Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma

A protein-encoding CCDC7 circular RNA inhibits the progression of prostate cancer by up-regulating FLRT3

Circular RNAs (circRNAs) are a family of endogenous RNAs that have become a focus of biological research in recent years. Emerging evidence has revealed that circRNAs exert biological functions by acting as transcriptional regulators, microRNA sponges, and binding partners with RNA-binding proteins. However, few studies have identified coding circRNAs, which may lead to a hidden repertoire of proteins. In this study, we unexpectedly discovered a protein-encoding circular RNA circCCDC7(15,16,17,18,19) while we were searching for prostate cancer related chimeric RNAs. circCCDC7(15,16,17,18,19) is derived from exon 19 back spliced to exon 15 of the CCDC7 gene. It is significantly downregulated in patients with high Gleason score. Prostate cancer patients with decreased circCCDC7(15,16,17,18,19) expression have a worse prognosis, while linear CCDC7 had no such association. Overexpressed circCCDC7(15,16,17,18,19) inhibited prostate cancer cell migration, invasion, and viability, supporting classification of circCCDC7(15,16,17,18,19) as a bona fide tumor suppressor gene. We provide evidence that its tumor suppressive activity is driven by the protein it encodes, and that circCCDC7(15,16,17,18,19) encodes a secretory protein. Consistently, conditioned media from circCCDC7(15,16,17,18,19) overexpressing cells has the same tumor suppressive activity. We further demonstrate that the tumor suppressive activity of circCCDC7(15,16,17,18,19) is at least partially mediated by FLRT3, whose expression also negatively correlates with Gleason score and clinical prognosis. In conclusion, circCCDC7(15,16,17,18,19) functions as a tumor suppressor in prostate cancer cells through the circCCDC7-180aa secretory protein it encodes, and is a promising therapeutic peptide for prostate cancer.

MMP12 is a Potential Predictive and Prognostic Biomarker of Various Cancers Including Lung Adenocarcinoma

OBJECTIVE

This study sought to explore the clinical value of matrix metalloproteinases 12 (MMP12) in multiple cancers, including lung adenocarcinoma (LUAD).

METHODS

Using >10,000 samples, this retrospective study demonstrated the first pan-cancer analysis of MMP12. The expression of MMP12 between cancer groups and their control groups was analyzed using Wilcoxon rank-sum tests. The clinical significance of MMP12 expression in multiple cancers was assessed using receiver operating characteristic curves, Kaplan-Meier curves, and univariate Cox analysis. A further LUAD-related analysis based on 4565 multi-center and in-house samples was performed to verify the findings regarding MMP12 in pan-cancer analysis partly.

RESULTS

MMP12 mRNA is highly expressed in 13 cancers compared to their controls, and the MMP12 protein level is elevated in some of these cancers (e.g., colon adenocarcinoma) (P < .05). MMP12 expression makes it feasible to distinguish 21 cancer tissues from normal tissues (AUC = 0.86). A high MMP12 expression is a prognosis risk factor in eight cancers, such as adrenocortical carcinoma (hazard ratio >1, P < .05). The elevated MMP12 expression is also a prognosis protective factor in breast-invasive carcinoma and colon adenocarcinoma (hazard ratio <1, P < .05). Some pan-cancer findings regarding MMP12 are verified in LUAD-MMP12 expression is upregulated in LUAD at both the mRNA and protein levels (P < .05), has the potential to distinguish LUAD with considerable accuracy (AUC = .91), and plays a risk prognosis factor for patients with the disease (P < .05).

CONCLUSIONS

MMP12 is highly expressed in most cancers and may serve as a novel biomarker for the prediction and prognosis of numerous cancers.

Serum exosomal m6A demethylase FTO promotes gefitinib resistance in non-small cell lung cancer by up-regulating FLRT3, PTGIS and SIRPα expression

This study investigates the molecular mechanism of FTO m6A demethylase in non-small cell lung cancer (NSCLC) and gefitinib resistance using GEO and TCGA databases. Differentially expressed genes (DEGs) were screened from RNA-seq data sets of serum exosomes of gefitinib-resistant NSCLC patients in the GEO database and the NSCLC data set in the GEPIA2 database. From this analysis, FTO m6A demethylase was found to be significantly upregulated in the serum exosomes of gefitinib-resistant NSCLC patients. To identify downstream genes affected by FTO m6A demethylase, weighted correlation network analysis and differential expression analysis were performed, resulting in the identification of three key downstream genes (FLRT3, PTGIS, and SIRPA). Using these genes, the authors constructed a prognostic risk assessment model. Patients with high-risk scores exhibited a significantly worse prognosis. The model could predict the prognosis of NSCLC with high accuracy measured by AUC values of 0.588, 0.608, and 0.603 at 1, 3, and 5 years respectively. Furthermore, m6A sites were found in FLRT3, PTGIS, and SIRPA genes, and FTO was significantly positively correlated with the expression of these downstream genes. Overall, FTO m6A demethylase promotes gefitinib resistance in NSCLC patients by upregulating downstream FLRT3, PTGIS, and SIRPA expression, with these three downstream genes serving as strong prognostic indicators.

MMP12 serves as an immune cell-related marker of disease status and prognosis in lung squamous cell carcinoma

Background

Worldwide, lung squamous cell carcinoma (LUSC) has wreaked havoc on humanity. Matrix metallopeptidase 12 (MMP12) plays an essential role in a variety of cancers. This study aimed to reveal the expression, clinical significance, and potential molecular mechanisms of MMP12 in LUSC.

Methods

There were 2,738 messenger RNA (mRNA) samples from several multicenter databases used to detect MMP12 expression in LUSC, and 125 tissue samples were validated by immunohistochemistry (IHC) experiments. Receiver operator characteristic (ROC) curves, Kaplan-Meier curves, and univariate and multivariate Cox regression analyses were used to assess the clinical value of MMP12 in LUSC. The potential molecular mechanisms of MMP12 were explored by gene enrichment analysis and immune correlation analysis. Furthermore, single-cell sequencing was used to determine the distribution of MMP12 in multiple tumor microenvironment cells.

Results

MMP12 was significantly overexpressed at the mRNA level (p < 0.05, SMD = 3.13, 95% CI [2.51-3.75]), which was verified at the protein level (p < 0.001) by internal IHC experiments. MMP12 expression could be used to differentiate LUSC samples from normal samples, and overexpression of MMP12 itself implied a worse clinical prognosis and higher levels of immune cell infiltration in LUSC patients. MMP12 was involved in cancer development and progression through two immune-related signaling pathways. The high expression of MMP12 in LUSC might act as an antigen-presenting cell-associated tumor neoantigen and activate the body's immune response.

Conclusions

MMP12 expression is upregulated in LUSC and high expression of MMP12 serves as a risk factor for LUSC patients. MMP12 may be involved in cancer development by participating in immune-related signaling pathways and elevating the level of immune cell infiltration.

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

TGF-β-induced FLRT3 attenuation is essential for cancer-associated fibroblast-mediated epithelial-mesenchymal transition in colorectal cancer

Cancer-associated fibroblasts (CAFs) constitute a major component of the tumor microenvironment. The effects of CAFs on the progression of colorectal cancer (CRC) remain controversial. In this study, we found the ectopic overexpression of Fibronectin leucine-rich transmembrane protein 3 (FLRT3) inhibited the process of Epithelial-mesenchymal transition (EMT), as well as the proliferation, migration, invasion, and promote apoptosis of CRC cells, whereas silencing FLRT3 expression resulted in the opposite phenomenon. FLRT3 downregulation was associated with a poor prognosis in CRC. Also, FLRT3 expression was significantly related to some clinicopathological factors, including T stage (p=0.037), N stage (p=0.042), and E-cadherin (p=0.002) level. Via univariate and multivariate analyses, M stage (p<0.0001), FLRT3 (p=0.044), and E-cadherin (p=0.003) were associated with overall survival and were independent prognostic factors for it. Mechanistically, CAFs secreted TGF-β, which downregulated FLRT3 expression by activating SMAD4 to promote aggressive phenotypes in CRC cells. Moreover, FLRT3 repressed tumorigenesis and lung metastasis, which could be reversed by LY2109761, a dual inhibitor of TGF-β receptor type I and II. Treatment with LY2109761 increased IFN-γ expression in CD8+ T cells and reduced the number of regulatory T cells in the tumor microenvironment. Taken together, we revealed the metastasis-suppressive function of FLRT3, which was attenuated during the CAFs-mediated activation of the TGF-β/SMAD4 signaling pathway to promote EMT in CRC. LY2109761 that significantly inhibited metastasis could be a new treatment option for advanced CRC. Implications: CAFs enhance CRC aggressiveness by reducing FLRT3 expression through activating TGF-β/SMAD4 signaling pathway. CAFs-targeted therapy and/or LY2109761 were promising treatments for CRC.

Signatures of malignant cells and novel therapeutic targets revealed by single-cell sequencing in lung adenocarcinoma

Background

Single‐cell transcriptomics has been used to investigate various tumors to elucidate the molecular distinction of all cell type compositions of a complex mix.

Aims

This study aimed to investigate malignant‐cell‐specific genes to explore diagnostic and therapeutic biomarkers using single‐cell transcriptomic data of lung adenocarcinoma.

Materials & Methods

10X single‐cell RNA‐seq data of fourteen patients with lung adenocarcinoma were analyzed. Genes that expressed differentially and those with higher confidence to distinguish tumor cells from normal cells were picked out using the ROC curves. The LASSO regression method was used to select most markedly correlated genes to predict the malignancy of every single cell within a model. We also conducted further experiments to determine their roles in lung cancer in vitro.

Results

Twenty two thousand four hundred and ninety one tumor and 181 666 normal single cells were analyzed where 369 genes were found to be specifically expressed in single malignant cells. Seventy of them, encoding secreted or membrane‐bound proteins, showed involvement in cell‐to‐cell communications in tumor biology. KRT18 and the other six genes were identified as predictors to distinguish single malignant cells and were integrated to construct an accurate (96.1%) predicting model. Notably, IRX2, SPINK13, and CAPN8 outperformed the other four genes. Further experiments confirmed the upregulation of them in lung adenocarcinoma at both tissue and cell levels. Proliferative capacities of lung adenocarcinoma cells were attenuated by knocking‐down of either of them. However, targeting CAPN8, IRX2, or SPINK13 hardly exerted a cytotoxic effect on these cells.

Discussion

Apart from the current model, similar tools were still warranted using single‐cell RNA‐seq data of more types of tumors. The three genes identified as potential therapeutic targets in the present study still need to be validated in more in lung cancer.

Conclusion

Our model can aid the analyses of single‐cell sequencing data. CAPN8, IRX2, and SPINK13 may serve as novel targets of targeted and immune‐based therapies in lung adenocarcinoma.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes

Background

Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs).

Methods

The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.

Results

A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration.

Conclusions

We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01915-x.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data

PURPOSE

Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype.

METHODS

In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival.

RESULTS

Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9-10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like "ELANE" (LUSC) and "AHSG" (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10^-5] and LUAD [HR = 2.70, p value = 3.31 × 10^-7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both.

CONCLUSION

This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients.