Identification of potential diagnostic and therapeutic target genes for lung squamous cell carcinoma

Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.

MND1 functions as a potential prognostic biomarker associated with cell cycle and immune infiltration in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common and invasive subtype of renal tumors, which has poor prognosis and high mortality. MND1 is a meiosis specific protein that participates in the progress of diverse cancers. Nonetheless, its function in KIRC was unclear. Here, TIMER, TCGA, GEO databases and IHC found MND1 expression is upregulated in KIRC, leading to poor overall survival, and MND1 can serve as an independent prognostic factor. Moreover, enrichment analysis revealed the functional relationship between MND1 and cell cycle, immune infiltration. EdU and transwell assays confirmed that MND1 knockdown surely prohibited the proliferation, migration, and invasion of KIRC cells. Additionally, immune analysis showed that MND1 displayed a strong correlation with various immune cells. Interference with MND1 significantly reduces the expression of chemokines. TCGA and GEO databases indicated that MND1 expression is significantly related to two m6A modification related gene (METTL14, IGF2BP3). Finally, the drug sensitivity analysis revealed 7 potentially sensitive drugs for KIRC patients with high MND1 expression. In conclusion, MND1 can be used as a prognostic biomarker for KIRC and provides clues regarding cell cycle, immune infiltrates and m6A. Sensitive drugs may be an effective treatment strategy for KIRC patients with high expression of MND1.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586-0.671) in the TCGA cohort and 0.648 (95% CI: 0.535-0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

Meiotic Genes and DNA Double Strand Break Repair in Cancer

Tumor cells show widespread genetic alterations that change the expression of genes driving tumor progression, including genes that maintain genomic integrity. In recent years, it has become clear that tumors frequently reactivate genes whose expression is typically restricted to germ cells. As germ cells have specialized pathways to facilitate the exchange of genetic information between homologous chromosomes, their aberrant regulation influences how cancer cells repair DNA double strand breaks (DSB). This drives genomic instability and affects the response of tumor cells to anticancer therapies. Since meiotic genes are usually transcriptionally repressed in somatic cells of healthy tissues, targeting aberrantly expressed meiotic genes may provide a unique opportunity to specifically kill cancer cells whilst sparing the non-transformed somatic cells. In this review, we highlight meiotic genes that have been reported to affect DSB repair in cancers derived from somatic cells. A better understanding of their mechanistic role in the context of homology-directed DNA repair in somatic cancers may provide useful insights to find novel vulnerabilities that can be targeted.

Phyto-targeting the CEMIP Expression as a Strategy to Prevent Pancreatic Cancer Metastasis

INTRODUCTION

Metastasis of primary pancreatic cancer (PC) to adjacent or distant organs is responsible for the poor survival rate of affected individuals. Chemotherapy, radiotherapy, and immunotherapy are currently being prescribed to treat PC in addition to surgical resection. Surgical resection is the preferred treatment for PC that leads to 20% of 5-year survival, but only less than 20% of patients are eligible for surgical resection because of the poor prognosis. To improve the prognosis and clinical outcome, early diagnostic markers need to be identified, and targeting them would be of immense benefit to increase the efficiency of the treatment. Cell migration-inducing hyaluronan-binding protein (CEMIP) is identified as an important risk factor for the metastasis of various cancers, including PC. Emerging studies have pointed out the crucial role of CEMIP in the regulation of various signaling mechanisms, leading to enhanced migration and metastasis of PC.

METHODS

The published findings on PC metastasis, phytoconstituents, and CEMIP were retrieved from Pubmed, ScienceDirect, and Cochrane Library. Computational tools, such as gene expression profiling interactive analysis (GEPIA) and Kaplan-Meier (KM) plotter, were used to study the relationship between CEMIP expression and survival of PC individuals.

RESULTS

Gene expression analysis using the GEPIA database identified a stupendous increase in the CEMIP transcript in PC compared to adjacent normal tissues. KM plotter analysis revealed the impact of CEMIP on the overall survival (OS) and disease-free survival (DFS) among PC patients. Subsequently, several risk factors associated with PC development were screened, and their ability to regulate CEMIP gene expression was analyzed using computational tools.

CONCLUSION

The current review is focused on gathering information regarding the regulatory role of phytocomponents in PC migration and exploring their possible impact on the CEMIP expression.

Genetic network and gene set enrichment analyses identify MND1 as potential diagnostic and therapeutic target gene for lung adenocarcinoma

This study aimed to characterize the key survival-specific genes for lung adenocarcinoma (LUAD) using machine-based learning approaches. Gene expression profiles were download from gene expression omnibus to analyze differentially expressed genes (DEGs) in LUAD tissues versus healthy lung tissue and to construct protein–protein interaction (PPI) networks. Using high-dimensional datasets of cancer specimens from clinical patients in the cancer genome atlas, gene set enrichment analysis was employed to assess the independent effect of meiotic nuclear divisions 1 (MND1) expression on survival status, and univariate and multivariate Cox regression analyses were applied to determine the associations of clinic-pathologic characteristics and MND1 expression with overall survival (OS). A set of 495 DEGs (145 upregulated and 350 downregulated) was detected, including 63 hub genes with ≥ 10 nodes in the PPI network. Among them, MND1 was participated in several important pathways by connecting with other genes via 17 nodes in lung cancer, and more frequently expressed in LUAD patients with advancing stage (OR = 1.68 for stage III vs. stage I). Univariate and multivariate Cox analyses demonstrated that the expression level of MND1 was significantly and negatively correlated with OS. Therefore, MND1 is a promising diagnostic and therapeutic target for LUAD.

Meiotic nuclear divisions 1 (MND1) fuels cell cycle progression by activating a KLF6/E2F1 positive feedback loop in lung adenocarcinoma

Background

Considering the increase in the proportion of lung adenocarcinoma (LUAD) cases among all lung cancers and its considerable contribution to cancer‐related deaths worldwide, we sought to identify novel oncogenes to provide potential targets and facilitate a better understanding of the malignant progression of LUAD.

Methods

The results from the screening of transcriptome and survival analyses according to the integrated Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) data were combined, and a promising risk biomarker called meiotic nuclear divisions 1 (MND1) was selectively acquired. Cell viability assays and subcutaneous xenograft models were used to validate the oncogenic role of MND1 in LUAD cell proliferation and tumor growth. A series of assays, including mass spectrometry, co‐immunoprecipitation (Co‐IP), and chromatin immunoprecipitation (ChIP), were performed to explore the underlying mechanism.

Results

MND1 up‐regulation was identified to be an independent risk factor for overall survival in LUAD patients evaluated by both tissue microarray staining and third party data analysis. In vivo and in vitro assays showed that MND1 promoted LUAD cell proliferation by regulating cell cycle. The results of the Co‐IP, ChIP and dual‐luciferase reporter assays validated that MND1 competitively bound to tumor suppressor Kruppel‐like factor 6 (KLF6), and thereby protecting E2F transcription factor 1 (E2F1) from KLF6‐induced transcriptional repression. Luciferase reporter and ChIP assays found that E2F1 activated MND1 transcription by binding to its promoter in a feedback manner.

Conclusions

MND1, KLF6, and E2F1 form a positive feedback loop to regulate cell cycle and confer DDP resistance in LUAD. MND1 is crucial for malignant progression and may be a potential therapeutic target in LUAD patients.

Comparative Analysis of Age- and Gender-Associated Microbiome in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

The intra-tumor microbiota has been increasingly implicated in cancer pathogenesis. In this study, we aimed to examine the microbiome in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and determine its compositional differences with relation to age and gender. After grouping 497 LUAD and 433 LUSC patients by age and gender and removing potential contaminants, we identified differentially abundant microbes in each patient cohort vs. adjacent normal samples. We then correlated dysregulated microbes with patient survival rates, immune infiltration, immune and cancer pathways, and genomic alterations. We found that most age and gender cohorts in both LUAD and LUSC contained unique, significantly dysregulated microbes. For example, LUAD-associated Escherichia coli str. K-12 substr. W3110 was dysregulated in older female and male patients and correlated with both patient survival and genomic alterations. For LUSC, the most prominent bacterial species that we identified was Pseudomonas putida str. KT2440, which was uniquely associated with young LUSC male patients and immune infiltration. In conclusion, we found differentially abundant microbes implicated with age and gender that are also associated with genomic alterations and immune dysregulations. Further investigation should be conducted to determine the relationship between gender and age-associated microbes and the pathogenesis of lung cancer.