A ten-gene signature-based risk assessment model predicts the prognosis of lung adenocarcinoma

Identification of oxidative stress signatures of lung adenocarcinoma and prediction of patient prognosis or treatment response with single-cell RNA sequencing and bulk RNA sequencing data

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer globally, has seen improved prognosis with advancements in diagnostic, surgical, radiotherapy, and molecular therapy techniques, while its 5-year survival rate remains low. Molecular biomarkers provide prognostic value. Oxidative stress factors, such as reactive nitrogen species and ROS, are crucial in various stages of tumor progression, influencing cell transformation, proliferation, angiogenesis, and metastasis. ROS demonstrate dual roles, affecting tumor cells, hypoxia sensitivity, and the microenvironment. Comprehensive analysis of oxidative stress in LUAD has not been conducted to date. Therefore, we systematically investigated the regulatory patterns of oxidative stress in LUAD based on oxidative stress-related genes and correlated these patterns with cellular infiltration characteristics of the tumor immune microenvironment. The model utilizes single-factor Cox analysis to screen key differential genes with prognostic value and employs least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic-related prediction model. Ten candidate genes were selected based on this model. The risk score was constructed using the coefficients and expression levels of these ten genes. Furthermore, the impact of this risk score on overall survival (OS) was determined. Two genes with the most significant differential expression, SFTPB and S100P, were selected through qRT-PCR. Cell experiments including CCK-8, Edu, transwell assays confirmed their effects on lung cancer cells growth, consistent with the results of bioinformatics analysis. These findings suggested that this model held potential clinical value for evaluating the prognosis of lung adenocarcinoma.

Sideroflexin-1 promotes progression and sensitivity to lapatinib in triple-negative breast cancer by inhibiting TOLLIP-mediated autophagic degradation of CIP2A

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and it lacks specific therapeutic targets and effective treatment protocols. By analyzing a proteomic TNBC dataset, we found significant upregulation of sideroflexin 1 (SFXN1) in tumor tissues. However, the precise function of SFXN1 in TNBC remains unclear. Immunoblotting was performed to determine SFXN1 expression levels. Label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry were used to identify the downstream targets of SFXN1. Mechanistic studies of SFXN1 and cellular inhibitor of PP2A (CIP2A) were performed using immunoblotting, immunofluorescence staining, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Functional experiments were used to investigate the role of SFXN1 in TNBC cells. SFXN1 was significantly overexpressed in TNBC tumor tissues and was associated with unfavorable outcomes in patients with TNBC. Functional experiments demonstrated that SFXN1 promoted TNBC growth and metastasis in vitro and in vivo. Mechanistic studies revealed that SFXN1 promoted TNBC progression by inhibiting the autophagy receptor TOLLIP (toll interacting protein)-mediated autophagic degradation of CIP2A. The pro-tumorigenic effect of SFXN1 overexpression was partially prevented by lapatinib-mediated inhibition of the CIP2A/PP2A/p-AKT pathway. These findings may provide a new targeted therapy for patients with TNBC.

Effect of ZIC2 on immune infiltration and ceRNA axis regulation in lung adenocarcinoma via bioinformatics and experimental studies

OBJECTIVE

This study aimed to conclude the effect and mechanism of ZIC2 on immune infiltration in lung adenocarcinoma (LUAD).

METHODS

Expression of ZIC2 in several kinds of normal tissues of TCGA data was analyzed and its correlation with the baseline characteristic of LUAD patients were analyzed. The immune infiltration analysis of LUAD patients was performed by CIBERSORT algorithm. The correlation analysis between ZIC2 and immune cell composition was performed. Additionally, the potential upstream regulatory mechanisms of ZIC2 were predicted to identify the possible miRNAs and lncRNAs that regulated ZIC2 in LUAD. In vitro and in vivo experiments were also conducted to confirm the potential effect of ZIC2 on cell proliferation and invasion ability of LUAD cells.

RESULTS

ZIC2 expression was decreased in various normal tissues, but increased in multiple tumors, including LUAD, and correlated with the prognosis of LUAD patients. Enrichment by GO and KEGG suggested the possible association of ZIC2 with cell cycle and p53 signal pathway. ZIC2 expression was significantly correlated with T cells CD4 memory resting, Macrophages M1, and plasma cells, indicating that dysregulated ZIC2 expression in LUAD may directly influence immune infiltration. ZIC2 might be regulated by several different lncRNA-mediated ceRNA mechanisms. In vitro experiments validated the promotive effect of ZIC2 on cell viability and invasion ability of LUAD cells. In vivo experiments validated ZIC2 can accelerate tumor growth in nude mouse.

CONCLUSION

ZIC2 regulated by different lncRNA-mediated ceRNA mechanisms may play a critical regulatory role in LUAD through mediating the composition of immune cells in tumor microenvironment.

Integrated analysis of the role of PR/SET domain 14 in gastric cancer

BACKGROUND

Gastric cancer is one of the most common tumors worldwide, and most patients are deprived of treatment options when diagnosed at advanced stages. PRDM14 has carcinogenic potential in breast and non-small cell lung cancer. however, its role in gastric cancer has not been elucidated.

METHODS

We aimed to elucidate the expression of PRDM14 using pan-cancer analysis. We monitored the expression of PRDM14 in cells and patients using quantitative polymerase chain reaction, western blotting, and immunohistochemistry. We observed that cell phenotypes and regulatory genes were influenced by PRDM14 by silencing PRDM14. We evaluated and validated the value of the PRDM14-derived prognostic model. Finally, we predicted the relationship between PRDM14 and small-molecule drug responses using the Connectivity Map and The Genomics of Drug Sensitivity in Cancer databases.

RESULTS

PRDM14 was significantly overexpressed in gastric cancer, which identified in cell lines and patients' tissues. Silencing the expression of PRDM14 resulted in apoptosis promotion, cell cycle arrest, and inhibition of the growth and migration of GC cells. Functional analysis revealed that PRDM14 acts in epigenetic regulation and modulates multiple DNA methyltransferases or transcription factors. The PRDM14-derived differentially expressed gene prognostic model was validated to reliably predict the patient prognosis. Nomograms (age, sex, and PRDM14-risk score) were used to quantify the probability of survival. PRDM14 was positively correlated with sensitivity to small-molecule drugs such as TPCA-1, PF-56,227, mirin, and linsitinib.

CONCLUSIONS

Collectively, our findings suggest that PRDM14 is a positive regulator of gastric cancer progression. Therefore, it may be a potential therapeutic target for gastric cancer.

Lipid Metabolism-Related Gene Markers Used for Prediction Prognosis, Immune Microenvironment, and Tumor Stage of Pancreatic Cancer

Recently, more and more evidence shows that lipid metabolism disorder has been observed in tumor, which impacts tumor cell proliferation, survival, invasion, metastasis, and response to the tumor microenvironment (TME) and tumor treatment. However, hitherto there has not been sufficient research to demonstrate the role of lipid metabolism in pancreatic cancer. This study contrives to get an insight into the relationship between the characteristics of lipid metabolism and pancreatic cancer. We collected samples of patients with pancreatic cancer from the Gene Expression Omnibus (GEO), the Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the International Cancer Genome Consortium (ICGC) databases. Firstly, we implemented univariate regression analysis to get prognosis-related lipid metabolism genes screened and a construction of protein-protein interaction (PPI) network ensued. Then, contingent on our screening results, we explored the molecular subtypes mediated by lipid metabolism-related genes and the correlated TME cell infiltration. Additionally, we studied the disparately expressed genes among disparate lipid metabolism subtypes and established a scoring model of lipid metabolism-related characteristics using the least absolute shrinkage and selection operator (LASSO) regression analysis. At last, we explored the relationship between the scoring model and disease prognosis, tumor stage, tumor microenvironment, and immunotherapy. Two subtypes, C1 and C2, were identified, and lipid metabolism-related genes were studied. The result indicated that the patients with subtype C2 have a significantly lower survival rate than that of the patients with subtype C1, and we found difference in abundance of different immune-infiltrating cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed the association of these differentially expressed genes with functions and pathways related to lipid metabolism. Finally, we established a scoring model of lipid metabolism-related characteristics based on the disparately expressed genes. The results show that our scoring model have a substantial effect on forecasting the prognosis of patients with pancreatic cancer. The lipid metabolism model is an important biomarker of pancreatic cancer. Using the model, the relationship between disease prognosis, molecular subtypes, TME cell infiltration characteristics, and immunotherapy in pancreatic cancer patients could be explored.

PLEK2 mediates metastasis and invasion via α5-nAChR activation in nicotine-induced lung adenocarcinoma

Evidence has shown a strong relationship between smoking and epithelial mesenchymal transition (EMT). α5-nicotinic acetylcholine receptor (α5-nAChR) contributes to nicotine-induced lung cancer cell EMT. The cytoskeleton-associated protein PLEK2 is mainly involved in cytoskeletal protein recombination and cell stretch migration regulation, which is closely related to EMT. However, little is known about the link between nicotine/α5-nAChR and PLEK2 in lung adenocarcinoma (LUAD). Here, we identified a link between α5-nAChR and PLEK2 in LUAD. α5-nAChR expression was correlated with PLEK2 expression, smoking status and lower survival in vivo. α5-nAChR mediated nicotine-induced PLEK2 expression via STAT3. α5-nAChR/PLEK2 signaling is involved in LUAD cell migration, invasion and stemness. Moreover, PLEK2 was found to interact with CFL1 in nicotine-induced EMT in LUAD cells. Furthermore, the functional link among α5-nAChR, PLEK2 and CFL1 was confirmed in mouse xenograft tissues and human LUAD tissues. These findings reveal a novel α5-nAChR/PLEK2/CFL1 pathway involved in nicotine-induced LUAD progression.

DSCC1 interacts with HSP90AB1 and promotes the progression of lung adenocarcinoma via regulating ER stress

Lung cancer is a leading cause of cancer-related deaths, and the most common type is lung adenocarcinoma (LUAD). LUAD is frequently diagnosed in people who never smoked, patients are always diagnosed at advanced inoperable stages, and the prognosis is ultimately poor. Thus, there is an urgent need for the development of novel targeted therapeutics to suppress LUAD progression. In this study, we demonstrated that the expression of DNA replication and sister chromatid cohesion 1 (DSCC1) was higher in LUAD samples than normal tissues, and the overexpression of DSCC1 or its coexpressed genes were highly correlated with poor outcomes of LUAD patients, highlighting DSCC1 might be involved in LUAD progression. Furthermore, the expression of DSCC1 was positively correlated with multiple genetic mutations which drive cancer development, including TP53, TTN, CSMD, and etc. More importantly, DSCC1 could promote the cell proliferation, stemness, EMT, and metastatic potential of LUAD cells. In addition, DSCC1 interacted with HSP90AB1 and promoted the progression of LUAD via regulating ER stress. Meanwhile, DSCC1 expression negatively correlated with immune cell infiltration in lung cancer, and DSCC1 positively regulated the expression of PD-L1 in LUAD cells. Collectively, this study revealed that DSCC1 is a novel therapeutic target to treat LUAD and a biomarker for predicting the efficiency of PD-1/PD-L1 blockade treatment.

Comprehensive analysis of Cuproplasia and immune microenvironment in lung adenocarcinoma

Background: Trace elements such as copper are essential for human health. Recently the journal Nat Rev Cancer has put forward the concept of Cuproplasia, a way of promoting tumor growth through reliance on copper. We attempted to conduct a comprehensive analysis of Cuproplasia-related genes in lung adenocarcinoma (LUAD) to explore the mechanism of action of Cuproplasia-related genes in LUAD. Method: Transcriptome data and clinical information of LUAD were obtained from TCGA-LUAD and GSE31210, and prognostic models of Cuproplasia-related genes were constructed and verified by regression analysis of GSVA, WGCNA, univariate COX and lasso. The signal pathways affected by Cuproplasia-related genes were analyzed by GO, KEGG and hallmarK pathway enrichment methods. Five immunocell infiltration algorithms and IMVIGOR210 data were used to analyze immune cell content and immunotherapy outcomes in the high-low risk group. Results: In the results of WGCNA, BROWN and TURQUOISE were identified as modules closely related to Cuproplasia score. In the end, lasso regression analysis established a Cuproplasia-related signature (CRS) based on 24 genes, and the prognosis of high-risk populations was worse in TCGA-LUAD and GSE31210 datasets. The enrichment analysis showed that copper proliferation was mainly through chromosome, cell cycle, dna replication, g2m checkpoint and other pathways. Immunoinfiltration analysis showed that there were differences in the content of macrophages among the four algorithms. And IMVIGOR210 found that the lower the score, the more effective the immunotherapy was. Conclusion: The Cuproplasia related gene can be used to predict the prognosis and immunotherapy outcome of LUAD patients, and may exert its effect by affecting chromosome-related pathways and macrophages.

Identification of an optimized glycolytic-related risk signature for predicting the prognosis in breast cancer using integrated bioinformatic analysis

Aberrant metabolic disorders and significant glycolytic alterations in tumor tissues and cells are hallmarks of breast cancer (BC) progression. This study aims to elucidate the key biomarkers and pathways mediating abnormal glycolysis in breast cancer using bioinformatics analysis. Differential genes expression analysis, gene ontology analysis, Kyoto encyclopedia of genes and genomes analysis, gene set enrichment analyses, and correlation analysis were performed to explore the expression and prognostic implications of glycolysis-related genes. We effectively integrated 4 genes to construct a prognostic model of shorter survival in the high-risk versus low-risk group. The prognostic model showed promising predictive value and may be an integral part of the prognosis of BC. The survival analysis and receiver operating characteristic curves suggested that the signature showed a good predictive performance in both the The Cancer Genome Atlas training set and 2 gene expression omnibus validation sets. Multivariable analysis demonstrated that the 4-gene signature had an independent prognostic value. Furthermore, all calibration curves exhibited robust validity in prognostic prediction. We established an optimized 4-gene signature to clarify the connection between glycolysis and BC, and offered an attractive platform for risk stratification and prognosis predication of BC patients.

Drug resistance related genes in lung adenocarcinoma predict patient prognosis and influence the tumor microenvironment

Lung adenocarcinoma (LUAD) is the predominant type of non-small lung cancer (NSCLC) with strong invasive ability and poor prognosis. The drug resistance related genes are potentially associated with prognosis of LUAD. Our research aimed to identify the drug resistance related genes and explore their potential prognostic value in LUAD patients. The data used in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Firstly, we screened drug resistance related genes in LUAD by differential gene analysis, univariate Cox regression and drug sensitivity analyses. Subsequently, we constructed a risk score model using LASSO Cox regression analysis, and verified whether the risk score can predict the survival of LUAD patients independent of other factors. Moreover, we explored the immune infiltration of 22 immune cells between high-risk and low-risk patients. Totally 10 drug-resistance positively related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) were identified in LUAD. The risk score model of LUAD constructed with these 10 genes could reliably predict the prognosis of LUAD patients. 18 pathways were significantly activated in high-risk group compared with low-risk group. In addition, the infiltration proportion of multiple immune cells was significantly different between high-risk and low-risk groups, and the proportion of M1 phagocytes was significantly higher in the high-risk group compared with the low-risk group. The drug resistance related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) could predict the prognosis of LUAD patients. Clarifying the roles and mechanisms of these 10 genes in regulating drug resistance in LUAD will help to improve individualized clinical treatment protocols and predict patient sensitivity to treatment.

BAIAP2L2 is a novel prognostic biomarker related to migration and invasion of HCC and associated with cuprotosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and its pathophysiological mechanisms remain unknown. IRSp53 family members, such as BAIAP2L1, participate in the progression of multiple tumors. However, the role of BAIAP2L2 in HCC remains unclear. This study comprehensively analyzed the potential role of BAIAP2L2 in HCC using bioinformatic techniques. The expression of BAIAP2L2 in HCC was analyzed using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), and Human Protein Atlas (HPA) databases and in vitro experiments. In addition, the prognostic value of BAIAP2L2 in HCC was analyzed using the TCGA database. TCGA and GEO database were used to analyze the role of BAIAP2L2 in immune features. We also explored the function of BAIAP2L2 in methylation and cuprotosis. The CellMiner database was used to analyze the relationship between BAIAP2L2 expression and drug sensitivity. Our study revealed that BAIAP2L2 is overexpressed in HCC and promotes the migration and invasion of HCC cells. BAIAP2L2 may affect the prognosis of HCC by regulating immunity, methylation, and cuprotosis. BAIAP2L2 is a novel HCC prognostic gene involved in immune infiltration associated with cuprotosis and may be a potential prognosis and therapeutic target for HCC.

An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma

Lung adenocarcinoma (LUAD) occupies major causes of tumor death. Identifying potential prognostic risk genes is crucial to predict the overall survival of patients with LUAD. In this study, we constructed and proved an 11-gene risk signature. This prognostic signature divided LUAD patients into low- and high-risk groups. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.699, 5 years: 0.713, and 7 years: 0.716). Two GEO datasets also indicate the great accuracy of the risk signature (AUC = 782 and 771, respectively). Multivariate analysis identified 4 independent risk factors including stage N (HR 1.320, 95% CI 1.102-1.581, P = 0.003), stage T (HR 3.159, 95% CI 1.920-3.959, P < 0.001), tumor status (HR 5.688, 95% CI 3.883-8.334, P < 0.001), and the 11-gene risk model (HR 2.823, 95% CI 1.928-4.133, P < 0.001). The performance of the nomogram was good in the TCGA database (AUC = 0.806, 0.798, and 0.818 for 3-, 5- and 7-year survival). The subgroup analysis in different age, gender, tumor status, clinical stage, and recurrence stratifications indicated that the accuracy was high in different subgroups (all P < 0.05). Briefly, our work established an 11-gene risk model and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of LUAD patients for clinicians.

Comprehensive analysis of the role of SFXN family in breast cancer

The sideroflexin (SFXN) family is a group of mitochondrial membrane proteins. Although the function of the SFXN family in mitochondria has been widely recognized, the expression levels, role, and prognostic value of this family in breast cancer (BC) have not been clearly articulated and systematically analysed. In our research, SFXN1 and SFXN2 were significantly upregulated in BC versus normal samples based on Gene Expression Profiling Interactive Analysis 2 and the Human Protein Atlas databases. We found that high SFXN1 expression was significantly related to poor prognosis in BC patients and that high SFXN2 expression was significantly associated with good prognosis in BC patients. Gene Ontology analysis of the SFXN family was performed based on the STRING database to explore the potential functions of this family, including biological processes, cellular components, and molecular functions. Based on the MethSurv database, we found that two SFXN1 CpG sites (5′-UTR-S_Shelf-cg06573254 and TSS200-Island-cg17647431), two SFXN2 CpG sites (3′-UTR-Open_Sea-cg04774043 and Body-Open_Sea-cg18994254), one SFXN3 CpG site (Body-S_Shelf-cg17858697), and nine SFXN5 CpG sites (1stExon;5′-UTR-Island-cg03856450, Body-Open_Sea-cg04016113, Body-Open_Sea-cg04197631, Body-Open_Sea-cg07558704, Body-Open_Sea-cg08383863, Body-Open_Sea-cg10040131, Body-Open_Sea-cg10588340, Body-Open_Sea-cg17046766, and Body-Open_Sea-cg22830638) were significantly related to the prognosis of BC patients. According to the ENCORI database, four negative regulatory miRNAs for SFXN1 (hsa-miR-22-3p, hsa-miR-140-5p, hsa-miR-532-5p, and hsa-miR-582-3p) and four negative regulatory miRNAs for SFXN2 (hsa-miR-9-5p, hsa-miR-34a-5p, hsa-miR-532-5p, and hsa-miR-885-5p) were related to poor prognosis for BC patients. This study suggests that SFXN1 and SFXN2 are valuable biomarkers and treatment targets for patients with BC.

Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis

Background: Lung cancer poses great threat to human health, and lung adenocarcinoma (LUAD) is the main subtype. Immunotherapy has become first line therapy for LUAD. However, the pathogenic mechanism of LUAD is still unclear. Methods: We scored immune-related pathways in LUAD patients using single sample gene set enrichment analysis (ssGSEA) algorithm, and further identified distinct immune-related subtypes through consistent clustering analysis. Next, immune signatures, Kaplan-Meier survival analysis, copy number variation (CNV) analysis, gene methylation analysis, mutational analysis were used to reveal differences between subtypes. pRRophetic method was used to predict the response to chemotherapeutic drugs (half maximal inhibitory concentration). Then, weighted gene co-expression network analysis (WGCNA) was performed to screen hub genes. Significantly, we built an immune score (IMscore) model to predict prognosis of LUAD. Results: Consensus clustering analysis identified three LUAD subtypes, namely immune-Enrich subtype (Immune-E), stromal-Enrich subtype (Stromal-E) and immune-Deprived subtype (Immune-D). Stromal-E subtype had a better prognosis, as shown by Kaplan-Meier survival analysis. Higher tumor purity and lower immune cell scores were found in the Immune-D subtype. CNV analysis showed that homologous recombination deficiency was lower in Stromal-E and higher in Immune-D. Likewise, mutational analysis found that the Stromal-E subtype had a lower mutation frequency in TP53 mutations. Difference in gene methylation (ZEB2, TWIST1, CDH2, CDH1 and CLDN1) among three subtypes was also observed. Moreover, Immune-E was more sensitive to traditional chemotherapy drugs Cisplatin, Sunitinib, Crizotinib, Dasatinib, Bortezomib, and Midostaurin in both the TCGA and GSE cohorts. Furthermore, a 6-gene signature was constructed to predicting prognosis, which performed better than TIDE score. The performance of IMscore model was successfully validated in three independent datasets and pan-cancer.

Shared network pattern of lung squamous carcinoma and adenocarcinoma illuminates therapeutic targets for non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is a malignant tumor with high mortality. Lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the common subtypes of NSCLC. However, how LUSC and LUAD are compatible remains to be elucidated.

Methods

We used a network approach to find highly interconnected genes shared with LUSC and LUAD, and we then built modules to assess the degree of preservation between them. To quantify this result, Z-scores were used to summarize the interrelationships between LUSC and LUAD. Furthermore, we correlated network hub genes with patient survival time to identify risk factors.

Results

Our findings provided a look at the regulatory pattern for LUSC and LUAD. For LUSC, several genes, such as AKR1C1, AKR1C2, and AKR1C3, play key roles in regulating network modules of cell growth pathways. In addition, CCL19, CCR7, CCL21, and LY9 are enriched in LUAD network modules of T lymphocyte-related pathways. LUSC and LUAD have similar expressed gene expression patterns. Their networks share 46 hub genes with connectivity greater than 0.9. These genes are correlated with patient survival time. Among them, the expression level of COL5A2 in LUSC and LUAD is higher than that in normal tissues, which is closely related to the poor prognosis of LUSC and LUAD patients.

Conclusion

LUSC and LUAD share a network pattern. COL5A2 may be a risk factor in poor prognosis in LUSC and LUAD. The common landscape of LUSC and LUAD will help better define the regulation of NSCLC candidate genes and achieve the goals of precision medicine.

Transcriptomic data exploration of consensus genes and molecular mechanisms between chronic obstructive pulmonary disease and lung adenocarcinoma

Most current research has focused on chronic obstructive pulmonary disease (COPD) and lung adenocarcinoma (LUAD) alone; however, it is important to understand the complex mechanism of COPD progression to LUAD. This study is the first to explore the unique and jointly molecular mechanisms in the pathogenesis of COPD and LUAD across several datasets based on a variety of analysis methods. We used weighted correlation network analysis to search hub genes in two datasets from public databases: GSE10072 and GSE76925. We explored the unique and jointly molecular mechanistic signatures of the two diseases in pathogenesis through enrichment analysis, immune infiltration analysis, and therapeutic targets analysis. Finally, the results were confirmed using real-time quantitative reverse transcription PCR. Fifteen hub genes were identified: GPI, EZH2, EFNA4, CFB, ENO1, SH3PXD2B, SELL, CORIN, MAD2L1, CENPF, TOP2A, ASPM, IGFBP2, CDKN2A, and ELF3. For the first time, SELL, CORIN, GPI, and EFNA4 were found to play a role in the etiology of COPD and LUAD. The LUAD genes identified were primarily involved in the cell cycle and DNA replication processes; COPD genes we found were related to ubiquitin-mediated proteolysis, ribosome, and T/B-cell receptor signaling pathways. The tumor microenvironment of LUAD pathogenesis was influenced by CD4 + T cells, type 1 regulatory T cells, and T helper 1 cells. T follicular helper cells, natural killer T cells, and B cells all impact the immunological inflammation in COPD. The results of drug targets analysis suggest that cisplatin and tretinoin, as well as bortezomib and metformin may be potential targeted therapy for patients with COPD combined LUAD. These signatures may be provided a new direction for developing early interventions and treatments to improve the prognosis of COPD and LUAD.

Overexpression of SFXN1 indicates poor prognosis and promotes tumor progression in lung adenocarcinoma

Sideroflexin 1 (SFXN1) functions as a mitochondrial serine transporter in one-carbon metabolism. The association between SFXN1 and tumorigenesis remains to be elucidated. This study illustrated the functional role of SFXN1 in lung adenocarcinoma (LUAD). SFXN1 expression in LUAD specimens was examined using western blotting and quantitative real-time PCR (qRT-PCR), and the prognostic value between SFXN1 and clinicopathological parameters was investigated. Subsequently, the effects of SFXN1 on cellular proliferation, migration, and apoptosis were assessed by using Transwell assays and flow cytometry in A549 and H1299 cell lines. Western blotting was also employed to explore the mechanism of tumor progression. SFXN1 was significantly elevated in the LUAD samples compared with the para-carcinoma tissues. Furthermore, SFXN1 expression was an independent prognostic predictor for patients with LUAD. The expression of SFXN1 was altered in A549 and H1299 cell lines and this showed that SFXN1 promoted cell proliferation, migration, and invasion and inhibited apoptosis. SFXN1, at least partially, influenced LUAD progression via the mTOR signaling pathway. Collectively, the findings from this study demonstrated that SFXN1 promotes LUAD progression via the mTOR pathway and that SFXN1 expression is associated with clinicopathological features of LUAD. SFXN1 significantly contributes to the development of LUAD and might have potential, not only as an independent prognostic marker of LAUD but also as a promising target for LUAD therapy.

C1QTNF6 Targeted by MiR-184 Regulates the Proliferation, Migration, and Invasion of Lung Adenocarcinoma Cells

OBJECTIVE

To seek out the mechanism by which C1QTNF6 mediates lung adenocarcinoma (LUAD).

METHODS

Differentially expressed mRNAs and miRNAs in LUAD were analyzed using bioinformatics. In LUAD cells, C1QTNF6 mRNA and miR-184 expression were evaluated with qRT-PCR, and C1QTNF6 protein level was assessed by western blot. Cellular behaviors were assessed by colony formation, CCK-8, Transwell, and wound healing methods. The binding ability of miR-184 to C1QTNF6 was observed by dual-luciferase assay.

RESULTS

High expression of C1QTNF6 in LUAD stimulated cancer cellular behaviors. MiR-184 was lowly expressed in LUAD and downregulated C1QTNF6 expression. MiR-184 restrained LUAD cell processes by targeting C1QTNF6.

CONCLUSION

MiR-184 repressed LUAD cell processes via mediating C1QTNF6. MiR-184 and C1QTNF6 are expected to be indicators for LUAD treatment.

A Six-Gene Risk Model Based on the Immune Score Reveals Prognosis in Intermediate-Risk Acute Myeloid Leukemia

Tumor microenvironment (TME) has been revealed as an important determinant of diagnosis and treatment response in AML patients. The scores of immune and stromal cell scores of AML in the intermediate-risk group from The Cancer Genome Atlas (TCGA) database were calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Differentially expressed genes were identified between high and low scores. Gene set enrichment and pathway analyses were performed. A risk score model based on TME for six immune-related genes was established and validated. Patients with a lower immune score had a longer overall survival than those with a higher score (P = 0.044). A total of 805 intersected genes as differentially expressed genes were identified and selected according to the comparison of both immune and stromal scores. The functional enrichment analysis shows that these genes are mainly associated with the immune/inflammatory response. The risk score model based on TME for six immune-related genes (including MEF2C, ENPP2, FAM107A, CD37, TNFAIP8L2, and CASS4) was established and validated in the TCGA database and well validated in the TARGET database (P = 0.005). A key microenvironment-related gene signature was identified that affects the outcomes of AML patients in the intermediate-risk group and might serve as therapeutic targets.

Construction of a Prognosis-Related Gene Signature by Weighted Gene Coexpression Network Analysis in Ewing Sarcoma

Background

Ewing sarcoma (ES) is the second most common pediatric bone tumor with a high rate of metastasis, high recurrence, and low survival rate. Therefore, the identification of new biomarkers which can improve the prognosis of ES patients is urgently needed.

Methods

Here, GSE17679 dataset was downloaded from GEO databases. WGCNA method was used to identify one module associating with OVS (overall vital survival) and event. cytoHubba was used to screen out 50 hub genes from the module genes. Then, GSE17679 dataset was randomly divided into train cohort and test cohort. Next, univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were conducted on 50 hub genes combined with train cohort data to select pivotal genes. Finally, an optimal 7-gene-based risk assessment model was established, which was verified by test cohort, entire GSE17679, and two independent datasets (GSE63157 and TCGA-SARC).

Results

The results of the functional enrichment analysis revealed that the OVS and event-associated module were mainly enriched in the protein transcription, cell proliferation, and cell-cycle control. And the train cohort was divided into high-risk and low-risk subgroups based on the median risk score; the results showed that the survival of the low-risk subgroup was significantly longer than high-risk. ROC analysis revealed that AUC values of 1, 3, and 5-year survival were 0.85, 0.94, and 0.88, and Kaplan-Meier analysis also revealed that P value < 0.0001, indicating that this model was accurate, which was also verified in the test, entire cohort, and two independent datasets (GSE63157 and TCGA-SARC). Then, we performed a comprehensive analysis (differential expression analysis, correlation analysis and survival analysis) of seven pivotal genes, and found that four genes (NCAPG, KIF4A, NUF2 and CDC20) plays a more crucial role in the prognosis of ES.

Conclusion

Taken together, this study established an optimal 7-gene-based risk assessment model and identified 4 potential therapeutic targets, to improve the prognosis of ES patients.

Construction and Validation of a Glioma Prognostic Model Based on Immune Microenvironment

OBJECTIVE

This study aims to construct a prognostic model based on the different immune infiltration statuses of the glioma samples.

METHODS

Glioma-associated dataset was assessed from The Cancer Genome Atlas database. Hierarchical cluster analysis was performed to classify the glioma samples. Single-sample gene set enrichment analysis was introduced to the glioma samples for immune infiltration analysis. Kaplan-Meier survival analysis was applied to evaluate patients' prognoses. The differentially expressed genes (DEGs) between different sample groups were screened using limma package. Univariate Cox, LASSO Cox, and multivariate Cox regression analyses were employed to construct the prognostic model. The prediction performance of the model was examined by plotting a receiver-operating characteristic (ROC) curve, and GSEA was introduced to screen the differently activated pathways between high- and low-risk groups.

RESULTS

The glioma samples were classified into 3 clusters where the different immune infiltration and survival statuses were presented among the clusters. 123 immune-related DEGs were screened from the differential expression analyses, and based on these DEGs, an 8-gene prognostic model was constructed. The ROC curve exhibited an optimal performance of the prognostic model, and GSEA showed that ECM-receptor interaction, complement and coagulation cascades, cytokine receptor pathways, and viral protein interaction with cytokine were differently activated between the two risk groups.

CONCLUSION

The current study screened an immune-associated gene set by classifying and differential analysis, followed by constructing an 8-gene prognostic model based on the screened genes.

[A Nomogram for Prediction of Complications Based on TM&M System of VATS Major Lung Surgery for Lung Cancer]

BACKGROUND

Postoperative complications are an important cause of death after lung resection. At present, the adoption of video assisted thoracoscopic surgery (VATS) for lung cancer in China is increasing every year, but the prediction model of postoperative complications of VATS for lung cancer is still lack of evidence based on large sample database. In this study, Thoracic Mortality and Morbidity (TM&M) classification system was used to comprehensively describe the postoperative complications of VATS major lung resection in our center, and the prediction model of complications was established and verified. The model can provide basis for the prevention and intervention of postoperative complications in such patients, and accelerate the recovery of patients.

METHODS

The clinical data of patients underwent VATS major lung resection in our center from January 2007 to December 2018 were collected retrospectively. Only patients with stage I-III lung cancer were included. The postoperative complications were registered strictly by TM&M classification system. The patients were divided into two groups according to the operation period: the early phase group (From 2007 to 2012) and the late phase group (From 2013 to 2018). The baseline data of the two groups were matched by propensity score matching. After matching, binary logistic regression analysis was used to establish the prediction model of complications, and bootstrap internal sampling was used for internal verification.

RESULTS

A total of 2,881 patients with lung cancer were included in the study, with an average age of (61.0±10.1) years, including 180 major complications (6.2%). Binary Logistic regression analysis of 1,268 matched patients showed: age (OR=1.04, 95%CI: 1.02-1.06, P<0.001), other period (OR=0.62, 95%CI: 0.49-0.79, P<0.001), pathological type (OR=1.73, 95%CI: 1.24-2.41, P=0.001), blood loss (OR=1.001, 95%CI: 1.000-1.003, P=0.03), dissected lymph nodes (OR=1.022, 95%CI: 1.00-1.04, P=0.005) were independent risk factors for postoperative complications. The ROC curve indicates that the model has good discrimination (C-index=0.699), and the C-index is 0.680 verified by bootstrap internal sampling for 1,000 times. The calibration curve shows a good calibration of the prediction model.

CONCLUSIONS

TM&M system can comprehensively and accurately report the postoperative complications of thoracoscopic lung cancer surgery. Age, operative period, pathological type, intraoperative bleeding and dissected lymph nodes were independent risk factors for postoperative complications of VATS major lung resection for lung cancer. The established complication prediction model has good discrimination and calibration.

Emerging Roles of Pleckstrin-2 Beyond Cell Spreading

Pleckstrin-2 is a member of pleckstrin family with well-defined structural features that was first identified in 1999. Over the past 20 years, our understanding of PLEK2 biology has been limited to cell spreading. Recently, increasing evidences support that PLEK2 plays important roles in other cellular events beyond cell spreading, such as erythropoiesis, tumorigenesis and metastasis. It serves as a potential diagnostic and prognostic biomarker as well as an attractive target for the treatment of cancers. Herein, we summary the protein structure and molecular interactions of pleckstrin-2, with an emphasis on its regulatory roles in tumorigenesis.

Prognostic Value of lncRNA DRAIC and miR-3940-3p in Lung Adenocarcinoma and Their Effect on Lung Adenocarcinoma Cell Progression

Purpose

Lung adenocarcinoma (LUAD) is a most common malignant tumor, even worse for diseases with relatively poor prognosis. Non-coding RNAs have the potential to be biomarkers for the prognosis of various cancers. LncRNA DRAIC and miR-3940-3p have been screened as dysregulated RNAs in LUAD. The clinical significance and biological function of lncRNA DRAIC and miR-3940-3p in LUAD were assessed in this study.

Patients and Methods

A total of 122 cases of LUAD patients with complete clinical information were enrolled. The expression levels of lncRNA DRAIC and miR-3940-3p were determined via RT-qPCR in LUAD tissues and cells. The relationship between lncRNA DRAIC or miR-3940-3p expression and the clinicopathological features of patients was analyzed based on the Pearson Chi-square test. For the prognostic value, the Kaplan–Meier plot and multi-variate Cox proportional regression analysis were introduced. Finally, the effect of lnc DRAIC and miR-3940-3p on the LUAD cellular function was investigated by CCK-8 and Transwell assay.

Results

lnc DRAIC was upregulated in LUAD tissues and cells, but miR-3940-3p was downregulated. Both of them showed significant associations with and TNM stage, lymph node metastasis, and a poor prognosis. Lnc-DRAIC and miR-3940-3p have the potential as independent prognostic factors for LUAD. Furthermore, the inhibition of lnc DRAIC can inhibit cell proliferation, migration, and invasion of LUAD partly as a ceRNA of miR-3940-3p.

Conclusion

lncRNA DRAIC/miR-3940-3p axis may be involved in the progression of LUAD and can be developed to promising prognostic factors, which may provide new insights into the treatment of LUAD.

Silencing ZIC2 abrogates tumorigenesis and anoikis resistance of non-small cell lung cancer cells by inhibiting Src/FAK signaling

Aberrant expression of the zinc finger protein (ZIC) family has been extensively reported to contribute to progression and metastasis in multiple human cancers. However, the functional roles and underlying mechanisms of ZIC2 in non-small cell lung cancer (NSCLC) are largely unknown. In this study, ZIC2 expression was evaluated using qRT-PCR, western blot, and immunohistochemistry, respectively. Animal experiments in vivo and functional assays in vitro were performed to investigate the role of ZIC2 in NSCLC. Luciferase assays and chromatin immunoprecipitation (ChIP) were carried out to explore the underlying target involved in the roles of ZIC2 in NSCLC. Here, we reported that ZIC2 was upregulated in NSCLC tissues, and high expression of ZIC2 predicted worse overall and progression-free survival of NSCLC patients. Silencing ZIC2 repressed tumorigenesis and reduced the anoikis resistance of NSCLC cells. Mechanical investigation further revealed that silencing ZIC2 transcriptionally inhibited Src expression and inactivated steroid receptor coactivator/focal adhesion kinase signaling, which further attenuated the anoikis resistance of NSCLC cells. Importantly, our results showed that the number of circulating tumor cells (CTCs) was positively correlated with ZIC2 expression in NSCLC patients. Collectively, our findings unravel a novel mechanism implicating ZIC2 in NSCLC, which will facilitate the development of anti-tumor strategies in NSCLC.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the main causes of cancer deaths globally. Redox is emerging as a crucial contributor to the pathophysiology of LUAD, which can be regulated by long non-coding RNAs (lncRNAs). The aim of our research is to identify a novel redox-related lncRNA prognostic signature (redox-LPS) for better prediction of LUAD prognosis. 535 LUAD samples from The Cancer Genome Atlas (TCGA) database and 226 LUAD samples from the Gene Expression Omnibus (GEO) database were included in our study. 67 redox genes and 313 redox-related lncRNAs were identified. After performing LASSO-Cox regression analysis, a redox-LPS consisting of four lncRNAs (i.e., CRNDE, CASC15, LINC01137, and CYP1B1-AS1) was developed and validated. Our redox-LPS was superior to another three established models in predicting survival probability of LUAD patients. Univariate and multivariate Cox regression analysis revealed that risk score and stage were independent prognostic indicators. A nomogram plot including risk score and stage was constructed to predict survival probability of LUAD patients; this was further verified by calibration curves. Functional enrichment analysis and gene set enrichment analysis, were performed to determine the differences in cellular processes and signaling pathways between the high – and low-risk subgroups. A variety of algorithms (such as single-sample gene set enrichment analysis and CIBERSOFT) were conducted to uncover the landscape of tumor immune microenvironment in the high- and low-risk subgroups. In conclusion, a novel independent redox-LPS was constructed and validated for LUAD patients, which might provide new insights for clinical decision-making and precision medicine.

Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma

Background: Epigenetic dysregulation has been increasingly proposed as a hallmark of cancer. Here, the aim of this study is to establish an epigenetic-related signature for predicting the prognosis of lung adenocarcinoma (LUAD) patients.

Results: Five epigenetic-related genes (ERGs) (ARRB1, PARP1, PKM, TFDP1, and YWHAZ) were identified as prognostic hub genes and used to establish a prognostic signature. According our risk score system, LUAD patients were stratified into high and low risk groups, and patients in the high risk group had a worse prognosis. ROC analysis indicated that the signature was precise in predicting the prognosis. A new nomogram was constructed based on the five hub genes, which can predict the OS of every LUAD patients. The calibration curves showed that the nomogram had better accuracy in prediction. Finally, candidate drugs that aimed at hub ERGs were identified, which included 47 compounds.

Conclusions: Our epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs.

Methods: The genomic data and clinical data of LUAD cohort were downloaded from the TCGA database and ERGs were obtained from the EpiFactors database. GSE31210 and GSE50081 microarray datasets were included as independent external datasets. Univariate Cox, LASSO regression, and multivariate Cox analyses were applied to construct the epigenetic-related signature.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.

Methods

Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.

Results

A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.

Conclusion

Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01975-z.

The S100 protein family in lung cancer

The S100 protein family is involved in the pathogenesis of several malignancies including lung cancer. Recent studies have shown that one member, S100A2, was over-expressed in advanced stage non-small cell lung cancer (NSCLC). Another, S100A6, demonstrated variable expression in different lung cancer subtypes. Research using NSCLC cell lines reported that SIX3 inhibited cell metastasis and proliferation via S100P down-regulation. This review represents an update on S100 proteins in lung cancer from 2017 to 2021 and includes the aforementioned as well as S100A4, S100A7, and S100B. Inconsistencies in mechanisms of action for S100A8/S100A9 are highlighted and a comprehensive evaluation of the most recent evidence for the S100 proteins in lung cancer is presented.

BAIAP2L2 facilitates the malignancy of prostate cancer (PCa) via VEGF and apoptosis signaling pathways

BACKGROUND

Prostate cancer (PCa) is the second most common type of male cancer in western. Despite key roles of brain-specific angiogenesis inhibitor 1-associated protein like 2 (BAIAP2L2) in several cancers, the function of BAIAP2L2 in PCa is never reported.

OBJECTIVE

We aimed to investigate the role of BAIAP2L2 in the progression of PCa and decipher the underlying mechanisms.

METHODS

RNA sequencing data from TCGA database were used to evaluate the expression of BAIAP2L2 in PCa. Survival analysis and Cox regression model analysis were conducted to evaluate the prognostic value of BAIAP2L2. BAIAP2L2-associated pathways were preliminary analyzed by Gene Set Enrichment Analysis (GSEA) method and confirmed by western blot assays. Cell proliferation and transwell assays were performed to determine biological behaviors in BAIAP2L2 knocked-down or overexpressed PCa cell lines including LNCaP and PC-3 cells.

RESULTS

In our study, BAIAP2L2 was significantly up-regulated in PCa tissues and cell lines and independently associated with the poor prognosis of PCa patients. Knockdown of BAIAP2L2 notably repressed proliferation, migration and invasion of PCa cells. And overexpression of BAIAP2L2 obtained the contrary results. Mechanically, GSEA method and western blot results of key molecules in signaling pathways implicated that the depletion of BAIAP2L2 inactivated the vascular endothelial growth factors (VEGFs) and induced apoptosis signaling pathways in PCa cells.

CONCLUSIONS

Overall, these findings revealed that BAIAP2L2 may support tumorigenesis and malignant development of prostate cancer cells via VEGF and apoptosis signaling pathways, and it could be considered as a promising biomarker and independent prognostic predictor of prostate cancer.

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

Background

We constructed a predictive risk model of esophageal carcinoma (EC) for prognostic prediction.

Material/Methods

Immune genes and the expression data were downloaded from the ImmPort database and The Cancer Genome Atlas database. Univariate analysis, Lasso regression, and multivariate analysis were applied to screen the ultimately included prognostic immune genes for the model based on the training cohort. Survival analysis and receiver operating characteristic (ROC) curve were applied to evaluate the model. The model was further validated in the testing and entire cohorts, and the clinical utility of the model and its ability to assess the subtypes of EC were evaluated in the entire cohort.

Results

We detected 297 differentially expressed immune genes, including 241 upregulated genes and 56 downregulated genes in EC patients. Based on these genes, we developed a 7-immune gene model of EC, including HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. The area under the curve (AUC) of the model at 1 year was 0.825. Similarly, the AUC values for the validating cohorts were 0.813 and 0.816, respectively. Pathological stage and risk score of the model were independent prognostic factors. This model was effective for both subtypes of EC.

Conclusions

We constructed a 7-gene model consisting of HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. This risk model could be used for prognostic prediction of EC.