LCP1 is a prognostic biomarker correlated with immune infiltrates in gastric cancer

Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients

Objective

This study aimed to identify prognostic signatures to predict the prognosis of patients with stomach adenocarcinoma (STAD), which is necessary to improve poor prognosis and offer possible treatment strategies for STAD patients.

Methods

The overlapping genes between the key model genes that were screened by the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) whose expression was different with significance between normal and tumor tissues were extracted to serve as co-expression genes. Then, enrichment analysis was performed on these genes. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression was performed to screen the hub genes among overlapping genes. Finally, we constructed a model to explore the influence of polygenic risk scores on the survival probability of patients with STAD, and interaction effect and mediating analyses were also performed.

Results

DEGs included 2,899 upregulated genes and 2,896 downregulated genes. After crossing the DEGs and light-yellow module genes that were obtained by WGCNA, a total of 39 overlapping genes were extracted. The gene enrichment analysis revealed that these genes were enriched in the prion diseases, biosynthesis of unsaturated fatty acids, RNA metabolic process, hydrolase activity, etc. PIP5K1P1, PTTG3P, and SNORD15B were determined by LASSO-Cox. The prognostic prediction of the three-gene model was established. The Cox regression analysis showed that the comprehensive risk score for three genes was an independent prognosis factor.

Conclusion

PIP5K1P1, PTTG3P, and SNORD15B are related to the prognosis and overall survival of patients. The three-gene risk model constructed has independent prognosis predictive ability for STAD.

LCP1 correlates with immune infiltration: a prognostic marker for triple-negative breast cancer

OBJECTIVE

Triple-Negative Breast Cancer (TNBC) is known for its aggressiveness and treatment challenges due to the absence of ER, PR, and HER2 receptors. Our work emphasizes the prognostic value of LCP1 (Lymphocyte cytosolic protein 1), which plays a crucial role in cell processes and immune cell activity, to predict outcomes and guide treatments in TNBC.

METHODS

We explored LCP1 as a potential biomarker in TNBC and investigated the mRNA and protein expression levels of LCP1. We investigated different databases, including GTEX, TCGA, GEO, cBioPortal and Kaplan-Meier Plotter. Immunohistochemistry on TNBC and benign tumor samples was performed to examine LCP1's relationship with patient clinical characteristics and macrophage markers. We also assessed survival rates, immune cell infiltration, and drug sensitivity related to LCP1 using various bioinformatics tools.

RESULTS

The results indicated that LCP1 expression was higher in TNBC tissues compared to adjacent normal tissues. However, high expression of LCP1 was significantly associated with favorable survival outcomes in patients with TNBC. Enrichment analysis revealed that genes co-expressed with LCP1 were significantly enriched in various immune processes. LCP1 showed a positive correlation with the infiltration of resting dendritic cells, M1 macrophages, and memory CD4 T cells, and a negative correlation with M2 macrophages. Further analysis suggested a link between high levels of LCP1 and increased survival outcomes in cancer patients receiving immunotherapy.

CONCLUSION

LCP1 may serve as a potential diagnostic and prognostic biomarker for TNBC, which was closely associated with immune cell infiltration, particularly M1 and M2 macrophages. Our findings may provide valuable insights into immunotherapeutic strategies for TNBC patients.

Identification of Immune Infiltrating Cell-Related Biomarkers in Early Gastric Cancer Progression

OBJECTIVES

Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.

METHODS

The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.

RESULTS

Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.

CONCLUSION

This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.

Multiple bioinformatics analysis identifies IGFBP1 as associated with the prognosis of stomach adenocarcinoma

This study aimed to screen the hub gene for predicting the prognosis of patients with stomach adenocarcinoma (STAD). The RNA-sequencing expression data and clinical data of STAD were collected from the cancer genome atlas. The R package "limma" was performed to ascertain the differentially expressed genes (DEGs) between the relapse group and non-relapse group, and the DEGs between the survival dead status group and survival alive status group were screened. The overlapping genes between 2 DEGs sets were identified by the Venn diagram. Many different bioinformatics analysis methods were performed to analyze the importance of hub genes. One gene signature, IGFBP1, was extracted. The KM plot indicated that STAD patients with low IGFBP1 mRNA expression have a shorter overall survival time. The top 100 co-expression genes of IGFBP1 were mainly enriched in complement and coagulation cascades, epithelial cell signaling in Helicobacter pylori infection, and Wnt signaling pathway. Immune infiltration analysis indicated IGFBP1 may inhibit immune cell infiltration in tumors by infiltration and immune escape, leading to tumor metastasis and progression. The bioinformatics analysis results indicate that IGFBP1 can be used as a tool to evaluate the mortality risk of patients with STAD.

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

BACKGROUND

Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs).

METHODS

Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein-protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed.

RESULTS

Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971-1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes.

CONCLUSION

The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

Transcriptome Profiling Analysis Identifies LCP1 as a Contributor for Chidamide Resistance in Gastric Cancer

BACKGROUND

Gastric cancer (GC) remains a significant health problem and carries with it substantial morbidity and mortality. Chidamide is a novel and orally administered histone deacetylase (HDAC) inhibitor and has been demonstrated its anti-tumor efficacy on different kinds of hematological and solid tumors. However, the underlying mechanism of chidamide resistance is still poorly characterized.

METHODS

We established chidamide resistant GC cell lines, AGS ChiR and MGC803 ChiR and investigated the toxicologic effects through cell survival, colony formation and flow cytometry assays in vitro, and a subcutaneous xenograft model in vivo. RNA-sequence was then performed to screen chidamide resistance-associated genes between AGS and AGS ChiR cells. The role of Lymphocyte cytosolic protein 1 (LCP1) in chidamide resistance was explored by gain- and loss-of-function analyses.

RESULTS

We found that chidamide significantly inhibited cell proliferation and induced the apoptosis in a concentration-dependent manner in wild-type GC cell lines as compared to chidamide resistant cell lines. The transcriptomic profiling, quantitative RT-PCR, and western blot data revealed that LCP1 was upregulated in AGS ChiR cells compared with parental cells. Overexpression of LCP1 conferred and knockdown of LCP1 attenuated the chidamide resistance of GC cells. Epigenetic derepression of LCP1 by chidamide may be a possible reason for the contribution of LCP1 to chidamide resistance.

CONCLUSIONS

These findings illustrated that LCP1 may play a chidamide resistance role in GC, suggesting that LCP1 could be a potential target for the therapy of GC combined with chidamide.

Systemically Silencing Long Non-coding RNAs Maclpil With Short Interfering RNA Nanoparticles Alleviates Experimental Ischemic Stroke by Promoting Macrophage Apoptosis and Anti-inflammatory Activation

Background

Maclpil is a proinflammatory long non-coding RNA highly expressed on monocyte-derived macrophages in the ischemic brain. This study investigated the impact and the mechanisms of systemically delivering nanoparticle Maclpil short interfering RNA (siRNA) on experimental ischemic stroke in a mouse model.

Methods

Ischemic stroke (focal cerebral ischemia) was induced in male C57BL/6 mice through the middle cerebral artery occlusion. Three hours thereafter, mice were intravenously injected with Maclpil siRNA or scramble siRNA nanoparticles. Bone marrow cell-derived macrophages were transfected with Maclpil or scramble siRNA and subjected to oxygen glucose deprivation culture. The influence of silencing Maclpil on stroke outcomes, neuroinflammation, and macrophage fates was assessed via histology, flow cytometry, Western blotting, and quantitative PCR analysis.

Results

Three days following stroke induction, siRNA silencing Maclpil substantially reduced ischemic infarction size and improved neurological behaviors. Silencing Maclpil also markedly attenuated the accumulation of monocyte-derived macrophages, CD4⁺ T cells, and CD8⁺ T cells in the ischemic hemisphere without affecting microglia cellularity. Reciprocally, myeloid cells and both subsets of T cells were elevated in mouse peripheral blood following Maclpil siRNA treatment. Under oxygen glucose deprivation conditions that mimicked hypoxia and hypoglycemia in vitro, Maclpil siRNA silencing augmented macrophage apoptosis in conjunction with upregulation of proapoptotic Bax and caspase 3 expressions. siRNA knocking down Maclpil skewed macrophages from proinflammatory classical toward anti-inflammatory alternative activation as evidenced by increased arginase 1, Ym1, and Fizz1 and reduced inducible nitric oxide synthase, IL-1β, and TNF-α mRNA levels. Consistent with macrophage phenotype switching, silencing Maclpil by siRNA enhanced fatty acid oxidation as indicated by increased mRNA levels of 3 key metabolic enzymes (ACADM, ACADVL, and HADHA).

Conclusion

Systemically silencing Maclpil by siRNA nanoparticles attenuated experimental ischemic stroke by promoting macrophage apoptosis and anti-inflammatory alternative activation. Identifying and targeting Maclpil human homolog(s) may help develop a novel therapy for stroke clinical management.

Distinct molecular subtypes of papillary thyroid carcinoma and gene signature with diagnostic capability

Papillary thyroid carcinoma (PTC) is heterogeneous and its molecular characteristics remain elusive. We integrated transcriptomic sequencing, genomic analysis and clinicopathologic information from 582 tissue samples of 216 PTC and 75 benign thyroid nodule (BTN) patients. We discovered four subtypes of PTC including Immune-enriched Subtype, BRAF-enriched Subtype, Stromal Subtype and CNV-enriched Subtype. Molecular subtypes were validated in an external cohort of 497 PTC cases from the TCGA. Tumors in the Immune-enriched Subtype showed higher immune infiltration and overexpression of immune checkpoints, whilst BRAF-enriched Subtype showed a higher tendency for extrathyroidal extension and more advanced TNM stage. Key oncogenes including LRRK2, SLC34A2, MUC1, FOXQ1 and KRT19 were overexpressed and enriched in oncogenic MAPK and PI3K/AKT signaling pathways in BRAF-enriched subtype. Further analysis of BRAF-enriched Subtype identified three subclasses with different degrees of malignancies. We also uncovered the molecular link of the initiation and progression from BTN to subtypes of PTC using trajectory analysis. Moreover, a 20-gene expression signature was generated for differential diagnosis of PTC from BTN patients. Together, our work identified previously unreported molecular subtypes of PTC, offering opportunities to stratify patients into optimal treatment plans based on molecular subtyping.