Identification of small proline-rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

SPRR1B+ keratinocytes prime oral mucosa for rapid wound healing via STAT3 activation

Oral mucosal wounds exhibit accelerated healing with reduced scarring compared to cutaneous wounds, representing an optimal wound healing paradigm. However, the specific cellular subtypes orchestrating the efficient healing of mucosal tissues remain elusive. Through a comprehensive analysis integrating bulk-mRNA and single-cell sequencing data during the wound healing process in oral mucosa and skin, we have delineated a distinct set of genes markedly upregulated during tissue repair. This collection of wound healing-associated genesets was highly enriched in a specific keratinocyte subpopulation identified as STAT3-activated SPRR1B+ keratinocytes. Notably, despite the inherent rapidity of oral mucosal healing, the induction of SPRR1B+ keratinocytes is evident in both skin and mucosal wound healing processes in murine model. Intriguingly, these wound healing-promoting SPRR1B+ keratinocytes, which are induced via STAT3 activation, inherently abundant in unwounded normal mucosa but absent in normal skin. SPRR1B knockdown significantly inhibits mucosal keratinocyte migration, a critical attribute for effective wound healing. In summary, through analysis of human oral and skin wound healing processes at single-cell resolution, coupled with validation in murine model, suggests STAT3-activated SPRR1B+ keratinocytes are associated with the rapid mucosal repair process. This discovery underscores the potential application of SPRR1B+ keratinocytes in the therapeutic management of chronic or non-healing wounds.

Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.

Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.

SPRR1B is Related to the Immune Microenvironment and Can Be Used as a Biomarker for the Diagnosis of Psoriasis

Background

Psoriasis, a chronic inflammatory disorder with an unknown cause, significantly impacts the physical and psychological well-being of patients. However, current biomarkers related to psoriasis lack clinical specificity, sensitivity, and predictive ability.

Methods

In this study, we collected skin lesion tissues from 20 psoriasis patients and 20 normal skin samples. Additionally, we obtained four datasets from the GEO database, which included human psoriasis and healthy specimens. We utilized SVM-RFE analysis and the LASSO regression model to identify potential biomarkers. Furthermore, we examined the composition of immune cell types in psoriasis and their correlation with specific genes.

Results

Our investigation revealed 57 differentially expressed genes (DEGs), and we identified significantly enriched pathways through KEGG pathway analysis. The results of machine learning and WGCNA suggested that LCE3D and SPRR1B could potentially be used as marker genes for diagnosing psoriasis. RT-PCR and immunohistochemical detection confirmed the abnormally high expression of the SPRR1B gene in psoriasis. Analysis of immune cell infiltration revealed a strong positive correlation between SPRR1B and Macrophages M0 and T cells follicular helper, while showing the strongest negative correlation with resting Mast cells. In addition, we found that silencing SPRR1B in IFN-γ-treated HaCat cells could significantly reduce the increase in IL-17, IL-22, KRT6, and KRT16 caused by IFN-γ.

Conclusion

These findings suggest that SPRR1B may have a significant role in the pathogenesis of psoriasis and could be employed as a novel immunomarker for its development.

Targeting proprotein convertase subtilisin/kexin type 9 (PCSK9): from bench to bedside

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has evolved as a pivotal enzyme in lipid metabolism and a revolutionary therapeutic target for hypercholesterolemia and its related cardiovascular diseases (CVD). This comprehensive review delineates the intricate roles and wide-ranging implications of PCSK9, extending beyond CVD to emphasize its significance in diverse physiological and pathological states, including liver diseases, infectious diseases, autoimmune disorders, and notably, cancer. Our exploration offers insights into the interaction between PCSK9 and low-density lipoprotein receptors (LDLRs), elucidating its substantial impact on cholesterol homeostasis and cardiovascular health. It also details the evolution of PCSK9-targeted therapies, translating foundational bench discoveries into bedside applications for optimized patient care. The advent and clinical approval of innovative PCSK9 inhibitory therapies (PCSK9-iTs), including three monoclonal antibodies (Evolocumab, Alirocumab, and Tafolecimab) and one small interfering RNA (siRNA, Inclisiran), have marked a significant breakthrough in cardiovascular medicine. These therapies have demonstrated unparalleled efficacy in mitigating hypercholesterolemia, reducing cardiovascular risks, and have showcased profound value in clinical applications, offering novel therapeutic avenues and a promising future in personalized medicine for cardiovascular disorders. Furthermore, emerging research, inclusive of our findings, unveils PCSK9's potential role as a pivotal indicator for cancer prognosis and its prospective application as a transformative target for cancer treatment. This review also highlights PCSK9's aberrant expression in various cancer forms, its association with cancer prognosis, and its crucial roles in carcinogenesis and cancer immunity. In conclusion, this synthesized review integrates existing knowledge and novel insights on PCSK9, providing a holistic perspective on its transformative impact in reshaping therapeutic paradigms across various disorders. It emphasizes the clinical value and effect of PCSK9-iT, underscoring its potential in advancing the landscape of biomedical research and its capabilities in heralding new eras in personalized medicine.

Rank aggregation of independent genetic screen results highlights new strategies for adoptive cellular transfer therapy of cancer

Efficient intratumoral infiltration of adoptively transferred cells is a significant barrier to effectively treating solid tumors with adoptive cellular transfer (ACT) therapies. Our recent forward genetic, whole-genome screen identified T cell-intrinsic gene candidates that may improve tumor infiltration of T cells. Here, results are combined with five independent genetic screens using rank aggregation to improve rigor. This resulted in a combined total of 1,523 candidate genes - including 1,464 genes not currently being evaluated as therapeutic targets - that may improve tumor infiltration of T cells. Gene set enrichment analysis of a published human dataset shows that these gene candidates are differentially expressed in tumor infiltrating compared to circulating T cells, supporting translational potential. Importantly, adoptive transfer of T cells overexpressing gain-of-function candidates (AAK1ΔN125, SPRR1B, and EHHADH) into tumor-bearing mice resulted in increased T cell infiltration into tumors. These novel gene candidates may be considered as potential therapeutic candidates that can aid adoptive cellular therapy in improving T cell infiltration into solid tumors.

An integrated single-cell transcriptomic dataset for non-small cell lung cancer

As single-cell RNA sequencing (scRNA-seq) has emerged as a great tool for studying cellular heterogeneity within the past decade, the number of available scRNA-seq datasets also rapidly increased. However, reuse of such data is often problematic due to a small cohort size, limited cell types, and insufficient information on cell type classification. Here, we present a large integrated scRNA-seq dataset containing 224,611 cells from human primary non-small cell lung cancer (NSCLC) tumors. Using publicly available resources, we pre-processed and integrated seven independent scRNA-seq datasets using an anchor-based approach, with five datasets utilized as reference and the remaining two, as validation. We created two levels of annotation based on cell type-specific markers conserved across the datasets. To demonstrate usability of the integrated dataset, we created annotation predictions for the two validation datasets using our integrated reference. Additionally, we conducted a trajectory analysis on subsets of T cells and lung cancer cells. This integrated data may serve as a resource for studying NSCLC transcriptome at the single cell level.

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

Background

Treatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.

Methods

Based on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.

Results

Based on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.

Conclusions

PMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.

Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers (i.e., microRNA, long non-coding RNA, differentially methylated regions) were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g., SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.

Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis

BACKGROUND

Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear.

AIM

To further elucidate the value of EMT-related genes in LUAD prognosis.

METHODS

Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.

RESULTS

Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5).

CONCLUSION

The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

Comprehensive Integrated Single-Cell Whole Transcriptome Analysis Revealed the p-EMT Tumor Cells-CAFs Communication in Oral Squamous Cell Carcinoma

Cancer-associated fibroblasts (CAFs) and partial epithelial–mesenchymal transition (p-EMT) tumor cells are closed together and contribute to the tumor progression of oral squamous cell carcinoma (OSCC). In the present study, we deeply analyzed and integrated OSCC single-cell RNA sequencing datasets to define OSCC CAFs and p-EMT subpopulations. We highlighted the cell–cell interaction network of CAFs and p-EMT tumor cells and suggested biomarkers for the diagnosis and prognosis of OSCC during the metastasis condition. The analysis discovered four subtypes of CAFs: one p-EMT tumor cell population, and cycling tumor cells as well as TNFSF12-TNFRSF25/TNFRSF12A interactions between CAFs and p-EMT tumor cells during tumor metastasis. This suggests the prediction of therapeutically targetable checkpoint receptor–ligand interactions between CAFs and p-EMT tumor cells in OSCC regarding the metastasis status.

Combining EGFR-TKI With SAHA Overcomes EGFR-TKI-Acquired Resistance by Reducing the Protective Autophagy in Non-Small Cell Lung Cancer

Nowadays, lung cancer has the highest mortality worldwide. The emergence of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) has greatly improved the survival of patients with non-small cell lung cancer (NSCLC) having EGFR-TKI-sensitive mutations. Unfortunately, acquired resistance happens for most patients. In the present research, we found that EGFR-TKIs (such as gefitinib and osimertinib) can induce autophagy in NSCLC cell lines. Compared with parental sensitive cells, drug-resistant cells have higher autophagy activity. The use of an autophagy inhibitor could enhance the toxicity of gefitinib and osimertinib, which indicates that the enhancement of protective autophagy might be one of the mechanisms of EGFR-TKI resistance in NSCLC. In addition, increased autophagy activity is associated with decreased enhancer of zeste homolog 2 (EZH2) expression. Knockdown of EZH2 or EZH2 inhibitor treatment could lead to increased autophagy in NSCLC cells, indicating that EZH2 is a negative regulator of autophagy. We revealed that the increase in autophagy caused by the reduction of EZH2 was reversed in vitro and in vivo when combining gefitinib or osimertinib with suberoylanilide hydroxamic acid (SAHA), a broad-spectrum histone deacetylase inhibitor (HDACi). In conclusion, our results indicated that the combination of EGFR-TKIs and SAHA may be a new strategy to overcome EGFR-TKIs acquired resistance.

Identification of Potential Key Biomarkers and Immune Infiltration in Oral Lichen Planus

Background

Oral lichen planus (OLP) is a chronic autoimmune oral mucosal disease that seriously affects the life quality of the patients. But till now, the exact etiology and pathogenesis of OLP remain unclear. Our study is aimed at finding the key molecules and pathways involved in the pathogenesis mechanisms of OLP, providing more effective therapeutic strategies for OLP.

Methods

Data from GSE52130 were downloaded from GEO datasets for analysis. Then, we carried out enrichment analysis of the differentially expressed genes (DEGs) using Gene Ontology (GO) and KEGG pathway analyses. Next, the CIBERSORT algorithm was used to assess immune cell infiltration in OLP patients. Furthermore, we also constructed a protein-protein interaction network using STRING and Cytoscape and simultaneously sought potential transcription factors plug-in including MCODE CytoHubba and iRegulon. In addition, ROC analysis was employed to assess the diagnostic performance of these hub genes. Lastly, we identified 6 promising novel drugs to treat OLP through Connectivity Map.

Results

We illustrated that 255 DEGs were mainly enriched in the focal adhesion pathway and metabolism pathways. Besides, Cibersort analysis showed that M1 macrophages, T follicular helper cells, and T regulatory cells are more infiltrated in OLP samples. In addition, ROC analysis demonstrated that these hub genes owned higher diagnostic value in OLP, in which SPRR1B had the highest diagnostic value. And we also predicted that SOX7 was the most relevant transcription factor of those hub genes. Lastly, through the CMap database, we identified 6 small molecules as possible treatment drugs of OLP.

Conclusion

Our research identified that SPRR1B could be used as potential biomarkers for the early diagnosis of OLP. In addition, as a chronic autoimmune oral mucosal disease, OLP has different infiltration types of immune cells. Furthermore, 6 small molecules were proposed as promising novel treatment drugs for OLP patients. Therefore, our research may provide new impetus for the development of effective OLP biological treatment options.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

DGUOK-AS1 promotes cervical squamous cell carcinoma progression by suppressing miR-499a-5p that targets SPRR1B in vitro

PURPOSE

Cervical squamous cell carcinoma (CESC) is the most common cancer type of cervical cancer, which threatens women's life seriously. LncRNA DGUOK-AS1has been reported to promote the biologic processes of CESC. We aim to figure out the role of DGUOK-AS1-miR-499a-5p-SPRR1B axis in modulating the CESC progression in vitro.

METHODS

The levels of DGUOK-AS1, miR-499a-5p, and SPRR1B in CESC tissues and cells were examined by RT-qPCR. The interaction of DGUOK-AS1-miR-499a-5p-SPRR1B was verified by luciferase assay. Inhibition of DGUOK-AS1, miR-499a-5p, and SPRR1B was applied for exploring the biological function based on detection of cell viability, proliferation, migration, and apoptosis in CESC SiHa and HeLa cells.

RESULTS

DGUOK-AS1 and SPRR1B expressions were obviously elevated, whereas the expression of miR-499a-5p was reduced in both CESC tissues and cells. Silencing of DGUOK-AS1 attenuated cell growth and boosted apoptosis of CESC cells. Notably, DGUOK-AS1 inhibited miR-499a-5p to release SPRR1B, which significantly accelerated the development of CESC.

CONCLUSION

DGUOK-AS1sponging miR-499a-5p facilitated CESC cells progression by releasing SPRR1B in vitro. It provides a new sight for the treatment of CESC patients involving DGUOK-AS1-miR-499a-5p-SPRR1B.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Identification of small proline-rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

BACKGROUND

With the ongoing development of targeted therapy and immunotherapy in recent years, the overall five-year survival rate of NSCLC patients has not improved, and the search for novel diagnostic and prognostic markers for lung adenocarcinoma continues.

METHODS

Lung adenocarcinoma (LUAD) gene expression data and relevant clinical information were obtained from the TCGA. Hub genes were identified with weighted gene co-expression network analysis (WGCNA) and protein-protein interaction network (PPI). Survival analyses were also performed using GEPIA. The 536 LUAD patients were divided into two groups according to the SPRR1B expression level and analyzed by gene set enrichment analysis (GSEA) and verified by immunoblotting. The effects of SPRR1B on cell proliferation and cell metastasis and apoptosis were evaluated by 5-ethynyl-2'-deoxyuridine (EdU) staining, colony formation assay, transwell migration and invasion assay, and flow cytometry, respectively.

RESULTS

A total of 2269 DEGs were analyzed by WGCNA and five hub genes (CCK, FETUB, PCSK9, SPRR1B, and SPRR2D) were identified. Among them, SPRR1B was selected as one of the most significant prognostic genes in LUAD. SPRR1B was found to be highly expressed in lung adenocarcinoma cells compared with that in normal bronchial epithelial cells. In addition, silencing of SPRR1B could inhibit the cell proliferation, invasion, and migration of lung adenocarcinoma cells, but induced cell apoptosis and G2/M phase arrest in vitro. The result of GSEA and immunoblotting revealed that SPRR1B activated the MAPK signaling pathway involved in the proliferation and metastasis of lung cancer.

CONCLUSIONS

Our findings demonstrate that SPRR1B may function as a prognosis predictor in lung adenocarcinoma.