To classify the UCEC samples into different immune clusters, we utilized consensus clustering analysis. Additionally, immune correlation algorithms were employed to investigate the tumor immune microenvironment (TIME) in diverse clusters. To explore the biological function, we conducted GSEA analysis. Next, we developed a Nomogram by integrating a prognostic model with clinical features. Finally, we performed experimental validation in vitro to verify our prognostic risk model. In our study, we classified UCEC patients into three clusters using consensus clustering. We hypothesized that cluster C1 represents the immune inflammation type, cluster C2 represents the immune rejection type, and cluster C3 represents the immune desert type. The hub genes identified in the training cohort were primarily enriched in the MAPK signaling pathway, as well as the PD-L1 expression and PD-1 checkpoint pathway in cancer, all of which are immune-related pathways. Cluster C1 may be a more suitable for immunotherapy. The prognostic risk model showed a strong predictive ability. Our constructed risk model demonstrated a high level of accuracy in predicting the prognosis of UCEC, while also effectively reflecting the state of TIME.

Ferroptosis-associated gene CISD2 suppresses colon cancer development by regulating tumor immune microenvironment

Background

Despite the association of ferroptosis with various tumors, the specific mechanism by which it influences colon adenocarcinoma (COAD) microenvironmental equilibrium remains elusive. This study aims to elucidate how ferroptosis affects COAD microenvironmental homeostasis and its potential impact on COAD research.

Objective

By employing genetic screening and single-cell analysis of tumor data, we investigated the role of ferroptosis genes in COAD microenvironmental homeostasis. The genes were correlated with immune cell infiltration in tissue samples and patient outcomes.

Methods

Ferroptosis-associated genes were initially identified through the FerrDb database. Utilizing the tidyverse and Seurat packages, genes with substantial expression differences were extracted, and clustering analysis was performed on the single-cell data. A Venn diagram depicted shared differential genes for ferroptosis and tumors. To screen key ferroptosis genes, further enrichment analysis and immune cell infiltration analysis were conducted. Lastly, human COAD cell lines were employed to overexpress CDGSH iron sulfur domain 2 (CISD2) through cellular assays to validate its function in COAD.

Results

Following screening of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, 414 COAD patient samples and 341 normal samples were included. Through the FerrDb database, 259 ferroptosis genes were identified. Clustering the single-cell data revealed 911 tumor marker genes, of which 18 were ferroptosis genes. Analysis of variance (ANOVA) and univariate regression analysis determined that only CISD2 was statistically significantly associated with clinical outcomes. Additionally, CISD2 was found to positively correlate with activated memory T cells and negatively correlate with regulatory T cells (Tregs) and plasma cells in COAD, as well as being significantly associated with several immune-related and cancer-related pathways. CISD2 expression was elevated in most tumors, likely due to cell cycle regulation and immune system activation. Moreover, CISD2 upregulation inhibited COAD cell proliferation and enhanced 5-fluorouracil (5-FU) sensitivity. Our findings indicate, for the first time, that CISD2 governs the cell cycle and stimulates the immune system to impede COAD progression.

Conclusion

By modulating the cell cycle and mediating immune infiltration, CISD2 may inhibit COAD development by influencing tumor immune microenvironment equilibrium, providing valuable insights into the relevance and potential impact of the research results on the COAD research field.

Exploring the Correlation Between GPR176, a Potential Target Gene of Gastric Cancer, and Immune Cell Infiltration

Introduction

GPR176, an orphan G protein-coupled receptor (GPCR), is essential for the progression of gastrointestinal cancers. However, it is still unclear how GPR176 affects tumor immunity and patient prognosis in gastric cancer (GC).

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were searched in this investigation to assess the expression patterns of GPR176 in GC tissues and normal gastric mucosa. The findings were further verified using immunohistochemical tests and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). The Kaplan-Meier method, univariate logistic regression, and Cox regression were then used to investigate the relationship between GPR176 and clinical traits. Additionally, the potential correlation between GPR176, immune checkpoint genes, and immune cell infiltration levels was investigated.

Results

As per the research findings, GC tissues had higher levels of GPR176 than normal tissues. Additionally, individuals with high expression of GPR176 had a worse 10-year overall survival (OS), in contrast with those having a low expression of GPR176 (p < 0.001). The OS of GC can be predicted using a validated nomogram model. The expression of GPR176 demonstrated a negative correlation with CD8+ T cells. When compared to the low-expression group of GPR176, Tumor Immune Dysfunction and Exclusion (TIDE) analysis demonstrated that the high-expression group had a considerably higher risk of immune evasion. A remarkable difference (variation) was observed in the levels of GPR176 expression across both groups, ie, low and high-risk groups, as determined by the immune phenomenon scores (IPS) immunotherapy assessment.

Conclusion

By examining GPR176 from various biological perspectives, it was determined that GPR176 can act as a predictive biomarker for poor patient prognosis in GC. Additionally, it was observed that GPR176 is capable of suppressing the proliferation of CD8+ T cells and facilitating immune evasion.

Erk5 functions in modulation of zebrafish intestinal permeability

The intestine of zebrafish consists of mucosa, muscularis and serosa. Intestinal epithelial cells (IECs) act as a physical and biochemical barrier to protect against invasion by external commensal bacteria. Cell junction is one of the crucial basis of the barrier function. When cell junctions were disrupted, intestinal permeability would be naturally impeded. Extracellular signal-regulated kinase 5 (ERK5), belonging to the Mitogen-activated protein kinase (MAPK) family, is involved in the normal physiological development of the cardiovascular system and nervous system. But the role of erk5 in intestinal morphogenesis and intestinal function is yet to know. Here, we showed that knockout of the erk5 in zebrafish larvae resulted in intestinal wall hypoplasia, including the thinned intestinal wall, reduced intestinal folds, and disrupted cell junctions. In addition, the intestinal permeability assay demonstrated that knockout of erk5 resulted in increased intestinal permeability. All of these showed that erk5 plays an essential role in the maintenance of intestinal barrier function. Thus, our data indicate that erk5 is a critical effector in intestinal morphogenesis and intestinal function, and dysfunction of erk5 would lead to intestinal diseases.