Gene Expression Alterations and Molecular Analysis of CHEK1 in Solid Tumors

Checkpoint kinase 1 in colorectal cancer: Upregulation of expression and promotion of cell proliferation

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignant tumor characterized by a high mortality rate, with significant challenges persisting in the identification and management of its metastatic stage. The role of checkpoint kinase 1 (CHEK1), a cell cycle checkpoint kinase, in CRC has not been fully clarified. We hypothesize that the upregulation of CHEK1 may enhance the proliferation of CRC cells, indicating its potential as a novel therapeutic target for CRC therapy.

AIM

To investigate the expression and function of CHEK1 in CRC, this study utilizes single-cell RNA sequencing and tissue microarray data.

METHODS

Single-cell RNA sequencing technology was employed to analyze CRC cells from the GSE144735 dataset, and immunohistochemistry was conducted to confirm the expression of CHEK1 in CRC and adjacent tissues. We also integrated mRNA expression data from multiple public databases to assess global CHEK1 expression in CRC. Molecular docking experiments were performed to explore the interaction between CHEK1 and the potential drug nitidine chloride (NC), as well as to investigate the influence of CHEK1 on CRC cell proliferation.

RESULTS

We found comparatively elevated CHEK1 expression in the malignant epithelial cells of CRC, with marked upregulation of its mRNA levels in CRC tissues. Immunohistochemical analysis further confirmed the high expression of CHEK1 in CRC tissues, and the receiver operating characteristic curve demonstrated high accuracy (area under the curve = 0.964) for CHEK1 as a biomarker. Analysis of global datasets indicated a statistically significant overexpression of CHEK1 in CRC (standard mean difference = 1.81, P < 0.01), with summary receiver operating characteristic analysis yielding sensitivity and specificity values of 0.83 and 0.88, respectively. Molecular docking studies indicated that NC specifically targeted CHEK1, while clustered regularly interspaced short palindromic repeats knockout experiments demonstrated that CHEK1 promoted CRC cell proliferation.

CONCLUSION

Upregulation of CHEK1 promotes CRC cell proliferation. However, the dataset's diversity is limited, requiring further investigation into its specific mechanisms.

Integrated analysis of cell cycle and p53 signaling pathways related genes in breast, colorectal, lung, and pancreatic cancers: implications for prognosis and drug sensitivity for therapeutic potential

Cancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022). Among various types, lung cancer is the most prevalent with high morbidity, while breast, colorectal, and pancreatic cancers also show high mortality rates. Cancer progression often involves disruption in cell cycle regulation and signaling pathways, with mutations in genes like TP53, EGFR, and K-RAS playing significant roles. In this study, we analyzed gene expression datasets to identify common molecular signatures across breast, colorectal, lung and pancreatic cancers. Our focus was on genes related to cell cycle regulation and p53 signaling pathway, intending to discover potential biomarkers for improved diagnosis and treatment strategies. The study analyzed GEO datasets; GSE45827, GSE9348, GSE30219, and GSE62165 for breast, colorectal, lung, and pancreatic cancers respectively. Differentially expressed genes (DEGs) were identified using GEO2R, and functional annotation and pathway analysis were performed using WebGestalt. Common cell cycle and p53 signaling genes were acquired from MSigDB using GSEA. A protein-protein interaction network was constructed using STRING and Cytoscape, identifying top hub genes. Validation of hub genes at mRNA and protein levels was done via GEPIA2 and Human Protein Atlas. Survival analysis was conducted using TCGA data by GEPIA2 and LASSO, and drug sensitivity was analyzed with the GSCA drug bank database, highlighting potential therapeutic targets. The study identified 411 common DEGs among these four cancers. Pathway and functional enrichment revealed key biological processes and pathways like p53 signaling, and cell cycle. The intersection of these DEGs with genes involved in cell cycle and p53 signaling, identified 23 common genes that were used for constructing a PPI network. The top 10 hub genes were validated both for mRNA and protein expression, revealing they are significantly overexpressed in all studied cancers. Prognostic relevance showed that MCM4, MCM6, CCNA2, CDC20, and CHEK1 are associated with survival. Additionally, drug sensitivity analysis highlighted key gene-drug interactions, suggesting potential targets for therapeutic intervention.

Altered TP53, CDKN2A, ATM, EPHA7, POT1, CHEK1, GRIN2A, and EGFR Predict Shorter Survival in Penile Squamous Cell Carcinoma

Penile squamous cell carcinoma (pSCC) represents an uncommon malignancy characterized by stagnant mortality, psychosexual distress, and a highly variable prognosis. Currently, the World Health Organization distinguishes between human papillomavirus (HPV)-related and HPV-independent pSCC. Recently, there has been an evolving line of research documenting the enrichment of HPV-independent pSCC with a high tumor mutational burden (TMB) and programmed death ligand-1 expression, as well as clusters of genes associated with HPV status. In this study, we conducted comprehensive next-generation sequencing DNA profiling of 146 pSCC samples using a panel consisting of 355 genes associated with tumors. This profiling was correlated with immunohistochemical markers and prognostic clinical data. A survival analysis of recurrent genomic events (found in ≥10 cases) was performed. TP53, CDKN2A, ATM, EPHA7, POT1, CHEK1, GRIN2A, and EGFR alterations were associated with significantly shortened overall survival in univariate and multivariate analysis. HPV positivity, diagnosed through both p16 immunohistochemistry and HPV DNA analysis, displayed no impact on survival but was associated with high-grade, lymphatic invasion, programmed death ligand-1 negativity/weak expression, and low TMB. FAT1, TP53, CDKN2A, CASP8, and HRAS were more often mutated in HPV-independent pSCC. In contrast, HPV-associated pSCCs were enriched by EPHA7, ATM, GRIN2A, and CHEK1 mutations. PIK3CA, FAT1, FBXW7, and KMT2D mutations were associated with high TMB. NOTCH1, TP53, CDKN2A, POT1, KMT2D, ATM, CHEK1, EPHA3, and EGFR alterations were related to adverse clinicopathologic signs, such as advanced stage, high tumor budding, and lymphovascular invasion. We detected 160 alterations with potential treatment implications, with 21.2% of samples showing alterations in the homologous recombination repair pathway. To the best of our knowledge, this study describes the largest cohort of pSCC with complex molecular pathologic, clinical, and prognostic analysis correlating with prognosis.

An Analysis of Genetic Polymorphisms in 76 Genes Related to the Development of Ovarian Tumors of Different Aggressiveness

Borderline ovarian tumors (BOTS) are rare neoplasms of intermediate aggressiveness between cystadenomas and low-grade ovarian cancers (lgOvCa), which they share some molecular resemblances with. In contrast to the most frequent and well-described high-grade ovarian carcinomas (hgOvCa), the molecular background of BOTS and lgOvCa is less thoroughly characterized. Here, we aimed to analyze genetic variants in crucial tumor suppressors and oncogenes in BOTS (with or without the BRAF V600E mutation), lgOvCa, and hgOvCa in two gene panels using next-generation sequencing. Then, we verified the existence of selected polymorphisms by Sanger sequencing. Finally, Western blot analyses were carried out to check the impact of the selected polymorphisms on the expression of the corresponding proteins. Our study contributes to the molecular characterization of ovarian neoplasms, demonstrating divergent polymorphic patterns pointing to distinct signaling pathways engaged in their development. Certain mutations seem to play an important role in BOTS without the BRAF V600E variant (KRAS) and in lgOvCa (KRAS and NRAS), but not in hgOvCa. Additionally, based on multivariable regression analyses, potential biomarkers in BOTS (PARP1) and hgOvCa (FANCI, BRCA2, TSC2, FANCF) were identified. Noteworthy, for some of the analyzed genes, such as FANCI, FANCD2, and FANCI, FANCF, TSC2, the status of BRCA1/2 and TP53, respectively, turned out to be crucial. Our results shed new light on the similarities and differences in the polymorphic patterns between ovarian tumors of diverse aggressiveness. Furthermore, the biomarkers identified herein are of potential use as predictors of the prognosis and/or response to therapy.

Case report: A germline CHEK1 c.613 + 2T>C leads to a splicing error in a family with multiple cancer patients

Background

Genome instability plays a crucial role in promoting tumor development. Germline mutations in genes responsible for DNA repair are often associated with familial cancer syndromes. A noticeable exception is the CHEK1 gene. Despite its well-established role in homologous recombination, germline mutations in CHEK1 are rarely reported.

Case presentation

In this report, we present a patient diagnosed with ovarian clear cell carcinoma who has a family history of cancer. Her relatives include a grandfather with esophageal cancer, a father with gastric cancer, and an uncle with a brain tumor. The patient carried a typical genomic profile of clear cell carcinoma including mutations in KRAS, PPP2R1A, and PIK3R1. Importantly, her paired peripheral blood cells harbored a germline CHEK1 mutation, CHEK1 exon 6 c.613 + 2T>C, which was also found in her father. Unfortunately, the CHEK1 status of her grandfather and uncle remains unknown due to the unavailability of their specimens. Further evaluation via RT-PCR confirmed a splicing error in the CHEK1 gene, resulting in truncation at the kinase domain region, indicative of a loss-of-function mutation.

Conclusion

This case highlights a rare germline CHEK1 mutation within a family with a history of cancer. The confirmed splicing error at the mRNA level underscores the functional consequences of this mutation. Documenting such cases is vital for future evaluation of inheritance patterns, clinical penetrance of the mutation, and its association with specific cancer types.

Network pharmacology and molecular docking reveal the mechanisms of curcumin activity against esophageal squamous cell carcinoma

Background: Curcumin (CUR), an effective traditional Chinese medicinal extract, displays good anti-cancer activity against various cancers. Nevertheless, the impacts and fundamental mechanisms of CUR to treat esophageal squamous cell carcinoma (ESCC) yet to be comprehensively clarified. This study examined the suppressive impacts of CUR on ESCC. Methods: For a comprehensive understanding of the effect of CUR in ESCC. The CUR targets and ESCC-related genes were identified respectively, and the intersection targets between CUR and ESCC were acquired. Then, we examined the intersection targets and discovered genes that were expressed differently in ESCC. Using DAVID, enrichment analyses were conducted on the targets of CUR-ESCC. The STRING database and Cytoscape v.3.9.1 were utilized to build networks for protein-protein interaction (PPI) and drug-target-pathway. Furthermore, the interactions between CUR and its core targets were confirmed by molecular docking studies. To confirm the effects of CUR on ESCC cells, in vitro experiments were finally conducted. Results: Overall, 47 potential CUR targets for ESCC treatment were identified. The KEGG pathway enrichment analysis identified 61 signaling pathways, primarily associated with the FoxO signaling, the cell cycle, cellular senescence, the IL-17 signaling pathway which play important roles in ESCC progression. In the PPI network and the docking results identified CHEK1 and CDK6 as the core targets that positively associated with ESCC survival. CUR arrested ESCC cells at the G2/M and S phases, as shown by flow cytometry. Colony formation and CCK8 assays showed that CUR can inhibit the proliferative ability of ESCC cells. The Transwell invasion results validated that CUR can significantly inhibit the invasion rates of ESCC cells. Conclusion: Collectively, these findings indicate that CUR exhibits pharmacological effects on multiple targets and pathways in ESCC.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis.

METHODS

We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups.

RESULTS

Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69).

CONCLUSION

The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

Inhibiting BRAF/EGFR/MEK suppresses cancer stemness and drug resistance of primary colorectal cancer cells

Drug resistance is a major barrier against successful treatments of cancer patients. Gain of stemness under drug pressure is a major mechanism that renders treatments ineffective. Identifying approaches to target cancer stem cells (CSCs) is expected to improve treatment outcomes for patients. To elucidate the role of cancer stemness in resistance of colorectal cancer cells to targeted therapies, we developed spheroid cultures of patient-derived BRAFmut and KRASmut tumor cells and studied resistance mechanisms to inhibition of MAPK pathway through phenotypic and gene and protein expression analysis. We found that treatments enriched the expression of CSC markers CD166, ALDH1A3, CD133, and LGR5 and activated PI3K/Akt pathway in cancer cells. We examined various combination treatments to block these activities and found that a triple combination against BRAF, EGFR, and MEK significantly reduced stemness and activities of oncogenic signaling pathways. This study demonstrates the feasibility of blocking stemness-mediated drug resistance and tumorigenic activities in colorectal cancer.

Predictive signature of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer integrating mRNA expression, taxonomic subtypes, and clinicopathological features

Background and objective

Neoadjuvant chemotherapy (NAC) followed by cystectomy is the standard of care in muscle-invasive bladder cancer (MIBC). Pathological response has been associated with longer survival, but no currently available clinicopathological variables can identify patients likely to respond, highlighting the need for predictive biomarkers. We sought to identify a predictive signature of response to NAC integrating clinical score, taxonomic subtype, and gene expression.

Material and methods

From 1994 to 2014, pre-treatment tumor samples were collected from MIBC patients (stage T2-4N0/+M0) at two Spanish hospitals. A clinical score was determined based on stage, hydronephrosis and histology. Taxonomic subtypes (BASQ, luminal, and mixed) were identified by immunohistochemistry. A custom set of 41 genes involved in DNA damage repair and immune response was analyzed in 84 patients with the NanoString nCounter platform. Genes related to pathological response were identified by LASSO penalized logistic regression. NAC consisted of cisplatin/methotrexate/vinblastine until 2000, after which most patients received cisplatin/gemcitabine. The capacity of the integrated signature to predict pathological response was assessed with AUC. Overall survival (OS) and disease-specific survival (DSS) were analyzed with the Kaplan-Meier method.

Results

LASSO selected eight genes to be included in the signature (RAD51, IFNγ, CHEK1, CXCL9, c-MET, KRT14, HERC2, FOXA1). The highest predictive accuracy was observed with the inclusion in the model of only three genes (RAD51, IFNɣ, CHEK1). The integrated clinical-taxonomic-gene expression signature including these three genes had a higher predictive ability (AUC=0.71) than only clinical score plus taxonomic subtype (AUC=0.58) or clinical score alone (AUC=0.56). This integrated signature was also significantly associated with OS (p=0.02) and DSS (p=0.02).

Conclusions

We have identified a predictive signature for response to NAC in MIBC patients that integrates the expression of three genes with clinicopathological characteristics and taxonomic subtypes. Prospective studies to validate these results are ongoing.

Cancer stem cell-mediated drug resistance: A comprehensive gene expression profile analysis in breast cancer

Breast cancer is the most frequently diagnosed malignancy in women and a major public health concern. In the current report, differential expression of the breast cancer resistance promoting genes with a focus on breast cancer stem cell related elements as well as the correlation of their mRNAs with various clinicopathologic characteristics, including molecular subtypes, tumor grade/stage, and methylation status, have been investigated using METABRIC and TCGA datasets. To achieve this goal, we downloaded gene expression data of breast cancer patients from TCGA and METABRIC. Then, statistical analyses were used to assess the correlation between the expression levels of stem cell related drug resistant genes and methylation status, tumor grades, various molecular subtypes, and some cancer hallmark gene sets such as immune evasion, metastasis, and angiogenesis. According to the results of this study, a number of stem cell related drug resistant genes are deregulated in breast cancer patients. Furthermore, we observe negative correlations between methylation of resistance genes and mRNA expression. There is a significant difference in the expression of resistance-promoting genes between different molecular subtypes. As mRNA expression and DNA methylation are clearly related, DNA methylation might be a mechanism that regulates these genes in breast cancer cells. As indicated by the differential expression of resistance-promoting genes among various breast cancer molecular subtypes, these genes may function differently in different subtypes of breast cancer. In conclusion, significant deregulation of resistance-promoting factors indicates that these genes may play a significant role in the development of breast cancer.

DNA Repair Mechanisms, Protein Interactions and Therapeutic Targeting of the MRN Complex

Repair of a DNA double-strand break relies upon a pathway of proteins to identify damage, regulate cell cycle checkpoints, and repair the damage. This process is initiated by a sensor protein complex, the MRN complex, comprised of three proteins-MRE11, RAD50, and NBS1. After a double-stranded break, the MRN complex recruits and activates ATM, in-turn activating other proteins such as BRCA1/2, ATR, CHEK1/2, PALB2 and RAD51. These proteins have been the focus of many studies for their individual roles in hereditary cancer syndromes and are included on several genetic testing panels. These panels have enabled us to acquire large amounts of genetic data, much of which remains a challenge to interpret due to the presence of variants of uncertain significance (VUS). While the primary aim of clinical testing is to accurately and confidently classify variants in order to inform medical management, the presence of VUSs has led to ambiguity in genetic counseling. Pathogenic variants within MRN complex genes have been implicated in breast, ovarian, prostate, colon cancers and gliomas; however, the hundreds of VUSs within MRE11, RAD50, and NBS1 precludes the application of these data in genetic guidance of carriers. In this review, we discuss the MRN complex's role in DNA double-strand break repair, its interactions with other cancer predisposing genes, the variants that can be found within the three MRN complex genes, and the MRN complex's potential as an anti-cancer therapeutic target.

Identification of hub genes and their correlation with infiltration of immune cells in MYCN positive neuroblastoma based on WGCNA and LASSO algorithm

Background

The prognosis of MYCN positive NB is poor, and there is no targeted drug for N-myc at present. This study aims to screen out hub genes closely related to MYCN, analyze the relationship between hub genes and NB microenvironment, and provide basis for molecular targeted therapy of MYCN positive NB.

Methods

We combined the microarray data of GSE45547 (n=649) and GSE49710 (n=498), screened the DEGs between MYCN positive (n=185) and MYCN negative NB (n=951), performed WGCNA, Lasso regression and Roc analyses on the merged matrix, and obtained the hub genes related to MYCN in the training group. We performed ssGSEA on the experimental group to calculate the infiltration level of 28 kinds of immune cells in each sample, compared the differences of immune cell infiltration between MYCN positive and MYCN negative group. The influences of hub genes on the distribution of each immune cell were also analyzed by ssGSEA. The expression differences of the three hub genes were verified in the E-MTAB-8248 cohort (n=223), and the correlation between hub genes and prognosis of NB was calculated by Kaplan-Meier method in GSE62564 (n=498) and the validation group. We also verified the expression differences of hub genes by qRT-PCR in SK-N-BE(2), SKNDZ, Kelly and SH-SY5Y cell lines.

Results

Here were 880 DEGs including 420 upregulated and 460 downregulated genes in MYCN positive NB in the training group. Overlap of the DEGs and WGCNA networks identified four shared genes, namely, ZNF695, CHEK1, C15ORF42 and EXO1, as candidate hub genes in MYCN positive NB. Three core genes, ZNF695, CHEK1 and C15ORF42, were finally identified by Lasso regression and Roc analyses. ZNF695, CHEK1 and C15ORF42 were highly expressed in MYCN positive NB tissues and cell lines. These three genes were closely related to the prognosis of children with NB. Except that Activated CD4 T cell and Type2 T helper cell increased, the infiltration levels of the other 26 cells decreased significantly in MYCN positive NB tissues. The infiltration levels of Type2 T helper cell and Activated CD4 T cell were also significantly positively correlated with the expression levels of the three hub genes.

Conclusion

ZNF695, CHEK1 and C15ORF42 are highly expressed in MYCN positive NB, and their expression levels are negatively correlated with the prognosis of children with NB. The infiltration levels of Activated CD4 T cell and Type2 T helper cell increased in the microenvironment of MYCN positive NB and were significantly positively correlated with the expression levels of the three hub genes. The results of this study provide that ZNF695, CHEK1 and C15ORF42 may be potential prognostic markers and immunotherapy targets for MYCN positive NB.

Prognostic and tumor immunity implication of inflammatory bowel disease-associated genes in colorectal cancer

BACKGROUND

Epidemiologic studies continue to emphasize that increasing patients with inflammatory bowel disease (IBD) develop to colorectal cancer (CRC). Although the function and mechanisms of IBD-associated genes (IBDGs) in CRC tumorigenesis have been extensively researched, the implications of IBDGs in the prognosis value and tumor immunity of CRC remain unclear.

RESULTS

In this study, the expression, pathological stages and prognostic value of IBDGs in CRC were systematically analyzed, and 7 prognostic genes including CDH1, CCL11, HLA-DRA, NOS2, NAT2, TIMP1 and TP53 were screened through LASSO-Cox regression analysis. Then, a prognostic signature was established based on the 7 prognostic genes, and the model exhibited a good ability in risk stratification of CRC patients. Subsequent results showed that the genetic alterations of the 7 prognostic genes exhibited more significant and extensive influence on immune cells infiltration in colon adenocarcinoma than that in rectal adenocarcinoma. Meanwhile, immune cells infiltration also showed a significant difference between low-risk group and high-risk group. What's more, 7 prognostic genes-based risk stratification was associated with microsatellite instability, and its prognostic characteristics were significantly negatively correlated with mismatch repair genes.

CONCLUSIONS

This study provided a promising insight that the 7 IBDGs could be used as valuable biomarkers for prognostic diagnosis and personalized immunotherapy of CRC patients.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Proteomic profiling based classification of CLL provides prognostication for modern therapy and identifies novel therapeutic targets

Protein expression for 384 total and post-translationally modified proteins was assessed in 871 CLL and MSBL patients and was integrated with clinical data to identify strategies for improving diagnostics and therapy, making this the largest CLL proteomics study to date. Proteomics identified six recurrent signatures that were highly prognostic of survival and time to first or second treatment at three levels: individual proteins, when grouped into 40 functionally related groups (PFGs), and systemically in signatures (SGs). A novel SG characterized by hairy cell leukemia like proteomics but poor therapy response was discovered. SG membership superseded other prognostic factors (Rai Staging, IGHV Status) and were prognostic for response to modern (BTK inhibition) and older CLL therapies. SGs and PFGs membership provided novel drug targets and defined optimal candidates for Watch and Wait vs. early intervention. Collectively proteomics demonstrates promise for improving classification, therapeutic strategy selection, and identifying novel therapeutic targets.

A Novel Defined Necroptosis-Related miRNAs Signature for Predicting the Prognosis of Colon Cancer

Objective

This study aims at exploring the relationship between necroptosis-related miRNAs and colon cancer prognosis.

Methods

We downloaded the miRNA sequencing data from the TCGA, and eight differentially expressed necroptosis-related miRNAs were screened. Then, we used Cox regression analysis to establish a prediction model of necroptosis-related miRNA. Finally, the prognosis related miRNAs were used to predict the target genes, and functional analysis was used to explore the potential mechanism of these target genes.

Results

The miRNA-seq data of 444 COAD cases were downloaded from TCGA. We identified 8 differentially expressed miRNAs (has-miR-16-5p, has-miR-141-3p, has-miR-148a-3p, has-miR-425-5p, has-miR-7-5p, has-miR-223-3p, has-miR-200a-5p, and has-miR-500a-3p), then Cox analysis was performed for determining eight-miRNA signature prognostic biomarkers with obviously different OS. The area under the curve (AUC) of receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival were 0.663, 0.653 and 0.639, respectively. The multivariate analysis also implied that the risk score was an independent prognostic factor considering other confounding factors (HR = 1.847, 95% CI = 1.197–2.848, P = 0.006). According to the Kaplan–Meier analysis, the expression of hsa-miR-500a-3p (P = 0.003), hsa-miR-16-5p (P = 0.004) and hsa-miR-148a-3p (P = 0.035) significantly affected OS outcomes. We predicted the target genes of these three miRNAs and then screened 10 hub genes (CCND1, SMAD3, SMAD2, CDK1, TGFB2, CDC25A, CHEK1, VEGFA, CCNE1, WEE1). In addition, CHEK1 was associated with the survival prognosis.

Conclusion

Our study demonstrated that necroptosis is closely associated with colon cancer, and the model of eight necroptosis-related miRNAs are potentially useful prognostic biomarkers and therapeutic targets for colon cancer.

CHEK1: a hub gene related to poor prognosis for lung adenocarcinoma

Aim: The study aims to pinpoint hub genes and investigate their functions in order to gain insightful understandings of lung adenocarcinoma (LUAD). Methods: Bioinformatic approaches were adopted to investigate genes in databases including Gene Expression Omnibus, WebGestalt, STRING and Cytoscape, GEPIA2, Oncomine, Human Protein Atlas, TIMER2.0, UALCAN, cBioPortal, TargetScanHuman, OncomiR, ENCORI, Kaplan-Meier plotter, UCSC Xena, European Molecular Biology Laboratory - European Bioinformatics Institute Single Cell Expression Atlas and CancerSEA. Results: Five hub genes were ascertained. CHEK1 was overexpressed in a range of cancers, including LUAD. Promoter methylation, amplification and miRNA regulation might trigger CHEK1 upregulation, signaling poor prognosis. CHEK1 with its coexpressed genes were enriched in the cell cycle pathway. Intratumor heterogeneity of CHEK1 expression could be observed. Cell clusters with CHEK1 expression were more prone to metastasis and epithelial-to-mesenchymal transition. Conclusion: CHEK1 might potentially act as a prognostic biomarker for LUAD.

Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer

Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

Expression of cyclin-dependent kinases and their clinical significance with immune infiltrates could predict prognosis in colorectal cancer

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The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis than previously studies.

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The association of CDKs with immune infiltrates may serve as target molecules for immunotherapy in CRC.

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The expression of CDK is significant among CRC subtypes and therefore, it can be inferred as a potential biomarker in the cancer subtype.

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An increase in tumor purity was positively correlated with the expression of CDK-1 in COAD due to CD4+ cells and CDK-4 in COAD and READ resulting from a fraction of immune cells.

Introduction

Colorectal cancer (CRC) is one of the most cancer-related mortalities worldwide and remains a major public health issue. Despite several attempts to develop promising therapies for CRC, its survival rate decreases with metastasis. Cyclin-dependent kinases (CDKs) are a family of protein kinases with various regulatory activities including cell cycle, mRNA expression, transcription, and differentiation. Aside from their role in cell proliferation when mutated, abnormal expression of these genes has been reported in some human cancer subtypes. This study explored the roles and therapeutic potentials of CDK 1 and 4 as prognostic biomarkers in CRC.

Methods

Bioinformatics analyses were carried out to demonstrate the expression and prognostic values of CDK-1 and CDK-4 with immune infiltrate in CRC.

Discussion

CDK levels in CRC were remarkably higher than those in normal tissues (p < 0.05), and overexpression in CRC tissues was significantly related to nodal metastatic status (p < 0.05) and histological subtypes. Kaplan-Meier analyses showed that patients with CRC who exhibited CDK-1 overexpression had worse overall survival (OS) as against patients with CDK-4 overexpression. The alteration observed was a mutation while the mutation hotspots include E163* and R24A/C/H/L respectively for CDK-1 and CDK-4 on the Pkinase domain. Of the associated genes, AURKA and RB1 were predominantly altered. Furthermore, CDK-4 is positively correlated with tumor purity in both COAD and READ while CDK-1is only positively correlated in COAD. CDK-1 overexpression was significantly associated with poor prognosis as opposed to CDK-4.

Conclusion

The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis. CDKs together with the co-expressed genes and their association with immune infiltrates may serve as target molecules for immunotherapy in CRC.

Construction of a disease-specific lncRNA-miRNA-mRNA regulatory network reveals potential regulatory axes and prognostic biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA‐seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA‐miRNA‐mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool‐based ceRNA network, an HCC‐specific ceRNA network was obtained, and lncRNA‐miRNA‐mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer‐related pathways. Four HCC‐specific lncRNA‐miRNA‐mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK‐AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK‐AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1‐year, 3‐year, and 5‐year survival was 0.777 (0.657‐0.865), 0.722 (0.640‐0.848), and 0.630 (0.528‐0.823), and 0.751 (0.664‐0.870), 0.773 (0.707‐0.849), and 0.734 (0.638‐0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC.

Inhibitory potential of repurposed drugs against the SARS-CoV-2 main protease: a computational-aided approach

The exponential increase in cases and mortality of coronavirus disease (COVID-19) has called for a need to develop drugs to treat this infection. Using in silico and molecular docking approaches, this study investigated the inhibitory effects of Pradimicin A, Lamivudine, Plerixafor and Lopinavir against SARS-CoV-2 M^pro. ADME/Tox of the ligands, pharmacophore hypothesis of the co-crystalized ligand and the receptor, and docking studies were carried out on different modules of Schrodinger (2019-4) Maestro v12.2. Among the ligands subjected to ADME/Tox by QikProp, Lamivudine demonstrated drug-like physico-chemical properties. A total of five pharmacophore binding sites (A3, A4, R9, R10, and R11) were predicted from the co-crystalized ligand and the binding cavity of the SARS-CoV-2 M^pro. The docking result showed that Lopinavir and Lamivudine bind with a higher affinity and lower free energy than the standard ligand having a glide score of -9.2 kcal/mol and -5.3 kcal/mol, respectively. Plerixafor and Pradimicin A have a glide score of -3.7 kcal/mol and -2.4 kcal/mol, respectively, which is lower than the co-crystallized ligand with a glide score of -5.3 kcal/mol. Molecular dynamics confirmed that the ligands maintained their interaction with the protein with lower RMSD fluctuations over the trajectory period of 100 nsecs and that GLU166 residue is pivotal for binding. On the whole, present study specifies the repurposing aptitude of these molecules as inhibitors of SARS-CoV-2 M^pro with higher binding scores and forms energetically stable complexes with M^pro.Communicated by Ramaswamy H. Sarma.

MicroRNA-based regulation of Aurora A kinase in breast cancer

The involvement of non-coding RNAs (ncRNAs) in cellular physiology and disease pathogenesis is becoming increasingly relevant in recent years specifically in cancer research. Breast cancer (BC) has become a health concern and accounts for most of the cancer-related incidences and mortalities reported amongst females. In spite of the presence of promising tools for BC therapy, the mortality rate of metastatic BC cases is still high. Therefore, the genomic exploration of the BC subtype and the use of ncRNAs for possible regulation is pivotal. The expression and prognostic values of AURKA gene were assessed by Oncomine, GEPIA, KM-plotter, and bc-GenExMiner v4.4, respectively. Associated proteins and functional enrichment were evaluated by Cytoscape and DAVID databases. Additionally, molecular docking approach was employed to investigate the regulatory role of hsa-miR-32-3p assisted argonaute (AGO) protein of AURKA gene in BC. AURKA gene was highly expressed in patients with BC relative to normal counterpart and significantly correlated with poor survival. The docking result suggested that AURKA could be regulated by hsa-miR-32-3p as confirmed by the reported binding energy and specific interactions. The study gives some insights into role of AURKA and its regulation by microRNAs through AGO protein. It also provides exciting opportunities for cancer therapeutic intervention.