ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

Multi-scale transcriptomics reveals that specific tumor cells promote lung adenocarcinoma metastasis through crosstalk with the microenvironment

Most advanced lung adenocarcinoma (LUAD) patient deaths are attributed to metastasis. However, the complete understanding of the metastatic mechanism in LUAD remains elusive. Single-cell RNA-seq (scRNA-seq), spatial RNA-seq (stRNA-seq) and bulk RNA-seq of primary LUAD were integrated to investigate metastatic driver genes, cell-cell interactions, and spatial colocalization of cells and ligand-receptor pairs. A lung adenocarcinoma metastasis risk scoring model (LMRS) was established to estimate the risk of metastasis in LUAD. Forty-two metastasis driver genes were identified and tumor epithelial cells were classified into two subtypes. Epithelial cell subclass characterized by susceptibility to metastasis are referred to as Epithelial_LM, and the remaining as Epithelial_LL. Epithelial_LM subtype has intimate ligand-receptor interactions with inflammatory endothelial cells (iendo), inflammatory cancer-associated fibroblasts (iCAF), and NKT cells. Epithelial_LM cells have a spatial colocalization relationship with these three types of cells. The LMRS was established and its efficacy was verified in bulk RNA-seq. We identified a subclass of epithelial cells prone to metastasis and demonstrated the contribution of inflammatory stromal cells and NKT cells in facilitating tumor metastasis.

Crosstalk between lncRNAs and Wnt/β-catenin signaling pathways in lung cancers: From cancer progression to therapeutic response

Lung cancer (LC) is considered to have the highest mortality rate around the world. Because there are no early diagnostic signs or efficient clinical alternatives, distal metastasis and increasing numbers of recurrences are a challenge in the clinical management of LC. Long non-coding RNAs (lncRNAs) have recently been recognized as a critical regulator involved in the progression and treatment response to LC. The Wnt/β-catenin pathway has been shown to influence LC occurrence and progress. Therefore, discovering connections between Wnt signaling pathway and lncRNAs may offer new therapeutic targets for improving LC treatment and management. In this review, the purpose of this article is to present possible therapeutic approaches by reviewing particular relationships, key processes, and molecules associated to the beginning and development of LC.

Hyper-methylation and DNMT3A mediated LTC4S downregulation promoted lung adenocarcinoma tumorigenesis via mTORC1 signaling pathway

Background

Lung adenocarcinoma is a malignancy characterized by high mortality rates and unfavorable prognosis. However, the role of Leukotriene C4 Synthase (LTC4S) in lung cancer remains uninvestigated.

Methods

The expression and prognostic value of LTC4S in LUAD were analyzed using the GEPIA online database. Subsequently, the function of LTC4S in lung cancer cells was examined through gain-of function experiments, using assays to evaluate tumor malignant behavior. Subcutaneous xenograft experiments in vivo was used for investigating the functions of LTC4S. Then, tumor hallmark pathways were analyzed by GSEA. Western blot assay was used to validate the impact of LTC4S on mTORC1 pathway. Finally, the correlation of mRNA and methylation of LTC4S were analyzed by cBioPortal. qRT-PCR, ChIP-qPCR and ChIP-Atlas were used to verify the regulation factors of LTC4S low expression in LUAD cells.

Results

LTC4S presented significant decreased expression and favorable prognostic significance in LUAD. LTC4S was correlated with clinical stages in LUAD, which showed decreased expression gradually and significantly along with TNM stages. LTC4S-co-expressed genes were closely related to Ras signaling pathway, and MAPK signaling pathway. Overexpression of LTC4S inhibited cancer malignant phenotype and tumor growth in vitro and vivo. GSEA analysis and Western blot assay suggested low expression of LTC4S activated mTORC1 signaling pathway in LUAD. Moreover, the DNA methylation level of LTC4S in LUAD tissue was markedly elevated compared to normal tissue. The hypermethylation of the LTC4S promoter by DNMT3A leads to the decreased expression of LTC4S in LUAD.

Conclusions

In conclusion, low expression of LTC4S serves as an unfavorable prognostic marker and the critical function of LTC4S in controlling the progression of LUAD. This highlights the promise for exploring the clinical benefits of manipulating LTC4S in LUAD targeted therapies.

Deciphering disulfidptosis: Uncovering a lncRNA-based signature for prognostic assessment, personalized immunotherapy, and therapeutic agent selection in lung adenocarcinoma patients

BACKGROUND

Disulfidptosis, a recently identified type of regulated cell death, plays critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remains to be fully elucidated. We aimed to adopt this concept to develop and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Disulfidptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature DISULncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, nomogram predictor analysis, and functional assays in our validation process. We also compared DISULncSig with previous studies. We performed analyses to evaluate DISULncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of DISULncSig lncRNAs, and the ability of DISULncSig in pan-cancer was also assessed.

RESULTS

DISULncSig containing twelve lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, DISULncSig had more prognostic ability advantages. DISULncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high DISULncSig score population, we found ten drug candidates, among which epothilone-b may have the most potential. The pan-cancer analysis found that DISULncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the DISULncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic DISULncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Bioinformatics Integrative Analysis of Circadian Rhythms Effects on Atopic Dermatitis and Dendritic Cells

Background

Atopic dermatitis (AD) is an allergic inflammatory skin disease caused by aberrant and over-reactive immune response. Although circadian rhythm disruption is implicated in multiple immunoinflammatory conditions, including AD, the mechanisms at the molecular level underlying AD and circadian rhythms remain elusive.

Methods

Bulk and single-cell RNA-sequencing data of AD patients were acquired from the Gene Expression Omnibus, including GSE121212, GSE120721, and GSE153760 datasets. A single-sample gene set enrichment analysis was performed to estimate circadian rhythm gene expression levels. A differential expression analysis was utilized to identify the key candidate genes in AD. CIBERSORT was used to quantify the proportions of immune cells, and the R package "Seurat" was utilized to investigate single-cell RNA-sequencing data.

Results

Circadian rhythm gene expression levels were lower in AD skin samples than in normal skin samples. Dendritic cells were significantly upregulated and negatively correlated with circadian rhythm gene expression levels in AD patients. Compared with circadian rhythm-related genes in the control samples, ARNTL2, NOCT, and RORC were differentially expressed in AD; ARNTL2 and NOCT were significantly upregulated, whereas RORC was significantly downregulated in AD. ARNTL2, NOCT, and RORC also showed robust abilities to diagnose AD. We validated that the abundance of the dendritic cell was positively correlated with the ARNTL2 and NOCT expression levels using bulk RNA-sequencing data of the GSE121212 and single-cell RNA-sequencing data of the GSE153760. Moreover, the functional enrichment analysis showed that the IL-17 and NF-κB signaling pathways, Th1 and Th2 cell differentiations, and primary immunodeficiency, were enriched in AD patients.

Conclusion

The findings of this study suggested that the circadian rhythm is involved in the progression of AD, and RNTL2, NOCT, and RORC as well as dendritic cells are differentially expressed in AD. These findings could be used to introduce diagnostic and chronotherapeutic modalities for AD.

Evaluation of ITGB1 expression as a predictor of the therapeutic effects of immune checkpoint inhibitors in gastric cancer

BACKGROUND

Gastric cancer (CC) is a disease with high incidence and mortality rate. Immunotherapy is an important method for gastric cancer while lack of effective predictor. Integrins play an important role in the development. We aimed to explore the predictive value of β1 integrin (ITGB1) as a predictor of immunnotherapy in gastric cancer.

METHODS

Differential expression analysis was conducted using the Gene Expression Profiling Interactive Analysis (GEPIA) 2.0 and GEO databases. GEPIA data were used to evaluate the prognostic value of ITGB1 in gastric cancer (GC). Transcriptomic and clinical data of GC and normal tissues were downloaded from The Cancer Genome Atlas database, and the TIMER database was used to evaluate the association between ITGB1 and immune infiltration. Time-dependent receiver operating characteristic (ROC) curve analysis was used to determine the prognostic value of ITGB1. To verify ITGB1 expression at the protein level, immunohistochemical staining was conducted. In addition, to analyze the correlation of ITGB1 with PD-1 and PD-L1, we examined levels of PD-1 and PD-L1 by IHC and determined the predictive value of ITGB1 for anti-PD-1 therapy in GC by ROC curve analysis.

RESULTS

Compared with normal tissues, analysis of GEPIA and data at protein levels showed significantly higher expression of ITGB1 in GC. In addition, higher expression of ITGB1 was associated with worse pathological G-staging and tumor T-staging, which suggested that ITGB1 is a risk factor for poor prognosis in GC. The level of ITGB1 expression was positively correlated with CD8 + T cells, neutrophils, macrophages, and dendritic cells. ITGB1 expression was also correlated with PD-L1 expression, and this was further verified at the protein level by immunohistochemical analysis. The area under the ROC curve was 0.808.

CONCLUSION

ITGB1 may be a promising prognostic biomarker and effective predictor for anti-PD-1 therapy in GC.

TRIAL REGISTRATION

Retrospectively registered.

Systematic analysis of lncRNA gene characteristics based on PD-1 immune related pathway for the prediction of non-small cell lung cancer prognosis

BACKGROUND

Non-small cell lung cancer (NSCLC) is heterogeneous. Molecular subtyping based on the gene expression profiles is an effective technique for diagnosing and determining the prognosis of NSCLC patients.

METHODS

Here, we downloaded the NSCLC expression profiles from The Cancer Genome Atlas and the Gene Expression Omnibus databases. ConsensusClusterPlus was used to derive the molecular subtypes based on long-chain noncoding RNA (lncRNA) associated with the PD-1-related pathway. The LIMMA package and least absolute shrinkage and selection operator (LASSO)-Cox analysis were used to construct the prognostic risk model. The nomogram was constructed to predict the clinical outcomes, followed by decision curve analysis (DCA) to validate the reliability of this nomogram.

RESULTS

We discovered that PD-1 was strongly and positively linked to the T-cell receptor signaling pathway. Furthermore, we identified two NSCLC molecular subtypes yielding a significantly distinctive prognosis. Subsequently, we developed and validated the 13-lncRNA-based prognostic risk model in the four datasets with high AUC values. Patients with low-risk showed a better survival rate and were more sensitive to PD-1 treatment. Nomogram construction combined with DCA revealed that the risk score model could accurately predict the prognosis of NSCLC patients.

CONCLUSIONS

This study demonstrated that lncRNAs engaged in the T-cell receptor signaling pathway played a significant role in the onset and development of NSCLC, and that they could influence the sensitivity to PD-1 treatment. In addition, the 13 lncRNA model was effective in assisting clinical treatment decision-making and prognosis evaluation.

ITGB1-mediated molecular landscape and cuproptosis phenotype induced the worse prognosis in diffuse gastric cancer

Diffuse type gastric cancer was identified with relatively worse prognosis than other Lauren’s histological classification. Integrin β1 (ITGB1) was a member of integrin family which played a markedly important role in tumorigenesis and progression. However, the influence of ITGB1 in diffuse gastric cancer (DGC) remains uncertain. Here, we leveraged the transcriptomic and proteomic data to explore the association between ITGB1 expression and clinicopathologic information and biological process in DGC. Cell phenotype experiments combined with quantitative-PCR (q-PCR) and western blotting were utilized to identify the potential molecular mechanism underling ITGB1.Transcriptomics and proteomics both revealed that the higher ITGB1 expression was significantly associated with worse prognosis in DGC, but not in intestinal GC. Genomic analysis indicated that the mutation frequency of significantly mutated genes of ARID1A and COL11A1, and mutational signatures of SBS6 and SBS15 were markedly increased in the ITGB1 low expression subgroup. The enrichment analysis revealed diverse pathways related to dysregulation of ITGB1 in DGC, especially in cell adhesion, proliferation, metabolism reprogramming, and immune regulation alterations. Elevated activities of kinase-ROCK1, PKACA/PRKACA and AKT1 were observed in the ITGB1 high-expression subgroup. The ssGSEA analysis also found that ITGB1 low-expression had a higher cuproptosis score and was negatively correlated with key regulators of cuproptosis, including FDX1, DLAT, and DLST. We further observed that the upregulated expression of mitochondrial tricarboxylic acid (TCA) cycle in the ITGB1 low-expression group. Reduced expression of ITGB1 inhibited the ability of cell proliferation and motility and also potentiated the cell sensitive to copper ionophores via western blotting assay. Overall, this study revealed that ITGB1 was a protumorigenic gene and regulated tumor metabolism and cuproptosis in DGC.

circRanGAP1/miR-27b-3p/NRAS Axis may promote the progression of hepatocellular Carcinoma

BACKGROUND

Though circular RNAs (circRNAs) are the key regulators in tumor carcinogenesis, they remain largely unexplored in hepatocellular carcinoma (HCC).

METHODS

The expression of RanGAP1-derived circRNAs (circ_0063531, circ_0063534, circ_0063513, circ_0063518, circ_0063507, circ_0063723) were evaluated in eight paired HCC and normal tissues, and the correlation between circRanGAP1 (circ_0063531) expression and clinicopathological characteristics in 40 HCC patients was determined. The association between miR-27b-3p and circRanGAP1 or NRAS was predicted using bioinformatics analysis. The expression of circRanGAP1, miR-27b-3p, and NRAS were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The potential oncogenic role of circ-RanGAP1 was assessed using CCK-8, colony formation, transwell assays in vitro, subcutaneous tumor mouse model, vein tail metastatic model, and orthotopically implanted intrahepatic HCC model in vivo. Luciferase reporter and RNA immunoprecipitation (RIP) assays were used to explore the binding site between miR-27b-3p and circ-RanGAP1 or NRAS. Protein expression was detected using western blotting. The localization of miR-27b-3p and circ-RanGAP1 was investigated using fluorescence in situ hybridization (FISH). The level of immune infiltration was assessed by bioinformatics analysis, flow cytometry, and orthotopically implanted intrahepatic HCC models.

RESULTS

Here, we found elevated circRanGAP1 in the cells and clinical tissues of patients with HCC. Increased circRanGAP1 levels are associated with enlarged tumors and the advanced stage of TNM. CircRanGAP1 promotes the growth, migration, and HCC cell invasion, concurrently with the growth and metastasis of tumors in-vivo. Moreover, circRanGAP1 is mainly located inside the cytoplasm. Mechanistically, circRanGAP1 as an oncogene promotes HCC progression by miR-27b-3p/NRAS/ERK axis, furthermore, affects the infiltration level of tumor-associated macrophages probably by sponging miR-27b-3p. Immune infiltration analysis shows that NRAS is positively correlated with the levels of CD68+ tumor-associated macrophages in HCC samples and that NRAS and CD68 are related to the poor outcome of HCC.

CONCLUSION

These results reveal that circRanGAP1 is a HCC oncogene that function by the miR-27b-3p/NRAS/ERK axis and regulates the infiltration levels of tumor-associated macrophages by sponging miR-27b-3p. Therefore, circRANGAP1/ NRAS axis may be an important potential treatment target against HCC.

Interfering with ITGB1-DT expression delays cancer progression and promotes cell sensitivity of NSCLC to cisplatin by inhibiting the MAPK/ERK pathway

Long non-coding RNA ITGB1-DT is involved in the regulation of cancer growth and metastasis. However, the roles of ITGB1-DT in non-small cell lung cancer (NSCLC) progression and sensitivity to cisplatin has not been elucidated. ITGB1-DT expression in NSCLC tissues, and the relationship between ITGB1-DT expression with NSCLC diagnosis, prognosis, clinicopathological features, and immune cell infiltration were investigated in The Cancer Gene Atlas (TCGA) database. The roles and mechanisms of ITGB1-DT in cell growth, migration, and drug sensitivity of NSCLC cells were explored in the cell model. The prognostic nomograms of ITGB1-DT-related genes were evaluated using bioinformatics. ITGB1-DT was overexpressed in NSCLC. Elevated ITGB1-DT expression was related to the late T stage, N stage, M stage, short overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) of NSCLC patients. ITGB1-DT was the independent risk factors for poor prognosis, and had diagnostic value for NSCLC patients. Interfering with the ITGB1-DT expression can inhibit the proliferation, migration, and invasion of A549, H1299, and drug-resistant A549/DDP, possibly due to the inhibition of p38 MAPK and ERK phosphorylation levels. ITGB1-DT expression was correlated with the levels of NSCLC immune infiltration cells, such as the TReg, Th, and NK cells. ITGB1-DT-related gene nomograms were associated with the prognosis, and were expected to evaluate the prognosis of NSCLC patients. In conclusion, inhibition of ITGB1-DT expression delayed the growth and metastasis of NSCLC using the MAPK/ERK signaling mechanism and enhanced the sensitivity of NSCLC to cisplatin drugs. These results indicate that ITGB1-DT might be a biomarker for evaluating the diagnosis and prognosis of NSCLC patients.

Inhibition of ITGB1-DT expression delays the growth and migration of stomach adenocarcinoma and improves the prognosis of cancer patients using the bioinformatics and cell model analysis

BACKGROUND

The long non-coding RNA, integrin subunit beta 1 (ITGB1) divergent transcript (ITGB1-DT), is known to be involved in cancer progression and associated with the poor prognosis of cancer patients. At present, the role of ITGB1-DT in stomach adenocarcinoma (STAD) has not been reported.

METHODS

The expression level of ITGB1-DT was detected in normal gastric and STAD tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A receiver operating characteristic (ROC) analysis was used to evaluate the role of ITGB1-DT in diagnosing STAD. The relationship between ITGB1-DT overexpression and clinicopathological features, prognosis, and immune-infiltrated cells in STAD were explored using correlation, survival, and Cox regression analyses. A cell model of ITGB1-DT interference was constructed to explore the roles of ITGB1-DT on STAD cell proliferation and migration, and the signaling mechanism was investigated using Gene Set Enrichment Analysis (GSEA).

RESULTS

ITGB1-DT was expressed up-regulated in STAD tissues. ITGB1-DT overexpression was associated with the T stage, therapeutic effect, overall survival, progression-free interval status, and poor prognosis in STAD patients. ITGB1-DT overexpression was valuable in diagnosing STAD and a negative factor affecting the prognosis of STAD patients. Interference with ITGB1-DT expression inhibited STAD cell proliferation, invasion, and migration. GSEA results showed that ITGB1-DT may be involved in STAD progression through the insulin, p53, mechanistic target of rapamycin kinase (MTOR), and other signaling pathways. Overexpression of ITGB1-DT was significantly correlated with the levels of STAD B cells, T cells, T helper cells, CD8 T cells, cytotoxic cells, and other immune cells.

CONCLUSIONS

ITGB1-DT was overexpressed and associated with poor prognosis in STAD. Interference with ITGB1-DT expression may delay the progression of STAD to improve the prognosis of STAD patients.