Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer

Integration analysis of cis- and trans-regulatory long non-coding RNAs associated with immune-related pathways in non-small cell lung cancer

Background

Long non-coding RNAs (lncRNAs) are importantly involved in the initiation and progression of non-small cell lung cancer (NSCLC). However, the classification and mechanisms of lncRNAs remain largely elusive.

Aim

Hence, we addressed this through bioinformatics analysis.

Methods and results

We utilized microarray technology to analyze lncRNAs and mRNAs in twenty paired NSCLC tumor tissues and adjacent normal tissues. Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were conducted to discern the biological functions of identified differentially expressed transcripts. Additionally, networks of lncRNA-mRNA co-expression, including cis-regulation, lncRNA-transcription factor (TF)-mRNA, trans-regulation, and lncRNA-miRNA-mRNA interactions were explored. Furthermore, the study examined differentially expressed transcripts and their prognostic values in a large RNA-seq dataset of 1016 NSCLC tumors and normal tissues extracted from the Cancer Genome Atlas (TCGA). The analysis revealed 391 lncRNAs and 344 mRNAs with differential expression in NSCLC tumor tissues compared to adjacent normal tissues. Subsequently, 43,557 co-expressed lncRNA-mRNA pairs were identified, including 27 lncRNA-mRNA pairs in cis, 9 lncRNA-TF-mRNA networks, 34 lncRNA-mRNA pairs in trans, and 8701 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks. Notably, these lncRNAs were found to be involved in immune-related pathways. Six significant transcripts, including NTF4, PTPRD-AS, ITGA11, HID1-AS1, RASGRF2-AS1, and TBX2-AS1, were identified within the ceRNA network and trans-regulation.

Conclusion

This study brings important insights into the regulatory roles of lncRNAs in NSCLC, providing a fresh perspective on lncRNA research in tumor biology.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment

This study aimed to screen differentially expressed genes (DEGs) involved in the influence of antiangiogenic therapy on myeloid-derived suppressor cell (MDSC) infiltration and investigate their mechanisms of action. Data on DEGs after the action of antiangiogenic drugs in a pan-cancer context were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R software. Single-sample gene set enrichment analysis was performed using the gene set variation analysis package to evaluate the levels of immune cells and the activity of immune-related pathways. The relationships of DEGs with the infiltration levels of MDSCs and specific immune cell subpopulations were investigated via gene module analysis. The top 10 key genes were subsequently obtained from PPI network analysis using the cytoHubba plugin of the Cytoscape platform. When the DEGs of the four datasets were intersected, a DEG in the intersection of three datasets and 12 DEGs in the intersection of two datasets were upregulated, and 28 DEGs in the intersection of two datasets were downregulated. GO and KEGG pathway enrichment analyses revealed that the DEGs were associated with multiple important signaling pathways closely related to tumor onset and development, including cell differentiation, cell proliferation, the cell cycle, and immune responses. Most downregulated genes in lung adenocarcinoma (LUAD) were positively correlated with MDSC expression. Only MGP was negatively correlated; the correlation between CACNG6 and MDSC expression was statistically insignificant. In lung squamous cell carcinoma (LUSC), the relationships of PMEPA1, PCDH7, NEURL1B, and CACNG6 with MDSC expression were statistically insignificant; MGP was negatively correlated with MDSC expression. The top 10 key genes with the highest degree scores obtained using the cytoHubba plugin of Cytoscape were AURKB, RRM2, BUB1, NUSAP1, PRC1, TOP2A, NCAPH, CENPA, KIF2C, and CCNA2. Most of these genes were upregulated in LUAD and associated with immune cell infiltration and prognosis in tumors. An analysis of the relationships between DEGs and infiltration by other specific immune cells revealed the presence of consistent patterns in the downregulated genes, which exhibited positive correlations with the levels of Th2 cells, γδ T cells, and CD56dim NK cells, and negative correlations with other infiltrating immune cells. Antiangiogenic therapy may regulate MDSC infiltration through multiple important signaling pathways closely associated with tumor onset and development, such as cell differentiation, cell proliferation, the cell cycle, and immune responses. Antiangiogenic drugs may exert effects by affecting various types of infiltrating cells associated with immune suppression.

KIF2C: An important factor involved in signaling pathways, immune infiltration, and DNA damage repair in tumorigenesis

BACKGROUNDS

Poorly regulated mitosis and chromosomal instability are common characteristics in malignant tumor cells. Kinesin family member 2 C (KIF2C), also known as mitotic centromere-associated kinesin (MCAK) is an essential component during mitotic regulation. In recent years, KIF2C was shown to be dysregulated in several tumors and was involved in many aspects of tumor self-regulation. Research on KIF2C may be a new direction and target for anti-tumor therapy.

OBJECT

The article aims at reviewing current literatures and summarizing the research status of KIF2C in malignant tumors as well as the oncogenic signaling pathways associated with KIF2C and its role in immune infiltration.

RESULT

In this review, we summarize the KIF2C mechanisms and signaling pathways in different malignant tumors, and briefly describe its involvement in pathways related to classical chemotherapeutic drug resistance, such as MEK/ERK, mTOR, Wnt/β-catenin, P53 and TGF-β1/Smad pathways. KIF2C upregulation was shown to promote tumor cell migration, invasion, chemotherapy resistance and inhibit DNA damage repair. It was also highly correlated with microRNAs, and CD4 +T cell and CD8 +T cell tumor immune infiltration.

CONCLUSION

This review shows that KIF2C may function as a new anticancer drug target with great potential for malignant tumor treatment and the mitigation of chemotherapy resistance.

Integrated bioinformatics analyses identifying key transcriptomes correlated with prognosis and immune infiltrations in lung squamous cell carcinoma

Background

Lung Squamous Cell Carcinoma (LUSC) is a major subtype of lung malignancies and is associated with the cause of cancer-mediated mortality worldwide. However, identification of transcriptomic signatures associated with survival-prognosis and immunity of tumor remains lacking.

Method

The GSE2088, GSE6044, GSE19188, GSE21933, GSE33479, GSE33532, and GSE74706 were integrated for identifying differentially expressed genes (DEGs) with combined effect sizes. Also, the TCGA LUSC cohort was used for further analysis. A series of bioinformatics methods were utilized for conducting the whole study.

Results

The 831 genes (such as DSG3, PKP1, DSC3, TPX2, and UBE2C) were found upregulated and the 731 genes (such as ABCA8, SELENBP1, FAM107A, and CACNA2D2) were downregulated in the LUSC. The functional enrichment analysis identifies the upregulated KEGG pathways, including cell cycle, DNA replication, base excision repair, proteasome, mismatch repair, and cellular senescence. Also, the key hub genes (such as EGFR, HRAS, JUN, CDH1, BRCA1, CASP3, RHOA, HDAC1, HIF1A, and CCNA2) were identified along with the eight gene modules that were significantly related to the protein-protein interaction (PPI). The clinical analyses identified that the overexpression group of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, LOX, and ITPA are substantially associated with a poor survival prognosis and the downregulated group of IL18R1 showed a similar trend. Moreover, our investigation demonstrated that the survival-associated genes were correlated with the stromal and immune scores in LUSC, indicating that the survival-associated genes regulate tumor immunity. The survival-associated genes were genetically altered in 27% of LUSC patients and showed excellent diagnostic efficiency. Finally, the consistent expression level of CDH3, PLAU, PKP3, STIL, CALU, LOXL2, POSTN, DPP3, GALNT2, and ITPA were found in the TCGA LUSC cohort.

Conclusions

The identification of key transcriptomic signatures can be elucidated by the crucial mechanism of LUSC carcinogenesis.

131I-Caerin 1.1 and 131I-Caerin 1.9 for the treatment of non-small-cell lung cancer

Objective

To investigate the effect of the ¹³¹I-labeled high-affinity peptides Caerin 1.1 and Caerin 1.9 for the treatment of A549 human NSCLC cells.

Methods

① 3-[4,5-Dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT) and plate clone formation assays were performed to confirm the in vitro anti-tumor activity of Caerin 1.1 and Caerin 1.9. ② Chloramine-T was used to label Caerin 1.1 and Caerin 1.9 with ¹³¹I, and the Cell Counting Kit 8 assay was performed to analyze the inhibitory effect of unlabeled Caerin 1.1, unlabeled Caerin 1.9, ¹³¹I-labeled Caerin 1.1, and ¹³¹I-labeled Caerin 1.9 on the proliferation of NSCLC cells. An A549 NSCLC nude mouse model was established to investigate the in vivo anti-tumor activity of unlabeled Caerin 1.1, unlabeled Caerin 1.9, ¹³¹I-labeled Caerin 1.1, and ¹³¹I-labeled Caerin 1.9.

Results

① Caerin 1.1 and Caerin 1.9 inhibited the proliferation of NSCLC cells in vitro in a concentration-dependent manner. The half-maximal inhibitory concentration was 16.26 µg/ml and 17.46 µg/ml, respectively, with no significant intergroup difference (P>0.05). ② ¹³¹I-labeled Caerin 1.1 and ¹³¹I-labeled Caerin 1.9 were equally effective and were superior to their unlabeled versions in their ability to inhibit the proliferation and growth of NSCLC cells (P>0.05).

Conclusions

¹³¹I-labeled Caerin 1.1 and ¹³¹I-labeled Caerin 1.9 inhibit the proliferation and growth of NSCLC cells and may become potential treatments for NSCLC.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.

Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.

Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.

Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

Analysis and verification of N6-methyladenosine-modified genes as novel biomarkers for clear cell renal cell carcinoma

N⁶-methyladenosine (m⁶A) has been involved in diverse biological processes in cancer, but its function and clinical value in clear cell renal cell carcinoma (ccRCC) remain largely unknown. In this study, we found that 1453 m⁶A-modified differentially expressed genes (DEGs) of ccRCC were mainly enriched in cell cycle, PI3K-AKT, and p53 signaling pathways. Then we constructed a co-expression network of the 1453 m⁶A-modified DEGs and identified a most clinically relevant module, where NUF2, CDCA3, CKAP2L, KIF14, and ASPM were hub genes. NUF2, CDCA3, and KIF14 could combine with a major RNA m⁶A methyltransferase METTL14, serving as biomarkers for ccRCC. Real-time quantitative PCR assay confirmed that NUF2, CDCA3, and KIF14 were highly expressed in ccRCC cell lines and ccRCC tissues. Furthermore, these three genes were modified by m⁶A and negatively regulated by METTL14. This study revealed that NUF2, CDCA3, and KIF14 were m⁶A-modified biomarkers, representing a potential diagnostic, prognostic, and therapeutic target for ccRCC.

Abbreviations: m⁶A: N⁶-methyladenosine; ccRCC: clear cell renal cell carcinoma; DEGs: differentially expressed genes; NUF2: NUF2 component of NDC80 kinetochore complex; CDCA3: cell division cycle associated 3; CKAP2L: cytoskeleton associated protein 2 like; KIF14: kinesin family member 14; ASPM: assembly factor for spindle microtubules; METTL14: methyltransferase 14; OS: overall survival; FPKM: fragments per kilobase million; GEO: gene expression omnibus; TCGA: the Cancer Genome Atlas; RMA: robust multi-array average expression measure; WGCNA: weighted gene co-expression network analysis; GO: gene ontology; KEGG: kyoto encyclopedia of genes and genomes; ROC: receiver operating characteristic curve; AUC: area under the curve; RIP: RNA immunoprecipitation; qPCR: real-time quantitative PCR.