Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis

Identification of prognosis-related lncRNAs and cell validation in lung squamous cell carcinoma based on TCGA data

Objective

To discern long non-coding RNAs (lncRNAs) with prognostic relevance in the context of lung squamous cell carcinoma (LUSC), we intend to predict target genes by leveraging The Cancer Genome Atlas (TCGA) repository. Subsequently, we aim to investigate the proliferative potential of critical lncRNAs within the LUSC milieu.

Methods

DESeq2 was employed to identify differentially expressed genes within the TCGA database. Following this, we utilized both univariate and multivariate Cox regression analyses to identify lncRNAs with prognostic relevance. Noteworthy lncRNAs were selected for validation in cell lines. The intracellular localization of these lncRNAs was ascertained through nucleocytoplasmic isolation experiments. Additionally, the impact of these lncRNAs on cellular proliferation, invasion, and migration capabilities was investigated using an Antisense oligonucleotides (ASO) knockdown system.

Results

Multivariate Cox regression identified a total of 12 candidate genes, consisting of seven downregulated lncRNAs (BRE-AS1, CCL15-CCL14, DNMBP-AS1, LINC00482, LOC100129034, MIR22HG, PRR26) and five upregulated lncRNAs (FAM83A-AS1, LINC00628, LINC00923, LINC01341, LOC100130691). The target genes associated with these lncRNAs exhibit significant enrichment within diverse biological pathways, including metabolic processes, cancer pathways, MAPK signaling, PI3K-Akt signaling, protein binding, cellular components, cellular transformation, and other functional categories. Furthermore, nucleocytoplasmic fractionation experiments demonstrated that LINC00923 and LINC01341 are predominantly localized within the cellular nucleus. Subsequent investigations utilizing CCK-8 assays and colony formation assays revealed that the knockdown of LINC00923 and LINC01341 effectively suppressed the proliferation of H226 and H1703 cells. Additionally, transwell assays showed that knockdown of LINC00923 and LINC01341 significantly attenuated the invasive and migratory capacities of H226 and H1703 cells.

Conclusion

This study has identified 12 candidate lncRNA associated with prognostic implications, among which LINC00923 and LINC01341 exhibit potential as markers for the prediction of LUSC outcomes.

Sputum Microbiome Composition in Patients with Squamous Cell Lung Carcinoma

Background: Recent findings indicate that the host microbiome can have a significant impact on the development of lung cancer by inducing an inflammatory response, causing dysbiosis, and generating genome damage. The aim of this study was to search for bacterial communities specifically associated with squamous cell carcinoma (LUSC). Methods: In this study, the taxonomic composition of the sputum microbiome of 40 men with untreated LUSC was compared with that of 40 healthy controls. Next-Generation sequencing of bacterial 16S rRNA genes was used to determine the taxonomic composition of the respiratory microbiome. Results: There were no differences in alpha diversity between the LUSC and control groups. Meanwhile, differences in the structure of bacterial communities (β diversity) among patients and controls differed significantly in sputum samples (pseudo-F = 1.53; p = 0.005). Genera of Streptococcus, Bacillus, Gemella, and Haemophilus were found to be significantly enriched in patients with LUSC compared to the control subjects, while 19 bacterial genera were significantly reduced, indicating a decrease in beta diversity in the microbiome of patients with LUSC. Conclusions: Among other candidates, Streptococcus (Streptococcus agalactiae) emerges as the most likely LUSC biomarker, but more research is needed to confirm this assumption.

Evaluation of the Prognostic Value of Long Noncoding RNAs in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is the most common type of lung cancer accounting for 40% to 51%. Long noncoding RNAs (lncRNAs) have been reported to play a significant role in the invasion, migration, and proliferation of lung cancer tissue cells. However, systematic identification of lncRNA signatures and evaluation of the prognostic value for LUSC are still an urgent problem. In this work, LUSC RNA-seq data were collected from TCGA database, and the limma R package was used to screen differentially expressed lncRNAs (DElncRNAs). In total, 216 DElncRNAs were identified between the LUSC and normal samples. lncRNAs associated with prognosis were calculated using univariate Cox regression analysis. The overall survival (OS) prognostic model containing 10 lncRNAs and the disease-free survival (DFS) prognostic model consisting of 11 lncRNAs were constructed using a machine learning-based algorithm, systematic LASSO-Cox regression analysis. We found that the survival rate of samples in the high-risk group was lower than that in the low-risk group. Results of ROC curves showed that both the OS and DFS risk score had better prognostic effects than the clinical characteristics, including age, stage, gender, and TNM. Two lncRNAs (LINC00519 and FAM83A-AS1) that were commonly identified as prognostic factors in both models could be further investigated for their clinical significance and therapeutic value. In conclusion, we constructed lncRNA prognostic models with considerable prognostic effect for both OS and DFS of LUSC.

[18F]AlF-NOTA-FAPI-04 PET/CT uptake in metastatic lesions on PET/CT imaging might distinguish different pathological types of lung cancer

Purpose

Heterogeneity is found in the tumor microenvironment among different pathological types of tumors. Radionuclide-labeled fibroblast-activation-protein inhibitor (FAPI), as an important tracer for non-invasive imaging of the tumor microenvironment, can be used to evaluate the expression of FAP in cancer-associated fibroblasts, macrophages, and tumor cells. Our aim was to explore the ability of [¹⁸F]AlF-NOTA-FAPI-04 positron emission tomography (PET)/computed tomography (CT) to distinguish different types of lung cancer by evaluating the uptake of this tracer in primary and metastatic lesions.

Methods

We prospectively enrolled 61 patients with histopathologically proven primary lung cancer with metastases. PET/CT scanning was performed before any antitumor therapy and 1 h after injection of 235.10 ± 3.89 MBq of [¹⁸F]AlF-NOTA-FAPI-04. Maximum standard uptake values (SUV_max) were calculated for comparison among primary and metastatic lesions. Immunohistochemical staining for FAP was performed on tumor specimens.

Results

Sixty-one patients with adenocarcinoma (ADC, n = 30), squamous cell carcinoma (SCC, n = 17), and small cell lung cancer (SCLC, n = 14) were enrolled in this study, and 61 primary tumors and 199 metastases were evaluated. No difference in [¹⁸F]AlF-NOTA-FAPI-04 uptake was observed among primary ADC, SCC, and SCLC tumors (P = 0.198). Additionally, no difference in uptake was found between primary and metastatic lesions of lung cancer with the same pathological type (P > 0.05). However, uptake did differ among metastases of differing pathological types (P < 0.001). The SUV_max of metastatic lymph nodes was highest for SCC, followed by ADC and then SCLC (P < 0.001). The SUV_max of bone metastases also was highest for SCC, followed by ADC and SCLC (P < 0.05), but no difference was observed between ADC and SCLC. The SUV_max of metastases in other organs was higher in SCC cases than in ADC cases but did not differ between SCC and SCLC or ADC and SCLC. Bone metastases exhibited higher uptake than those of lymph nodes and other organs in SCC and ADC (P < 0.05) but not in SCLC. Positive correlations were found between FAPI uptake and FAP expression in surgical plus biopsy specimens (r = 0.439, P = 0.012) and surgical specimens (r = 0.938, P = 0.005).

Conclusion

[¹⁸F]AlF-NOTA-FAPI-04 PET/CT imaging revealed differences in FAP expression in metastases of lung cancer, with the highest expression specifically in bone metastases, and thus, may be valuable for distinguishing different pathological types of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05638-z.

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics

Background

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. There are great geographical differences in the incidence of GC, and somatic mutation rates of driver genes are also different. The present study is aimed at screening core prognosis-related candidate genes in Chinese gastric cancer population based on integrated bioinformatics for the early diagnosis and prognosis of GC.

Methods

In the present study, the differentially expressed genes (DEGs) in GC were identified using four microarray datasets from the Gene Expression Omnibus (GEO) database. The samples of these datasets were all from China. Functional enrichment analysis of DEGs was conducted to evaluate the underlying molecular mechanisms involved in GC. Protein-protein interaction (PPI) network and cytoHubba were performed to determine hub genes associated with GC. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the hub genes.

Results

A total of 240 DEGs were obtained through the RRA method, including 80 upregulated genes and 160 downregulated genes. Upregulated genes were mainly enriched in extracellular matrix organization, extracellular matrix, and extracellular matrix structural constituent. The downregulated genes were mainly enriched in digestion, extracellular space, and oxidoreductase activity. The KEGG pathway enrichment analysis showed that the upregulated genes were mainly associated with ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. And downregulated genes were mainly associated with the metabolism of xenobiotics by cytochrome P450, metabolic pathways, and gastric acid secretion. The transcriptional and translational expression levels of the genes including COL1A1, COL5A2, COL12A1, and VCAN were higher in GC tissues than normal tissues.

Conclusion

A total of four genes including COL1A1, COL5A2, COL12A1, and VCAN were considered potential GC biomarkers in the Chinese population. And ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway were revealed to be important mechanisms of GC. Our findings provide novel insights into the occurrence and progression of GC in the Chinese population.

Integrative analysis of genomic alteration, immune cells infiltration and prognosis of lung squamous cell carcinoma (LUSC) to identify smoking-related biomarkers

Lung squamous cell carcinoma (LUSC) is the most common histologic type of smoking-related non-small cell lung cancer (NSCLC). However, there are no identified potential biomarkers for smoking-related LUSC diagnosis and prognosis. Especially, the characteristics of genetic alteration and tumor microenvironment induced by cigarette smoking remain unknown. Here, we performed integrative analysis of 463 LUSC with smoking history information from The Cancer Genome Atlas (TCGA). Non-smokers had the best prognosis, and current reformed smokers had better overall survival (OS) than current smokers in all and stage I-II cohort. Then, pathway enrichment analysis might suggest that smoking may play a role in regulating tumor metabolism and invasion and metastasis via those pathways. We constructed an eight-gene signature and identified WNT7A, Solute carrier-7A5 (SLC7A5) and Brain‑type glycogen phosphorylase (PYGB), which may be served as biomarkers related to the smoking. Notably, the single copy deletion of WNT7A and SLC17A5 and the low-level amplification of PYGB may be related to the epigenetic mechanism of smoking on tumorigenesis. We also estimated the relative proportion of 24 immune cell subtypes within tumor microenvironment in different smoking status. Interestingly, we found NK cells activated, NK cells resting and endothelial cells might play an important role in immunologic dysfunction and harmful tumor microenvironment induced by cigarette smoking. Our research has identified potential biomarkers for smoking-related LUSC diagnosis and prognosis, which would help to further understand the pathogenesis of LUSC.

Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

Transcription factors encoded by Homeobox (HOX) genes play numerous key functions during early embryonic development and differentiation. Multiple reports have shown that mis-regulation of HOX gene expression plays key roles in the development of cancers. Their expression levels in cancers tend to differ based on tissue and tumor type. Here, we performed a comprehensive analysis comparing HOX gene expression in different cancer types, obtained from The Cancer Genome Atlas (TCGA), with matched healthy tissues, obtained from Genotype-Tissue Expression (GTEx). We identified and quantified differential expression patterns that confirmed previously identified expression changes and highlighted new differential expression signatures. We discovered differential expression patterns that are in line with patient survival data. This comprehensive and quantitative analysis provides a global picture of HOX genes' differential expression patterns in different cancer types.