Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

LINC01572 promotes the malignant progression of lung adenocarcinoma by modulating p53 mediated by miRNA-338-5p/TTK axis

OBJECTIVES

Lung cancer is one of the malignant tumors that threaten human health seriously. Long non-coding RNA (lncRNA) is an important factor affecting tumorigenesis and development. However, the mechanism of lncRNA in lung cancer progression remains to be further explored.

METHODS

In this study, the TCGA database was analyzed, and LINC01572 was found to be increased in lung adenocarcinoma (LUAD) tissues. Thereafter, with the help of databases including lncBase, TargetScan, and mirDIP, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, LINC01572/miRNA-338-5p/TTK regulatory axis and downstream p53 signaling pathway were excavated. qRT-PCR was adopted to detect levels of LINC01572, miRNA-338-5p, and TTK in LUAD cells. The role that LINC01572 played in LUAD cells was validated by CCK-8 assay, flow cytometry, colony formation, Transwell, and scratch healing assays. The binding ability between LINC01572/TTK and miRNA-338-5p was then verified by dual-luciferase and RIP analysis.

KEY FINDINGS

The results of this study demonstrated that LINC01572 was elevated in LUAD cells compared with normal cells. The overexpression of LINC01572 promoted the proliferative and migratory properties of LUAD cells but inhibited cell apoptosis. The inhibition of LINC01572 resulted in the opposite result. In addition, rescue experiments revealed that LINC01572, as a molecular sponge of miRNA-338-5p, targeted TTK to manipulate p53 for facilitating LUAD cell malignant progression. Apart from this, we constructed a mouse xenograft model and confirmed that the knockdown of LINC01572 hindered the growth of LUAD solid tumors in vivo.

CONCLUSIONS

Our findings illuminated the molecular mechanism of LINC01572 influencing LUAD and provided new insights for targeted therapy of LUAD cells.

Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods

BACKGROUND

Targeted therapy has revolutionized cancer treatment, greatly improving patient outcomes and quality of life. Lung cancer, specifically non-small cell lung cancer, is frequently driven by the G12C mutation at the KRAS locus. The development of KRAS inhibitors has been a breakthrough in the field of cancer research, given the crucial role of KRAS mutations in driving tumor growth and progression. However, over half of patients with cancer bypass inhibition show limited response to treatment. The mechanisms underlying tumor cell resistance to this treatment remain poorly understood.

METHODS

To address above gap in knowledge, we conducted a study aimed to elucidate the differences between tumor cells that respond positively to KRAS (G12C) inhibitor therapy and those that do not. Specifically, we analyzed single-cell gene expression profiles from KRAS G12C-mutant tumor cell models (H358, H2122, and SW1573) treated with KRAS G12C (ARS-1620) inhibitor, which contained 4297 cells that continued to proliferate under treatment and 3315 cells that became quiescent. Each cell was represented by the expression levels on 8687 genes. We then designed an innovative machine learning based framework, incorporating seven feature ranking algorithms and four classification algorithms to identify essential genes and establish quantitative rules.

RESULTS

Our analysis identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, that are known to be associated with the progression of multiple cancers.

CONCLUSION

Above genes were relevant to tumor cell resistance to targeted therapy. This study provides important insights into the molecular mechanisms underlying tumor cell resistance to KRAS inhibitor treatment.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

The potential bioactive ingredients and hub genes of five TCM prescriptions against lung adenocarcinoma were explored based on bioinformatics

Analysis of the commonness of several prescriptions of traditional Chinese medicine (TCM) in the treatment of lung adenocarcinoma (LUAD) based on bioinformatics. Searched the TCM prescriptions for the treatment of LUAD in the literature published in the database, searched ingredients in the TCM through TCMSP and Swiss target prediction databases (OB ≥ 30%, DL > 0.18, Caco-2 > 0), and predicted the potential targets. GEO database retrieved LUAD gene chip data and screened (P < 0.05, | log2 (fold change) |> 1). The biological function, hub gene selection and survival period, immune infiltration, methylation, copy number variations (CNVs), and single-nucleotide variants (SNV) of hub genes were analyzed by DAVID, STRING, Kaplan-Meier plotter database, Cytoscape software, GSCALite database, and TIMER2.0. In this study, 5 TCM prescriptions were analyzed, and a total of 173 ingredients were obtained through database search, including 35 coincidence ingredients, a total of 603 potential targets, 621 LUAD-related genes, 16 up-regulated genes, and 31 down-regulated genes. A total of 61 terms of biological process (BP), 14 terms of cellular component (CC), and 14 terms of molecular function (MF) were obtained. Twenty core genes were obtained, including 15 genes with different survival periods, which were closely related to immune cells (B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cells). The low expression of ADRB2 and MAOA and the high expression of AUARK, CDK1, KIF11, MIF, TOP2A, and TTK were associated with the survival rate of LUAD patients (P < 0.05). Baicalein, Arachidonate, Hederagenin, and hub genes may become potential drugs and potential targets for LUAD treatment. Evaluated the efficacy of TCM in the treatment of LUAD from macro to micro, mined the hub genes, and predicted the mechanism of action, so as to lay the foundation for the development of new drugs of TCM, prescription optimization, or disease control.

MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning

Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.

Integrated multi-omics analyses and functional validation reveal TTK as a novel EMT activator for endometrial cancer

BACKGROUND

Cancer-testis antigens (CTAs) are often expressed in tumor and testicular tissues but not in other normal tissues. To date, there has been no comprehensive study of the expression and clinical significance of CTA genes associated with endometrial cancer (EC) development. Additionally, the clinical relevance, biological role, and molecular mechanisms of the CTA gene TTK protein kinase (TTK) in EC are yet to be fully understood.

METHODS

Using bioinformatics methods, we comprehensively investigated the genomic, transcriptomic, and epigenetic changes associated with aberrant TTK overexpression in EC samples from the TCGA database. We further investigated the mechanisms of the lower survival associated with TTK dysregulation using single-cell data of EC samples from the GEO database. Cell functional assays were used to confirm the biological roles of TTK in EC cells.

RESULTS

We identified 80 CTA genes that were more abundant in EC than in normal tissues, and high expression of TTK was significantly linked with lower survival in EC patients. Furthermore, ROC analysis revealed that TTK could accurately distinguish stage I EC tissues from benign endometrial samples, suggesting that TTK has the potential to be a biomarker for early EC detection. We found TTK overexpression was more prevalent in EC patients with high-grade, advanced tumors, serous carcinoma, and TP53 alterations. Furthermore, in EC tissue, TTK expression showed a strong positive correlation with EMT-related genes. With single-cell transcriptome data, we identified a proliferative cell subpopulation with high expression of TTK and known epithelial-mesenchymal transition (EMT)-related genes and transcription factors. When proliferative cells were grouped according to TTK expression levels, the overexpressed genes in the TTK^high group were shown to be functionally involved in the control of chemoresistance. Utilizing shRNA to repress TTK expression in EC cells resulted in substantial decreases in cell proliferation, invasion, EMT, and chemoresistance. Further research identified microRNA-21 (miR-21) as a key downstream regulator of TTK-induced EMT and chemoresistance. Finally, the TTK inhibitor AZ3146 was effective in reducing EC cell growth and invasion and enhancing the apoptosis of EC cells generated by paclitaxel.

CONCLUSION

Our findings establish the clinical significance of TTK as a new biomarker for EC and an as-yet-unknown carcinogenic function. This present study proposes that the therapeutic targeting of TTK might provide a viable approach for the treatment of EC.

Ethnic and racial-specific differences in levels of centrosome-associated mitotic kinases, proliferative and epithelial-to-mesenchymal markers in breast cancers

Molecular epidemiology evidence indicates racial and ethnic differences in the aggressiveness and survival of breast cancer. Hispanics/Latinas (H/Ls) and non-Hispanic Black women (NHB) are at higher risk of breast cancer (BC)-related death relative to non-Hispanic white (NHW) women in part because they are diagnosed with hormone receptor-negative (HR) subtype and at higher stages. Since the cell cycle is one of the most commonly deregulated cellular processes in cancer, we propose that the mitotic kinases TTK (or Mps1), TBK1, and Nek2 could be novel targets to prevent breast cancer progression among NHBs and H/Ls. In this study, we calculated levels of TTK, p-TBK1, epithelial (E-cadherin), mesenchymal (Vimentin), and proliferation (Ki67) markers through immunohistochemical (IHC) staining of breast cancer tissue microarrays (TMAs) that includes samples from 6 regions in the Southeast of the United States and Puerto Rico -regions enriched with NHB and H/L breast cancer patients. IHC analysis showed that TTK, Ki67, and Vimentin were significantly expressed in triple-negative (TNBC) tumors relative to other subtypes, while E-cadherin showed decreased expression. TTK correlated with all of the clinical variables but p-TBK1 did not correlate with any of them. TCGA analysis revealed that the mRNA levels of multiple mitotic kinases, including TTK, Nek2, Plk1, Bub1, and Aurora kinases A and B, and transcription factors that are known to control the expression of these kinases (e.g. FoxM1 and E2F1-3) were upregulated in NHBs versus NHWs and correlated with higher aneuploidy indexes in NHB, suggesting that these mitotic kinases may be future novel targets for breast cancer treatment in NHB women.

Comprehensive analysis of the prognostic value and immune implications of the TTK gene in lung adenocarcinoma: a meta-analysis and bioinformatics analysis

Background

High expression levels of the TTK gene are closely related to tumor occurrence and poor prognosis, as confirmed by some studies. Our study explored the prognosis of lung adenocarcinoma (LUAD) patients with different TTK levels and the possible pathological mechanism of TTK in LUAD.

Methods

We extensively searched literature databases and high-throughput sequencing databases and included relevant literature or datasets in the meta-analysis according to the inclusion and exclusion criteria. Hazard ratios (HRs) and 95% confidence intervals (CIs) related to TTK expression were calculated, publication bias was assessed, and sensitivity tests were performed. We also compared the relationship between cancer immune infiltrating cells and tumor mutation burden (TMB) in patients with different TTK expression levels via bioinformatics analysis. The half maximal inhibitory concentration (IC50) of some chemotherapeutic and targeted therapy drugs were calculated. The potential biological functions or pathways associated with different TTK expression levels were determined by gene set enrichment analysis (GSEA).

Results

The meta-analysis revealed that higher TTK expression level was significantly associated with poor prognosis in LUAD patients, both in overall survival (OS) and progression-free survival (PFS). The expression level of TTK was significantly correlated with presence of some immune cells and TMB. Tumors with higher TTK expression levels were mostly enriched for the cell cycle, DNA replication and homologous recombination pathways. In addition, patients with different TTK expression levels were differently sensitive to some antitumor drugs.

Conclusion

TTK may be a promising prognostic biomarker for LUAD and is worthy of further investigation.

Network Pharmacology and Comparative Transcriptome Reveals Biotargets and Mechanisms of Curcumol Treating Lung Adenocarcinoma Patients With COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has led to 4,255,892 deaths worldwide. Although COVID-19 vaccines are available, mutant forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have reduced the effectiveness of vaccines. Patients with cancer are more vulnerable to COVID-19 than patients without cancer. Identification of new drugs to treat COVID-19 could reduce mortality rate, and traditional Chinese Medicine(TCM) has shown potential in COVID-19 treatment. In this study, we focused on lung adenocarcinoma (LUAD) patients with COVID-19. We aimed to investigate the use of curcumol, a TCM, to treat LUAD patients with COVID-19, using network pharmacology and systematic bioinformatics analysis. The results showed that LUAD and patients with COVID-19 share a cluster of common deregulated targets. The network pharmacology analysis identified seven core targets (namely, AURKA, CDK1, CCNB1, CCNB2, CCNE1, CCNE2, and TTK) of curcumol in patients with COVID-19 and LUAD. Clinicopathological analysis of these targets demonstrated that the expression of these targets is associated with poor patient survival rates. The bioinformatics analysis further highlighted the involvement of this target cluster in DNA damage response, chromosome stability, and pathogenesis of LUAD. More importantly, these targets influence cell-signaling associated with the Warburg effect, which supports SARS-CoV-2 replication and inflammatory response. Comparative transcriptomic analysis on in vitro LUAD cell further validated the effect of curcumol for treating LUAD through the control of cell cycle and DNA damage response. This study supports the earlier findings that curcumol is a potential treatment for patients with LUAD and COVID-19.

Hsp90 regulates the tumorigenic function of tyrosine protein kinase in osteosarcoma

Despite recent advances in diagnosis and treatment, osteosarcoma remains as the most common bone cancer in children and is associated with poor prognosis. Growing evidence has supported dysregulation of threonine and tyrosine protein kinase (TTK) expression as a hallmark of multiple cancers, however, its function in osteosarcoma remains to be elucidated. In the present study, we found that TTK was frequently overexpressed in osteosarcoma and associated with increased tumour growth and progression. Moreover, using both in vitro and in vivo assays, we provided evidence that TTK level was regulated by a molecular chaperone, heat shock protein 90 (Hsp90). Hsp90 directly interacted with TTK and prevents proteasome-dependent TTK degradation, leading to the accumulation of TTK in osteosarcoma cells. Elevated TTK promoted cancer cell proliferation and survival by activating cell-cycle progression and inhibiting apoptosis. Consistently, depletion of TTK by Hsp90 inhibition induced cell-cycle arrest, generated aneuploidy and eventually resulted in apoptotic cancer cell death. Together, our study revealed an important Hsp90-TTK regulatory axis in osteosarcoma cells to promote cancer cell growth and survival. These findings expand our knowledge on osteosarcoma pathogenesis and offer novel therapeutic options for clinical practice.

Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy

Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.