Identification of Predictive Biomarkers for Lymph Node Involvement in Obese Women With Endometrial Cancer

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

PURPOSE

This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

METHODS

Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

RESULTS

We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

CONCLUSION

We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

Adherence to ESGO guidelines and impact on survival in obese patients with endometrial cancer: a multicentric retrospective study

OBJECTIVES

Obesity is known to be both a major risk factor for endometrial cancer and associated with surgical complexity. Therefore, the management of patients with obesity is a challenge for surgeons and oncologists. The aim of this study is to assess the adherence to European Society of Gynaecological Oncology (ESGO) guidelines in morbidly obese patients (body mass index (BMI) >40 kg/m2). The secondary objectives were the impact on overall survival and recurrence-free survival.

METHODS

All the patients who were treated for an endometrial cancer in the 11 cancer institutes of the FRANCOGYN group were included and classified into three weight groups: morbid (BMI >40 kg/m2), obese (BMI 30-40), and normal or overweight (BMI <30). Adherence to guidelines was evaluated for surgical management, lymph node staging, and adjuvant therapies.

RESULTS

In total, 2375 patients were included: 1330 in the normal or overweight group, 763 in the obese group, and 282 in the morbid group. The surgical management of the morbid group was in accordance with the guidelines in only 30% of cases, compared with 44% for the obese group and 48% for the normal or overweight group (p<0.001); this was largely because of a lack of lymph node staging. Morbid group patients were more likely to receive the recommended adjuvant therapy (61%) than the obese group (52%) or the normal or overweight group (46%) (p<0.001). Weight had no impact on overall survival (p=0.6) and morbid group patients had better recurrence-free survival (p=0.04).

CONCLUSION

Adherence to international guidelines for surgical management is significantly lower in morbid group patients, especially for lymph node staging. However, morbidly obese patients had more often the adequate adjuvant therapies. Morbid group patients had a better recurrence-free survival likely because of better prognosis tumors.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Potential Candidate Genes for Therapeutic Targeting in Chronic Myeloid Leukemia: A Pilot Study

BACKGROUND

Chronic myeloid leukemia (CML) is a prevalent hematological malignancy known for the presence of the Philadelphia chromosome and activation of the BCR-Abl kinase activity. Although tyrosine kinase inhibitors are widely used as the standard treatment, resistance remains a concern among certain patients. This study aimed to investigate the gene expression profile of a group of CML patients in comparison to a control group in order to identify novel candidate genes associated with the disease.

METHODS

Whole transcriptome sequencing was performed, and gene expression levels were validated using quantitative real-time PCR. Additionally, single nucleotide and insertion/deletion variants were analyzed in the selected candidate genes among 10 CML patients and 4 healthy control subjects.

RESULTS

Analysis revealed a set of differentially expressed genes, whose up- or downregulation was further confirmed by qRT-PCR. Among the upregulated genes in the patient group were ribosomal protein like (RPL) members, specifically RPL9, RPL34, RPL36A, and RPL39, while downregulation was observed in CCDC170, LDB1, and SBF1 compared to the healthy subjects. Furthermore, gene variant studies identified novel genetic changes in these candidate genes, suggesting potential clinical significance in CML.

CONCLUSIONS

This study highlights RPL9, RPL34, RPL36A, RPL39, CCDC170, LDB1, and SBF1 as potential targets in CML. Additionally, it underscores the importance of investigating these genes and their variants in larger cohort studies to assess their clinical significance in CML patients.

Genetic alterations in juvenile cervical clear cell adenocarcinoma unrelated to human papillomavirus

Clear cell adenocarcinoma of the cervix (CCAC) is a special type of HPV-independent cervical cancer. It has a low incidence rate, can be difficult to diagnose early, has a poor prognosis. Its peak incidence is in adolescence, which poses a great threat to women's health. Therefore, it is very important to explore the pathogenesis of cervical clear cell adenocarcinoma to guide subsequent treatment and prevention. This study analyzed 3 juvenile patients with CCAC diagnosed at the First Affiliated Hospital of Zhengzhou University. Using next-generation sequencing methods, we analyzed the pathogenesis of the patients and their close relatives by analyzing the genetic alterations of patients. CMTM5 was identified as the only shared mutated gene. Using published literature and comparative analyses of related disease-causing genes, 6 of the 19 genes (ALKBH7, MYCBP, MZF1, RNF207, RRS1, and TUSC2) were screened as genes with mutations in patients and had higher mutation rates in reproductive cancers. Pathway analysis showed that downregulated genes in non-HPV cervical cancer were mainly related to the immune system response, suggesting that non-HPV cervical cancer differs from HPV-infected cervical cancer in that the immune response is weaker, which is consistent with the weak correlation with viral infection.

In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers

BACKGROUND

Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method.

AIM

This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers.

METHODS

Publicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets.

RESULTS

A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets.

CONCLUSIONS

The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.

Identification of bicalutamide resistance-related genes and prognosis prediction in patients with prostate cancer

Background

Prostate cancer (PCa) is the second most common type of cancer and the fifth leading cause of cancer-related death in men. Androgen deprivation therapy (ADT) has become the first-line therapy for inhibiting PCa progression; however, nearly all patients receiving ADT eventually progress to castrate-resistant prostate cancer. Therefore, this study aimed to identify hub genes related to bicalutamide resistance in PCa and provide new insights into endocrine therapy resistance.

Methods

The data were obtained from public databases. Weighted correlation network analysis was used to identify the gene modules related to bicalutamide resistance, and the relationship between the samples and disease-free survival was analyzed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed, and hub genes were identified. The LASSO algorithm was used to develop a bicalutamide resistance prognostic model in patients with PCa, which was then verified. Finally, we analyzed the tumor mutational heterogeneity and immune microenvironment in both groups.

Results

Two drug resistance gene modules were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that both modules are involved in RNA splicing. The protein-protein interaction network identified 10 hub genes in the brown module LUC7L3, SNRNP70, PRPF3, LUC7L, CLASRP, CLK1, CLK2, U2AF1L4, NXF1, and THOC1) and 13 in the yellow module (PNN, PPWD1, SRRM2, DHX35, DMTF1, SALL4, MTA1, HDAC7, PHC1, ACIN1, HNRNPH1, DDX17, and HDAC6). The prognostic model composed of RNF207, REC8, DFNB59, HOXA2, EPOR, PILRB, LSMEM1, TCIRG1, ABTB1, ZNF276, ZNF540, and DPY19L2 could effectively predict patient prognosis. Genomic analysis revealed that the high- and low-risk groups had different mutation maps. Immune infiltration analysis showed a statistically significant difference in immune infiltration between the high- and low-risk groups, and that the high-risk group may benefit from immunotherapy.

Conclusion

In this study, bicalutamide resistance genes and hub genes were identified in PCa, a risk model for predicting the prognosis of patients with PCa was constructed, and the tumor mutation heterogeneity and immune infiltration in high- and low-risk groups were analyzed. These findings offer new insights into ADT resistance targets and prognostic prediction in patients with PCa.

High MutS homolog 2 expression predicts poor prognosis and is related to immune infiltration in endometrial carcinoma

Several studies have shown that MutS homolog 2 (MSH2) is highly expressed in many cancer tissues. Transcriptome expression data were collected from the Cancer Genome Atlas (TCGA) database. We analyzed the expression of MSH2 in normal and tumor tissues, the relationship between MSH2 expression and various prognostic factors, and the relationship between MSH2 expression and overall survival, disease specific survival, and progression free interval. We also examined MSH2 promoter methylation between endometrial cancer and normal endometrial tissues, and identified the prognostic value of MSH2 methylation in endometrial cancer. MSH2 was highly expressed in endometrial cancer tumor tissues compared with normal tissues. High MSH2 expression might be an independent prognostic factor for OS, DSS, and PFI. Further, high MSH2 expression was correlated with age and histological type, but not with BMI, clinical stage, tumor invasion, or other clinical features. MSH2 promoter methylation in endometrial cancer was significantly lower than in normal tissues. Additionally, MSH2 levels, OS, DSS, and PFI were associated with BMI, age, tumor invasion, and histological type. ssGSEA showed that MSH2 expression was positively correlated with the infiltration of Th2 cells, Tcm cells, T helper cells, and Tgd cells, whereas it was negatively correlated with NK CD56 bright cells, pDC cells, iDC cells, cytotoxic cells, and neutrophils. Increased MSH2 expression and reduced MSH2 methylation in endometrial cancer predicts poor prognosis. MSH2 may be used as a biomarker for the diagnosis and prognosis of endometrial cancer and as an immunotherapy target.

A comprehensive review of the progress of cell migration inducing hyaluronidase 1

The gene cell migration inducing hyaluronidase 1 (CEMIP) is on chromosome 15q25 and codes for a 150-kDa protein with an N-terminal secretion signal, a G8 domain, 2 GG domains, and several repeats. It was first described as a specific protein in the inner ear relating to nonsyndromic hearing loss. Recently, increasing research detected its association in various cancers, determining the progression, metastasis, and prognosis by influencing the proliferation and invasion of the cells. This relation is accomplished through various interacting pathways, such as the Wnt/β-catenin signaling pathway and the epidermal growth factor receptor signaling pathway. Thus, CEMIP could be a novel and potential focus for tumor diagnosis and treatment, but further studies on the regulatory role of CEMIP in vivo and in vitro are still needed. Herein, we summarize the process in recent studies of CEMIP, especially in cancer research.

A Cell Differentiation Trajectory-Related Signature for Predicting the Prognosis of Lung Adenocarcinoma

Objective

To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD).

Methods

LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis.

Results

Two subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy.

Conclusion

The cell differentiation-related signature and the prognostic nomogram could accurately predict survival.