Genome-wide somatic copy number alteration analysis and database construction for cervical cancer

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes

OBJECTIVE

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics.

METHOD

Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.

RESULTS

We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development.

CONCLUSION

Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

EGLN1: A Biomarker of Poor Prognosis of Cervical Cancer and a Target of Treatment

Objective: To conduct bioinformatics analysis on the prognostic effect, mechanism of action, and drug sensitivity of Egl-9 family hypoxia-inducible factor 1 (EGLN1) expression on cervical cancer. Methods: Bioinformatics were obtained from Gene Expression Profiling Interactive Analysis (GEPIA), Tumor Immune Estimation Resource (TIMER), and the human cancer metastasis database (HCMDB), and the effect of EGLN1 expression level on the prognosis of cervical cancer was comprehensively analyzed. The protein-protein interaction network was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and the possible mechanism of EGLN1 affecting the prognosis of cervical cancer was discussed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, Gene Set Cancer Analysis (GSCALite) was used to predict sensitive drugs online. Results: The higher the expression level of EGLN1, the shorter the tumor-free survival time and overall survival time of cervical cancer. The higher the stage of cervical cancer, the higher the expression level of EGLN1. The expression of EGLN1 affects the degree of immune infiltration, the variation of somatic copy number, and the level of N6-methyladenosine (m6A) modification in cervical cancer. COX regression model suggested that EGLN1 was an independent prognostic factor of cervical cancer. Conclusions: The high expression of EGLN1 in cervical cancer is an independent risk factor for the prognosis of cervical cancer, which affects the prognosis of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) through different signal pathways. It is expected to be used to predict the sensitive anticancer drugs for the treatment of cervical cancer.

Chromosome instability region analysis and identification of the driver genes of the epithelial ovarian cancer cell lines A2780 and SKOV3

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole-exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single-nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

Innovative molecular subtypes of multiple signaling pathways in colon cancer and validation of FMOD as a prognostic-related marker

PURPOSE

Colon cancer is highly heterogeneous in terms of the immune and stromal microenvironment, genomic integrity, and oncogenic properties; therefore, molecular subtypes of the four characteristic dimensions are expected to provide novel clues for immunotherapy of colon cancer.

METHODS

According to the enrichment of four dimensions, we performed consensus cluster analysis and identified three robust molecular subtypes for colon cancer, namely immune enriched, immune deficiency, and stroma enriched. We characterized and validated the immune infiltration, gene mutations, copy number variants, methylation, protein expression, and clinical features in different datasets. Finally, we developed an 8-gene risk prognostic model and proposed the innovative RiskScore. In addition, a nomogram model was constructed combining clinical characteristics and RiskScore to validate its excellent clinical predictive power.

RESULTS

Combining clinical patient tissue samples and histochemical microarray data, we found that high FMOD expression in tumor epithelial cells was associated with poorer patient prognosis, but FMOD expression in the mesenchyme was not associated with prognosis. In pan-cancer, RiskScore, a prognostic model constructed based on characteristic pathway scores, was a poor prognostic factor for malignancy and was negatively associated with immunotherapy response.

CONCLUSION

The identification of molecular subtypes could provide innovative ideas for immunotherapy of colon cancer.

The Role of FBXW7 in Gynecologic Malignancies

The F-Box and WD Repeat Domain Containing 7 (FBXW7) protein has been shown to regulate cellular growth and act as a tumor suppressor. This protein, also known as FBW7, hCDC4, SEL10 or hAGO, is encoded by the gene FBXW7. It is a crucial component of the Skp1-Cullin1-F-box (SCF) complex, which is a ubiquitin ligase. This complex aids in the degradation of many oncoproteins, such as cyclin E, c-JUN, c-MYC, NOTCH, and MCL1, via the ubiquitin-proteasome system (UPS). The FBXW7 gene is commonly mutated or deleted in numerous types of cancer, including gynecologic cancers (GCs). Such FBXW7 mutations are linked to a poor prognosis due to increased treatment resistance. Hence, detection of the FBXW7 mutation may possibly be an appropriate diagnostic and prognostic biomarker that plays a central role in determining suitable individualized management. Recent studies also suggest that, under specific circumstances, FBXW7 may act as an oncogene. There is mounting evidence indicating that the aberrant expression of FBXW7 is involved in the development of GCs. The aim of this review is to give an update on the role of FBXW7 as a potential biomarker and also as a therapeutic target for novel treatments, particularly in the management of GCs.

Diagnostic, prognostic, and immunological roles of CD177 in cervical cancer

BACKGROUND

CD177, an indicator of prognosis in diverse cancers, is involved in the physiological processes of various tumor cells, and acts as an immune molecule with novel functions in cancer pathogenesis. However, the diagnostic, prognostic, and immunological role of CD177 in cervical cancer remains unclear.

METHODS

Utilizing publicly available databases and integrating several bioinformatics analysis methods, we evaluated the expression level of CD177 in cervical cancer by GENT2, HPA, and GEO databases. And the experiments of western blot and immunohistochemical staining were used to test the hypothesis. The Kaplan-Meier Plotter database, Xena Shiny, and the constructed nomogram were clearly demonstrated its prognostic value for patients. Gene set enrichment analysis explored the relationship between CD177 and cervical cancer immune responses and immune cells infiltration level. In addition, we investigated the association between CD177 expression and stromal score, immune score, immune checkpoint, and drug sensitivity by TCGA RNA-seq data.

RESULTS

CD177 was apparently expressed at low levels in cervical cancer and predicted a poor survival rate for patients. CD177 significantly activated immune-related signaling pathways and had a positive relationship with immune cell infiltration level. The high CD177 expression group possessed the high stromal score and immune score. CD177 had potential interactions with CTLA4, CD27, BLTA, CD200R1, CD80, NRP1, TNFRSF25, TIGIT, ICOS, and TNFSF9 checkpoint markers. And CD177 expression was positively relevant with drug sensitivity for Lapatinib, Belinostat, ATRA, Gefitinib, Navitoclax, and Tamoxifen.

SIGNIFICANCE

These findings may shed light on the vital role of CD177 in cervical cancer diagnosis, prognosis, and immunological functions, and it may be a promising predictor and potential factor for cervical cancer patients.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package "maftool" and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package "pRRophetic", CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

FN1 encoding fibronectin as a pivotal signaling gene for therapeutic intervention against pancreatic cancer

The delayed diagnosis of pancreatic cancer has resulted in rising mortality rate and low survival rate that can be circumvented using potent theranostics biomarkers. The treatment gets complicated with delayed detection resulting in lowered 5-year relative survival rate. In our present study, we employed systems biology approach to identify central genes that play crucial roles in tumor progression. Pancreatic cancer genes collected from various databases were used to construct a statistically significant interactome with 812 genes that was further analysed thoroughly using topological parameters and functional enrichment analysis. The significant genes in the network were then identified based on the maximum degree parameter. The overall survival analysis indicated through hazard ratio [HR] and gene expression [log Fold Change] across pancreatic adenocarcinoma revealed the critical role of FN1 [HR 1.4; log2(FC) 5.748], FGA [HR 0.78; log2(FC) 1.639] FGG [HR 0.9; log2(FC) 1.597], C3 [HR 1.1; log2(FC) 2.637], and QSOX1 [HR 1.4; log2(FC) 2.371]. The functional significance of the identified hub genes signified the enrichment of integrin cell surface interactions and proteoglycan syndecan-mediated cell signaling. The differential expression, low overall survival and functional significance of FN1 gene implied its possible role in controlling metastasis in pancreatic cancer. Furthermore, alternate splice variants of FN1 gene showed 10 protein coding transcripts with conserved cell attachment site and functional domains indicating the variants' potential role in pancreatic cancer. The strong association of the identified hub-genes can be better directed to design potential theranostics biomarkers for metastasized pancreatic tumor.

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Bladder Cancer

Tumors are closely related to the tumor microenvironment (TME). The complex interaction between tumor cells and the TME plays an indisputable role in tumor development. Tumor cells can affect the TME, promote tumor angiogenesis and induce immune tolerance by releasing cell signaling molecules. Immune cell infiltration (ICI) in the TME can affect the prognosis of patients with bladder cancer. However, the pattern of ICI of the TME in bladder cancer has not yet been elucidated. Herein, we identified three distinct ICI subtypes based on the TME immune infiltration pattern of 584 bladder cancer patients using the ESTIMATE and CIBERSORT algorithms. Then, we identified three gene clusters based on the differentially expressed genes (DEGs) between the three ICI subtypes. In addition, the ICI score was determined using single sample gene set enrichment analysis (ssGSEA). The results suggested that patients in the high ICI score subgroup had a favorable prognosis and higher expression of checkpoint-related and immune activity-related genes. The high ICI score subgroup was also linked to increased tumor mutation burden (TMB) and neoantigen burden. A cohort treated with anti-PD-L1 immunotherapy confirmed the therapeutic advantage and clinical benefit of patients with higher ICI scores. In the end, our study also shows that the ICI score represents an effective prognostic predictor for evaluating the response to immunotherapy. In conclusion, our study deepened the understanding of the TME, and it provides new ideas for improving patients' response to immunotherapy and promoting individualized tumor immunotherapy in the future.

Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

Combining targeted sequencing and ultra-low-pass whole-genome sequencing for accurate somatic copy number alteration detection

This study investigated the feasibility of combining targeted sequencing and ultra-low-pass whole-genome sequencing (ULP-WGS) for improved somatic copy number alteration (SCNA) detection, due to its role in tumorigenesis and prognosis. Cerebrospinal fluid and matched blood samples were obtained from 29 patients with brain metastasis derived from lung cancer. Samples were subjected to targeted sequencing (genomic coverage: 300 kb) and 2×ULP-WGS. The SCNA was detected by the CTLW_CNV, Control-FreeC, and CNVkit methods and their accuracy was analyzed. Eighteen tumor samples showed consistent SCNA results between the three methods, while a small fraction of samples resulted in different SCNA estimations. Further analysis indicated that consistency of SCNA highly correlated with the difference of baseline depth (normalized depth of regions without SCNA events) estimation between methods. Conflict Index showed that CTLW_CNV significantly improved the accuracy of SCNA detection through precise baseline depth estimation. CTLW_CNV combines targeted sequencing and ULP-WGS for improved SCNA detection. The improvement in detection accuracy is mainly due to a refined baseline depth estimation, guided by single-nucleotide polymorphism allele frequencies within the deeply sequenced region (targeted sequencing). This method is especially suitable for tumor samples with biased aneuploidy, a previously under-estimated genomic characteristic across different cancer types.

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Simple Summary

Bladder cancer (BC) development is highly related to immune cell infiltration. In this study, we aimed to construct a new classification of bladder cancer molecular subtypes based on immune-cell-associated CpG(Methylation) sites. The classification was accurate and stable. BC patients were successfully divided into three subtypes based on the immune-cell-associated CpG sites. The clinicopathologic features, distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN_respons, Type II IFN response, and DNA stemness score (DNAss) presented significant differences among the three subgroups. The specific genomic alteration was also different across subgroups. High-level immune infiltration showed a correlation with high-level methylation. A lower RNA stemness score (RNAss) was associated with higher immune infiltration. Cluster 2 demonstrated a better response to chemotherapy. The anti-cancer targeted drug therapy results are different among the three subgroups.

Abstract

Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.