The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer

Transcriptional switches in melanoma T Cells: Facilitating polarizing into regulatory T cells

Melanoma is a malignant skin tumor with a high mortality rate. Regulatory T cells (Tregs) are immune cells with immunosuppressive roles, however, the precise mechanisms governing Treg involvement in melanoma remain enigmatic. Experimental findings unveiled different transcription factor switches between normal and tumor T cell, with heightened FOXP3 and BATF in the latter. These factors induced immunosuppressive molecules and Treg maintenance genes, polarizing tumor T cells into Tregs. Spatial transcriptomics illuminated the preferential settlement of Tregs at the melanoma periphery. Within this context, FOXP3 in Tregs facilitated direct enhancement of specific ligand gene expression, fostering communication with neighboring cells. Novel functional molecules bound to FOXP3 or BATF in Tregs, such as SPOCK2, SH2D2A, and ligand molecules ITGB2, LTA, CLEC2C, CLEC2D, were discovered, which had not been previously reported in melanoma Treg studies. Furthermore, we validated our findings in a large number of clinical samples and identified the Melanoma Treg-Specific Regulatory Tag Set (Mel TregS). ELISA analysis showed that the protein levels of Mel TregS in melanoma Tregs were higher than in normal Tregs. We then utilized SERS technology to measure the signal values of Mel TregS in exosome, and successfully discriminated between healthy individuals and melanoma patients, as well as early and late-stage patients. This approach significantly enhanced detection sensitivity. In sum, our research elucidated fresh insights into the mechanisms governing Treg self-maintenance and communication with surrounding cells in melanoma. We also introduced an innovative method for clinical disease monitoring through SERS technology.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

Characterization of mRNA Signature in Milk Small Extracellular Vesicles from Cattle Infected with Bovine Leukemia Virus

This study aimed to characterize the mRNA signature of milk small extracellular vesicles (sEVs) from BLV-infected cattle. A total of 23 mRNAs, which showed greater abundance in milk sEVs from BLV-infected cattle compared to those from BLV-uninfected (control) cattle, were identified through microarray analyses conducted in our previous study. To assess the significance of these differences in mRNA abundance, milk was collected from six control cattle and twenty-six cattle infected with BLV. The infected cattle were categorized into two distinct groups based on their proviral loads: a group of eight cattle with low proviral loads (LPVL), characterized by <10,000 copies per 105 white blood cells (WBC), and a group of eighteen cattle with high proviral loads (HPVL), marked by ≥10,000 copies per 105 WBC. The qPCR analysis quantified 7 out of 23 mRNAs, including BoLA, CALB1, IL33, ITGB2, MYOF, TGFBR1, and TMEM156, in the milk sEVs from control cattle, LPVL cattle, and HPVL cattle. Significantly, the average relative expression of CALB1 mRNA in milk sEVs was higher in LPVL cattle compared to HPVL cattle and control cattle (p < 0.05), while it was relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). Likewise, the average relative expression of TMEM156 mRNA in milk sEVs was significantly higher in LPVL cattle compared to HPVL cattle (p < 0.05), and relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). The results indicate distinct patterns of CALB1 and TMEM156 mRNA levels in milk sEVs, with higher levels observed in LPVL cattle and lower levels in HPVL cattle. The current study could provide essential information to comprehend the complexities during the progression of BLV infection and direct the exploration of mRNA biomarkers for monitoring the clinical stage of BLV infection.

Identification of useful biomolecular markers in kidney renal clear cell carcinoma: an in silico and in vitro analysis-based study

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis.

METHODS

We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses.

RESULTS

The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the "Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells.

CONCLUSION

Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

Amorphous Calcium Carbonate Shows Anti-Cancer Properties That are Attributed to Its Buffering Capacity

AIM

Amorphous calcium carbonate (ACC) is a non-crystalline form of calcium carbonate, and it is composed of aggregated nano-size primary particles. Here, we evaluated its anti-cancer effect postulated relative to its buffering capabilities in lung cancer.

METHODS

Tumors were evaluated in vivo using the Lewis lung carcinoma (LLC) mouse cell line and A549 human lung cancer carcinoma cell line. LLC and A549 cells were injected subcutaneously into the right hind leg of mice. Treatments (ACC, cisplatin, vehicle, and ACC with cisplatin, all given via daily IP injections) started once tumors reached a measurable size. Treatments were carried out for 14 days in the LLC model and for 22 and 24 days in the xenograft model (two experiments). LLC tumors were resected from ACC at the end of the study, and vehicle groups were evaluated for cathepsin B activity. Differential gene expression was carried out on A549 cells following 8 weeks of in vitro culture in the presence or absence of ACC in a culture medium.

RESULTS

The ACC treatment decelerated tumor growth rates in both models. When tumor volumes were compared on the last day of each study, the ACC-treated animal tumor volume was reduced by 44.83% compared to vehicle-treated animals in the LLC model. In the xenograft model, the tumor volume was reduced by 51.6% in ACC-treated animals compared to vehicle-treated animals. A more substantial reduction of 74.75% occurred in the combined treatment of ACC and cisplatin compared to the vehicle (carried out only in the LLC model). Cathepsin B activity was significantly reduced in ACC-treated LLC tumors compared to control tumors. Differential gene expression results showed a shift towards anti-tumorigenic pathways in the ACC-treated A549 cells.

CONCLUSION

This study supports the ACC anti-malignant buffering hypothesis by demonstrating decelerated tumor growth, reduced cathepsin B activity, and altered gene expressions to produce anti-cancerous effects.

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10^-39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.

Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning

Background

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.

Methods

Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.

Results

Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.

Conclusion

By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.