Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis

Identification of RFC4 as a potential biomarker for pan-cancer involving prognosis, tumour immune microenvironment and drugs

RFC4 is required for DNA polymerase δ and DNA polymerase ε to initiate DNA template expansion. Downregulated RFC4 inhibits tumour proliferation by causing S-phase arrest and inhibiting mitosis, resulting in the reduction of tumour cells. RFC4 has been implicated that it plays an important role in the initiation and progression of cancers, but a comprehensive analysis of the role of RFC4 in cancer has not been performed. We comprehensively analysed the expression, prognosis, methylation level, splicing level, relationship of RFC4 and immune infiltration, and pan-cancer immunotherapy response used various databases (including TCGA, GTEx, UALCAN, Oncosplicing, TIDE, TISCH, HPA and CAMOIP), and experimented its biological function in HCC. Through pan-cancer analysis, we found that RFC4 is significantly upregulated in most tumours. The tumour patients with high expression of RFC4 have poor prognosis. The methylation level and variable splicing level of RFC4 were abnormal in most tumours compared with the adjacent tissues. Furthermore, RFC4 was closely associated with immune cell infiltration in various cancers. RFC4 was significantly co-expressed with immune checkpoints and other immune-related genes. The expression of RFC4 could indicate the immunotherapy efficacy of some tumours. The RFC4 expression was associated with sensitivity to specific small molecule drugs. Cell experiments have shown that downregulated RFC4 can inhibit cell cycle and tumour cell proliferation. We conducted a systematic pan-cancer analysis of RFC4, and the results showed that RFC4 can serve as a biomarker for cancer diagnosis and prognosis. These findings open new perspectives for precision medicine.

Analysis of Nucleoporin 107 Overexpression and Its Association with Prognosis and Immune Infiltration in Lung Adenocarcinoma by Bioinformatics Methods

Background

Lung adenocarcinoma (LUAD) has high morbidity and mortality. Current studies indicate nucleoporin 107 (NUP107) is involved in the construction of nuclear pore complex, and NUP107 overexpression contributes to the growth and development in most types of cancers, but its effect in LUAD has not been elucidated.

Methods

Differences in NUP107 expression were investigated using the Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) data sets. Enrichment analysis were implemented to probe the NUP107 function. The association of NUP107 with the degree of immune cell infiltration was investigated by the TIMER database, single-sample gene set enrichment analysis (ssGSEA), and ESTIMATE. The association of NUP107 expression with tumor mutation burden (TMB), TP53, and immune checkpoint was analyzed. Single-cell RNA sequencing data were used to detect NUP107 expression in different cell clusters. Finally, we performed real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) to prove the difference of NUP107 expression.

Results

NUP107 was overexpressed in LUAD and mainly expressed in cancer stem cell (CSC). Overexpression of NUP107 in LUAD suggested a poorer prognosis. Functional enrichment analysis pointed out that NUP107 was mainly linked to the regulation of cell cycle. Both immune cell infiltration and TMB were found to be in connection with NUP107. Cases in the group with high NUP107 expression had poorer immune infiltration, but had higher expression of immune checkpoints, TMB, and proportion of TP53 mutations.

Conclusion

NUP107 is a sensitive diagnostic and prognostic factor for LUAD and may be involved in tumor progression through its effects on cell cycle and immune infiltration.

Systematic analysis of the necroptosis index in pan-cancer and classification in discriminating the prognosis and immunotherapy responses of 1716 glioma patients

Necroptosis is a programmed form of necrotic cell death that serves as a host gatekeeper for defense against invasion by certain pathogens. Previous studies have uncovered the essential role of necroptosis in tumor progression and implied the potential for novel therapies targeting necroptosis. However, no comprehensive analysis of multi-omics data has been conducted to better understand the relationship between necroptosis and tumor. We developed the necroptosis index (NI) to uncover the effect of necroptosis in most cancers. NI not only correlated with clinical characteristics of multiple tumors, but also could influence drug sensitivity in glioma. Based on necroptosis-related differentially expressed genes, the consensus clustering was used to classify glioma patients into two NI subgroups. Then, we revealed NI subgroup I were more sensitive to immunotherapy, particularly anti-PD1 therapy. This new NI-based classification may have prospective predictive factors for prognosis and guide physicians in prioritizing immunotherapy for potential responders.

SNRPD1 inhibition suppresses the proliferation of hepatocellular carcinoma and promotes autophagy through the PI3K/AKT/mTOR/4EBP1 pathway

BACKGROUND

Small nuclear ribonucleoprotein Sm D1 (SNRPD1) has been reported as an oncogene in some solid cancers. Our previous study suggested that SNRPD1 has diagnostic and prognostic value in hepatocellular carcinoma (HCC), but its role in tumor growth and biological behavior remains unknown. In this study, we aimed to unravel the role and mechanism of SNRPD1 in HCC.

METHODS

We investigated the SNRPD1 mRNA level in adjacent normal liver tissues and HCC tissues with different tumor stages in the UALCAN database. The associations between SNRPD1 mRNA expression and HCC prognosis were investigated in TCGA database. Then, 52 pairs of frozen HCC tissues and corresponding adjacent normal liver tissues were collected to perform qPCR and immunohistochemistry assay. Next, we carried out a series of experiments in vitro and in vivo to investigate the effects of SNRPD1 expression on cell invasion, migration, proliferation, autophagy, and the PI3K/AKT/mTOR pathway.

RESULTS

The bioinformatics analysis and qPCR in our patient cohort demonstrated that the SNRPD1 mRNA level in HCC tissues was higher than in adjacent normal tissues. In addition, the immunohistochemistry assay exhibited an increased SNRPD1 protein level with the tumor stage increase. Survival analysis suggested that higher expression of SNRPD1 was significantly associated with unfavorable prognosis of patients with HCC. The functional experiments in vitro indicated that SNRPD1 knockdown suppressed the cellular proliferation, migration, and invasion capacities. Furthermore, SNRPD1 inhibition induced cellular apoptosis and arrested the HCC cells at the G0/G1 phase of the cell cycle. Mechanistic analyses demonstrated that SNRPD1 knockdown induced the increase of autophagic vacuoles and the expression of autophagy-related genes (ATG5, ATG7, and ATG12) and blocked the PI3K/AKT/mTOR/4EBP1 pathway in vitro. Moreover, SNRPD1 inhibition suppressed tumor growth and expression of the Ki67 protein in vivo.

CONCLUSIONS

SNRPD1 may serve as an oncogene in HCC and promote tumor proliferation via inhibiting autophagy induced through the PI3K/Akt/mTOR/4EBP1 pathway.

A genomic instability-related lncRNA model for predicting prognosis and immune checkpoint inhibitor efficacy in breast cancer

Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan–Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.

Effect of CDCA5 on Proliferation and Metastasis of Triple Negative Breast Cancer Cells under shRNA Interference Technology

Objective

It was to explore the effect of cell division cycle associated 5 (CDCA5) under shRNA interference on proliferation and metastasis of triple negative breast cancer (TNBC) cells.

Methods

MDA-ME-231 and BT549 cells were selected as the research objects. According to the different interference methods and CDCA5 interference sequences, they were divided into the interference group 1_MDA-ME-231, the interference group 2_MDA-ME-231, the interference group 1_BT549, the interference group 2_BT549 (using shRNA technology), the control group _MDA-ME-231, and the control group _BT549 (breast cancer cells under normal culture conditions). MCF10A cells were routinely cultured as the negative control group to analyze the effect of CDCA5 expression on the proliferation and migration of cancer cells.

Results

The expression of CDCA5 protein in _MDA-ME-231 and _BT549 cells in control group was significantly higher than that in negative control group (P < 0.05). Compared with the control group, the inhibition rates of CDCA5 expression in 1_MDA-ME-231, 2_MDA-ME-231, 1_BT549, and 2_BT549 cells in the interference group were 39.01%, 42.98%, 49.57%, and 60.98%, respectively (P < 0.05). From 12 h, the proliferation level of TNBC cells at different culture time was lower than that of the control group (P < 0.05). Compared with the number of staining cells in the control group, the positive staining cells in 1_MDA-ME-231 (61.42%), 2_MDA-ME-231 (72.06%), 1_BT549 (52.53%), and 2_BT549 (59.65%) in the interference group were significantly decreased (P < 0.05).

Conclusion

The results show that the expression of CDCA5 in TNBC is increased, which plays an important role in the proliferation and migration of cancer cells. shRNA interference technology can knock down the expression of CDCA5 and inhibit its “promoting cancer” effect.

Constructing a Prognostic Gene Signature for Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network Analysis and Single-Cell Analysis

Purpose

Lung adenocarcinoma (LUAD) has a high degree of intratumor heterogeneity. Advanced single-cell RNA sequencing (scRNA-seq) technologies have offered tools to analyze intratumor heterogeneity, which improves the accuracy of identifying biomarkers based on single-cell expression data, and thus helps in predicting prognosis of cancer patients and assisting decision-makings for cancer treatment.

Patients and Methods

ScRNA-seq data containing two LUAD and two para-cancerous tissue samples were included to identify different cell clusters in tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, LUAD molecular subtypes were constructed by unsupervised consensus clustering based on genes in key modules. Using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model of LUAD was established.

Results

A total of 14 cell clusters belonging to 10 cell types in LUAD were identified. The turquoise module was the most relevant to LUAD among all the modules; cluster 10 (C10, lung epithelial cells) was found to be the most strongly associated with the turquoise module. LUAD samples were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD.

Conclusion

Fourteen cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. Two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients.

Multiomics Data Analysis and Identification of Immune-Related Prognostic Signatures With Potential Implications in Prognosis and Immune Checkpoint Blockade Therapy of Glioblastoma

Background

In recent years, glioblastoma multiforme (GBM) has been a concern of many researchers, as it is one of the main drivers of cancer-related deaths worldwide. GBM in general usually does not responding well to immunotherapy due to its unique microenvironment.

Methods

To uncover any further informative immune-related prognostic signatures, we explored the immune-related distinction in the genetic or epigenetic features of the three types (expression profile, somatic mutation, and DNA methylation). Twenty eight immune-related hub genes were identified by Weighted Gene Co-Expression Network Analysis (WGCNA). The findings showed that three genes (IL1R1, TNFSF12, and VDR) were identified to construct an immune-related prognostic model (IRPM) by lasso regression. Then, we used three hub genes to construct an IRPM for GBM and clarify the immunity, mutation, and methylation characteristics.

Results

Survival analysis of patients undergoing anti-program cell death protein 1 (anti-PD-1) therapy showed that overall survival was superior in the low-risk group than in the high-risk group. The high-risk group had an association with epithelial-mesenchymal transition (EMT), high immune cell infiltration, immune activation, a low mutation number, and high methylation, while the low-risk group was adverse status.

Conclusions

In conclusion, IRPM is a promising tool to distinguish the prognosis of patients and molecular and immune characteristics in GBM, and the IRPM risk score can be used to predict patient sensitivity to checkpoint inhibitor blockade therapy. Thus, three immune-related signatures will guide us in improving treatment strategies and developing objective diagnostic tools.

Comprehensive analysis of expression and prognosis for LMNB family genes in human sarcoma

ABSTRACT

Previous studies indicated that lamin proteins were thought to be related to gene expression, chromatin structure, and unclear stability. There are 2 types of vertebrate lamins, including A and B. The 2 B type proteins are encoded by lamin B1 (LMNB1) and lamin B2 (LMNB2). The LMNBs factor has been found to be associated with the development of multiple tumors, but its association with sarcoma has been barely mentioned.The transcription levels of LMNBs were analyzed via Oncomine database. Gene Expression Profiling Interactive Analysis (GEPIA) dataset was adopted to analyze the differential expression of LMNBs in sarcoma. Cancer Cell Line Encyclopedia dataset was used to explore the expression of LMNBs in sarcoma cell line. We analyzed the prognostic value of LMNBs in GEPIA and Kaplan-Meier Plotter. Oncomine and GEPIA datasets were also used to detect the relationship between LMNBs and their co-expressed genes. We used the Database for Annotation, Visualization and Integrated Discovery to conduct the Gene Ontology analysis of LMNBs and their co-expressed genes. Kyoto Encyclopedia of Genes and Genomes was also used to analyze the pathway of LMNBs.LMNB1 and LMNB2 were reported to be hyperexpressed in sarcoma. The expression of LMNBs was elevated in various sarcoma cell lines. According to the results, we observed that LMNBs were connected to the poor overall survival, recurrence-free survival, and disease-free survival of sarcoma patients.This study indicated that hyperexpression of LMNBs was significantly related to worse outcome of sarcoma, LMNB1 and LMNB2 were expected to become potential biomarkers for human.

Insights into the Association Between QSER1 and M2 Macrophages and Remarkable Malignancy Characteristics in Hepatocellular Carcinoma

Purpose

Glutamine and serine rich 1 (QSER1), as a DNA methylation modulator, play a crucial role in transforming tumor cells. Previous studies have shown that QSER1 plays a role in regulating the progression of various malignancies and that QSER1 dysfunction is connected with precancerous lesions of hepatocellular carcinoma (HCC) as well as HCC prognosis. However, little is known about the detailed contribution of QSER1 in HCC.

Patients and Methods

Various statistical methods such as Kaplan–Meier method, AUC analysis, GSEA, and immune-infiltration analysis were used to evaluate the relationship between QSER1 expression and clinical features, prognostic factors, and potential functional mechanisms of QSER1.

Results

QSER1 expression was negatively correlated with clinicopathological features (clinical stage, pathological grade, TP53 mutation, lymph node metastasis) and clinical outcome (overall survival versus recurrence). Functional enrichment analysis further suggested that QSER1 is involved in multiple pathways related to DNA replication and tumor immunity. TIMER analysis indicated that high QSER1 expression was significantly associated with higher macrophage infiltration and poorer macrophage-related outcomes. In particular, QSER1 was significantly more associated with M2 macrophages than M1 macrophages.

Conclusion

Overall, elevated QSER1 is a potential prognostic marker for HCC and is associated with immune infiltration in HCC.

Identification of Novel Prognostic Markers Associated With Laryngeal Squamous Cell Carcinoma Using Comprehensive Analysis

Background

Laryngeal squamous cell carcinoma (LSCC) is a leading malignant cancer of the head and neck. Patients with LSCC, in which the cancer has infiltrated and metastasized, have a poor prognosis. Therefore, there is an urgent need to identify more potential targets for drugs and biomarkers for early diagnosis.

Methods

RNA sequence data from LSCC and patients’ clinical traits were obtained from the Gene Expression Omnibus (GEO) (GSE142083) and The Cancer Genome Atlas (TCGA) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, prognostic value analysis, receiver operating characteristic (ROC) curve analysis, gene mutation analysis, tumor-infiltrating immune cell abundance profile estimation, gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) were performed. Single-gene RNA sequencing data were obtained from the GSE150321 dataset. Cell proliferation and viability were confirmed by the CCK-8 assay and real-time PCR.

Results

A total of 701 DEGs, including 329 upregulated and 372 downregulated genes, were screened in the GSE142083 dataset. Using WGCNA, three modules were identified to be closely related to LSCC. After intersecting the DEGs and performing univariate and multivariate Cox analyses, a novel prognostic model based on three genes (SLC35C1, HOXB7, and TEDC2) for LSCC was established. Interfering TEDC2 expression inhibited tumor cell proliferation and migration.

Conclusions

Our results show that SLC35C1, HOXB7, and TEDC2 have the potential to become new therapeutic targets and prognostic biomarkers for LSCC.

The diagnostic and prognostic significance of small nuclear ribonucleoprotein Sm D1 aberrantly high expression in hepatocellular carcinoma

Small nuclear ribonucleoprotein Sm D1 (SNRPD1), one of the crucial genes encoding core spliceosome components, was abnormally highly expressed in multiple types of tumors. In this study, we investigated the diagnostic and prognostic significance of SNRPD1 in hepatocellular carcinoma (HCC). The investigation of datasets from GEO and TCGA databases revealed that SNRPD1 expression in HCC was significantly higher than adjacent normal liver tissues, which was validated by immunohistochemistry (IHC). Both GO, KEGG analysis showed that the SNRPD1 co-expressed genes mainly enriched in Cell division, Nuclear import, mRNA splicing via spliceosome, Ribosome, Cell cycle, etc. Survival analysis from the GSE14520 dataset and 154 HCC cohorts exhibited a significant association of high SNRPD1 expression with poor overall survival and recurrence-free survival. ROC analysis showed that the abnormally high SNRPD1 mRNA expression has diagnostic significance in distinguishing between HCC and normal liver tissue (AUC = 0.819). Gene set enrichment analysis (GSEA) demonstrated that the high expression of SNRPD1 might regulate HCC tumorigenesis and progression by affecting the cell cycle, mismatch repair, DNA replication, and RNA degradation, etc. The luciferase report assay revealed that SNRPD1 was the direct target gene of miR-100 manifested by decreased SNRPD1 expression and luciferase activity in the HCC cells upon miR-100 overexpression. Finally, SNRPD1 may as an oncogene affecting the progression of HCC through regulates the mTOR pathway and autophagy.

Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma

Objective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes. Results: The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, TEK, HGF, SPP1, and ENG. Genes that have an impact on survival included PECAM1, HGF, SPP1, and ENG. The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively. Conclusion: Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD.

A pan-cancer analysis based on weighted gene co-expression network analysis identifies the biomarker utility of lamin B1 in human tumors

Emerging evidence has revealed a relationship between lamin B1 (LMNB1) and several cancers such as cervical cancer, liver cancer, and prostate cancer. But no systematic pan-cancer analysis is available. Little is known about the clinical significance and biomarker utility of LMNB1. In this study, we first revealed the key role of LMNB1 in esophageal carcinoma (ESCA) through weighted gene co-expression network analysis (WGCNA) and disease-free survival (DFS) analysis. Based on this result and the datasets of the cancer genome atlas (TCGA), we explored the biomarker utility of LMNB1 across thirty-three tumors. We found that LMNB1 was highly expressed in most of the cancers and significant associations existed between LMNB1 expression and prognosis of cases of nearly half of the cancers. We also found that LMNB1 expression was associated with the infiltration level of Macrophages M1 and T cells CD4 memory activated in some cancers. Moreover, LMNB1 was mainly involved in the functional mechanisms of MRNA binding, olfactory transduction, and gene silencing. Our study first provides a pan-cancer study of LMNB1, thereby offering a relatively comprehensive understanding of the biomarker utility of LMNB1 across thirty-three tumors.

Impact of KMN network genes on progression and prognosis of non-small cell lung cancer

The Knl1-Mis12-Ndc80 (KMN) network genes (including KNL, MIS12 and NDC80 complexes) encode a highly conserved network of protein complexes that act in cell mitosis. In recent years, multiple studies revealed that KMN network genes also play a vital role in tumor appearance and growth. However, the role of the KMN gene network in non-small cell lung cancer (NSCLC) remains unknown. In this study, we analyzed the effects of KMN genes expression and clinical phenotype in patients with lung adenocarcinoma (LUAD). The expression of KMN network genes and related clinical information was extracted from The Cancer Genome Atlas. The samples were classified into cluster I and II by consistent clustering. We analyzed the gene distribution by principal component analysis, and the potential risk characteristics were analyzed using the least absolute shrinkage and selection operator Cox regression algorithm. Univariate and multivariate Cox regression analyses were used to analyze the clinical information. The Database for Annotation, Visualization, and Integrated Discovery, Gene MANIA and gene set enrichment analysis were used to analyze function and correlation among genes of the KMN network. The expression levels of nine out of ten KMN genes were significantly up-regulated in LUAD and were associated with poor overall survival (OS). Higher expression of NDC80 and KNL1 was related to low OS in both univariate and multivariate analyses. According to two independent prognostic KMN network genes (KNL1 and NDC80), a risk signature was established to predict the prognosis of patients with LUAD. Additionally, the genes NDC80 and KNL1 were considerably enriched in pathways associated with signaling pathways, biological processes, and the cell cycle. The results indicate that KMN network genes are intimately related to lung adenocarcinoma. KMN network genes are involved in the malignant process of LUAD. Assessment of NDC80 and KNL1 might be helpful for prognostic stratification and treatment strategy development.

Hypoxia-Induced ZWINT Mediates Pancreatic Cancer Proliferation by Interacting With p53/p21

p53/p21 signaling plays a vital role in pancreatic cancer (PC) progression. ZWINT was shown to function as an oncoprotein in the progression of multiple cancers. However, the involvement of ZWINT and p53 activation in the progression of PC remains poorly understood. Bioinformatics and tissue array chip analyses were performed to evaluate ZWINT expression in pancreatic cancer. ZWINT mRNA and protein expression were evaluated in normoxia and hypoxia. CHIP was used to evaluate HIF1α interaction with the ZWINT promoter. CCK8, colony formation, EDU, and cell cycle analysis were used to examine PC cell proliferation. Immunoprecipitation and immunofluorescence were used to examine the interaction of ZWINT, MDM2, and p53. p53 activity was evaluated by q-PCR and luciferase assay. Protein degradation and ubiquitination assays were used to analyze the role of ZWINT in p53 ubiquitination. ZWINT was overexpressed in pancreatic cancer and induced in hypoxia. ZWINT promoted pancreatic cancer growth and cell cycle progression. Bioinformatic analysis revealed that ZWINT may regulate the p53 signal pathway. ZWINT interacts with p53 and promotes its ubiquitination and degradation. ZWINT promoted proliferation via p53/p21. Immunohistochemistry of clinical specimens revealed that that ZWINT expression was significantly negatively correlated with p53/p21. Our data showed that hypoxia regulates the expression of ZWINT, which activated p53/p21 signaling pathway to promote PC growth.

High expression of Ran binding protein 1 predicts poor outcomes in hepatocellular carcinoma patients: a Cancer Genome Atlas database analysis

BACKGROUND

Ran-specific binding protein 1 (RANBP1) is involved in the regulation of the cell cycle, while its role in hepatocellular carcinoma (HCC) is unknown. Therefore, we aimed to demonstrate the association of RANBP1 with clinicopathologic features and potential biological functions in HCC based on The Cancer Genome Atlas (TCGA) data.

METHODS

We assessed RANBP1 expression and its correlation with clinicopathologic features and evaluated the prognostic value of RANBP1 with Kaplan-Meier survival analysis and the MethSurv database. Univariate and multivariate Cox regression analyses were conducted to elucidate the factors responsible for prognosis. The identification of a co-expression network and the analysis of related biological events with RANBP1 in HCC were assessed using LinkedOmics. Moreover, gene set enrichment analysis (GSEA) was employed to annotate the biological function of RANBP1. We also explored the correlation between RANBP1 and tumor immune infiltrates using a single sample GSEA (ssGSEA).

RESULTS

The expression of RANBP1 was found significantly elevated in HCC and linked to advanced T stage and histopathological grade. Up-regulated RANBP1 expression was linked to poor prognosis. High DNA methylation levels of RANBP1 were significantly linked to very poor overall survival (OS). Co-expression network analysis revealed that RANBP1 was involved in ribosome, spliceosome, deoxyribonucleic acid (DNA) replication, ribonucleic acid (RNA) transport, and cell cycle. GSEA showed enrichment of G2M-checkpoint, Wingless and Int-1 (Wnt) cell signaling, and DNA repair in the RANBP1 high-expression phenotype. By using ssGSEA analysis, the increased RANBP1 expression was positively linked to the immune infiltration level of T helper cell type-1 (Th1) and negatively linked to the immune infiltration levels of T helper cell type-17 (Th17).

CONCLUSIONS

Findings suggest that RANBP1 may play a pivotal role in HCC prognosis and can potentially serve as a candidate biosignature and as a therapeutic target for HCC.

19-Hydroxybufalin inhibits non-small cell lung cancer cell proliferation and promotes cell apoptosis via the Wnt/β-catenin pathway

Background

Bufadienolides derived from the skin of toads are often regarded as the main active components with antitumor effects. 19-Hydroxybufalin (19-HB) is a monomer of bufadienolides; however, its effects and underlying molecular mechanisms on tumor growth remain to be ascertained. In this report, we focused on the antitumor effects of 19-HB on non-small cell lung cancer to provide a scientific basis for its further development and utilization.

Methods

The antitumor effects of 19-HB on the human NSCLC cell lines NCI-H1299 and NCI-H838 were examined in vitro. The cells were treated with different concentrations of 19-HB, and the inhibition of cell growth was measured by CCK-8 and colony formation assays. Furthermore, cell apoptosis was analyzed by flow cytometry, TUNEL staining, JC-1 staining, and western blotting. The effects on migration and invasion were evaluated by wound-healing assay, transwell assay, and western blotting. Finally, the antitumor effects of 19-HB were evaluated in vivo using a xenograft mouse model.

Results

19-HB-treated NSCLC cells showed inhibited cell viability and increased apoptosis. The expression levels of cleaved caspase-3, cleaved-PARP, and Bax/Bcl-2 were upregulated, while the mitochondrial membrane potential decreased. In contrast, migration, invasion, as well as the expression of MMP2, MMP7, MMP9, the epithelial–mesenchymal transition-related proteins N-cadherin and Vimentin, and the transcription factors Snail and Slug were inhibited. Furthermore, the expression levels of the key molecules in the Wnt/β-catenin signaling pathway (CyclinD1, c-Myc, and β-catenin) were decreased. In vivo, the growth of xenograft tumors in nude mice was also significantly inhibited by 19-HB, and there were no significant changes in biochemical indicators of hepatic and renal function.

Conclusions

19-HB inhibited the proliferation, migration, and invasion, and promoted the apoptosis of NSCLC cells via the Wnt/β-catenin pathway. In addition, 19-HB inhibited the growth of xenograft tumors in nude mice with little toxicity to the liver and kidney. Thus, 19-HB may be a potential antitumor agent for treating NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40164-021-00243-0.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Weighted gene co-expression network analysis of hub genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a tumor with high incidence. This study aimed to identify the central genes of LUAD. LUAD were analyzed by weighted gene co-expression network (WGCNA), and differentially expressed genes (DEGs) were identified. Samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases and included 515 LUAD samples and 347 normal samples. The WGCNA algorithm generated a total of 10 modules. The top 2 modules (MEturquoise and MEblue) with the highest correlation to LUAD were selected. Ten Hub genes (IL6, CDH1, PECAM1, SPP1, THBS1, HGF, SNCA, CDH5, CAV1, and DLC1) were screened in the intersecting genes of DEGs and WGCNA (MEturquoise and MEblue). Only SPP1 was correlated with LUAD poor survival, indicating that SPP1 may be a key Hub gene for LUAD. The competing endogenous RNA (ceRNA) network was constructed to analyze the regulatory relationship of Hub genes, and SPP1 may be directly regulated by 4 microRNAs (miRNAs) and indirectly regulated by 49 long noncoding RNAs (lncRNAs).

Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Simple Summary

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In the past few years, studies on the gene expression changes of dairy cow mammary glands were only based on transcriptome comparisons between two lactation stages. Few studies focused on the relationships between gene expression of the dairy mammary gland and lactation stage or milk composition in a lactation cycle. In this study, we detected milk yield and composition in a lactation cycle. For the first time, we constructed a gene coexpression network using WGCNA on the basis of 18 gene expression profiles during six stages of a lactation cycle by transcriptome sequencing, generating 10 specific modules. Genes in each module were performed with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Module–trait relationship analysis showed a series of potential candidates related to milk yield and composition. The current study provides an important theoretical basis for the further molecular breeding of dairy cows.

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates

Malaria is a fatal parasitic disease with unelucidated pathogenetic mechanism. Herein, we aimed to uncover genes associated with different clinical aspects of malaria based on the GSE1124 dataset that is publicly accessible by using WGCNA. We obtained 16 co-expression modules and their correlations with clinical features. Using the MCODE tool, we identified THEM4, STYX, VPS36, LCOR, KIAA1143, EEA1, RAPGEF6, LOC439994, ZBTB33, PTPN22, ESCO1, and KLF3 as hub genes positively associated with Plasmodium falciparum infection (ASPF). These hub genes were involved in the biological processes of endosomal transport, regulation of natural killer cell proliferation, and KEGG pathways of endocytosis and fatty acid elongation. For the purple module negatively correlated with ASPF, we identified 19 hub genes that were involved in the biological processes of positive regulation of cellular protein catabolic process and KEGG pathways of other glycan degradation. For the salmon module positively correlated with severe malaria anemia (SMA), we identified 17 hub genes that were among those driving the biological processes of positive regulation of erythrocyte differentiation. For the brown module positively correlated with cerebral malaria (CM), we identified eight hub genes and these genes participated in phagolysosome assembly and positive regulation of exosomal secretion, and animal mitophagy pathway. For the tan module negatively correlated with CM, we identified four hub genes that were involved in CD8-positive, alpha-beta T cell differentiation and notching signaling pathway. These findings may provide new insights into the pathogenesis of malaria and help define new diagnostic and therapeutic approaches for malaria patients.

Hypoxia-associated alternative splicing signature in lung adenocarcinoma

Aim: To establish a signature based on hypoxia-related alternative splicing (AS) events for lung adenocarcinoma. Materials & methods: The least absolute shrinkage and selection operator Cox approach was used to construct a prognostic model. A nomogram that integrates the final AS predictor and stage was created. The network of the key AS events and splicing factors was created. Results: We created a prognostic signature of 11 AS events. Moreover, a nomogram that constitutes the pathological stage and risk was exhibited to be greatly effective in estimating the survival likelihood of lung adenocarcinoma patients. Conclusion: Herein we developed the first-ever signature based on hypoxia-related AS events with both prognostic predictive power and diagnostic efficacy.

Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

Background

Increasing bodies of evidence reveal that targeting a programmed cell death protein 1 (PD-1) monoclonal antibody is a promising immunotherapy for lung adenocarcinoma. Although PD receptor ligand 1 (PDL1) expression is widely recognized as the most powerful predictive biomarker for anti-PD-1 therapy, its regulatory mechanisms in lung adenocarcinoma remain unclear. Therefore, we conducted this study to explore differentially expressed genes (DEGs) and elucidate the regulatory mechanism of PDL1 in lung adenocarcinoma.

Methods

The GSE99995 data set was obtained from the Gene Expression Omnibus (GEO) database. Patients with and without PDL1 expression were divided into PDL1-positive and PDL1-negative groups, respectively. DEGs were screened using R. The Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) networks of DEGs was visualized using Cytoscape, and the MNC algorithm was applied to screen hub genes. A survival analysis involving Gene Expression Profiling Interactive Analysis was used to verify the GEO results. Mutation characteristics of the hub genes were further analyzed in a combined study of five datasets in The Cancer Genome Atlas (TCGA) database.

Results

In total, 869 DEGs were identified, 387 in the PDL1-positive group and 482 in the PDL1-negative group. GO and KEGG analysis results of the PDL1-positive group mainly exhibited enrichment of biological processes and pathways related to cell adhesion and the peroxisome proliferators-activated receptors (PPAR) signaling pathway, whereas biological process and pathways associated with cell division and repair were mainly enriched in the PDL1-negative group. The top 10 hub genes were screened during the PPI network analysis. Notably, survival analysis revealed BRCA1, mainly involved in cell cycle and DNA damage responses, to be a novel prognostic indicator in lung adenocarcinoma. Moreover, the prognosis of patients with different forms of lung adenocarcinoma was associated with differences in mutations and pathways in potential hub genes.

Conclusions

PDL1-positive lung adenocarcinoma and PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. The hub genes might play an important role in PDL1 regulatory pathways. Further studies on hub genes are warranted to reveal new mechanisms underlying the regulation of PDL1 expression. These results are crucial for understanding and applying precision immunotherapy for lung adenocarcinoma.