A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

A Novel Transcriptomic Signature for Prediction of Response to Adjuvant Chemotherapy in Patients With Stages II and III Gastric Cancer

BACKGROUND

Predicting patients who will benefit from postoperative adjuvant chemotherapy is crucial for precision medicine. Therefore, this study comprehensively analyzed messenger RNA (mRNA) expression profiles to identify novel biomarkers and developed a prediction signature for postoperative adjuvant chemotherapy in patients with gastric cancer (GC).

METHODS

Biomarkers were discovered by analyzing two publicly available genome-wide datasets from 343 patients with pathologic stages (pStages) II and III GC. A novel prediction signature was developed based on a quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay using 137 pStages II and III GC frozen tissue specimens.

RESULTS

Nine novel mRNAs were identified as candidate biomarkers in biomarker discovery, and a Gene Expression-based ADJuvant chemotherapy Response prediction for stages II and III GC (GEx-ADJ-Res) signature was developed using these candidate biomarkers and key clinicopathologic features by qRT-PCR assay. The GEx-ADJ-Res signature robustly predicted postoperative recurrence in clinical tissue samples (area under the curve [AUC], 0.84). The signature demonstrated sufficient potential for predicting response to postoperative adjuvant chemotherapy (AUC, 0.82) and was shown to be an independent predictor of postoperative recurrence and survival in multivariate analysis. Finally, the GEx-ADJ-Res signature was successfully validated using independent multi-institutional datasets (AUC, 0.91, 0.85, and 0.78, respectively).

CONCLUSIONS

We identified the novel mRNA biomarkers and developed a novel signature that allowed robust prediction of response to postoperative adjuvant chemotherapy in patients with GC. This signature could become a precision medicine tool in GC treatment.

Fatty acid metabolism-related risk signature revealing the immune landscape of neuroblastoma and predicting overall survival in pediatric neuroblastoma patients

BACKGROUND

Tumor metabolic reprogramming is a hallmark in cancer cells, wherein fatty acid metabolism assumes a pivotal role in energy supply and the provision of diverse biosynthetic precursors. However, there is a lack of systematic analysis regarding the impact of fatty acid metabolism on prognosis in neuroblastoma (NB) patients and its influence on the immune microenvironment.

METHODS

We acquired RNA expression profiles and corresponding clinical-pathological information for NB patients from the Gene Expression Omnibus, ArrayExpress, and TARGET databases. The GSE49710 cohort was utilized as a training set, whereas E-MTAB-8248 and the TARGET cohorts served as testing sets. Consensus clustering was employed to identify molecular subtypes based on fatty acid metabolism. Independent prognostic genes were pinpointed using LASSO-Cox analysis, which facilitated the development of a novel risk signature that was subsequently validated using the testing sets. We then proceeded to analyze the predictive power of the risk signature for prognosis, its correlation with clinical-pathological features, the immune landscape, and drug sensitivity.

RESULTS

In the consensus clustering analysis, patients in the training set were segregated into two clusters. Cluster 2 exhibiting significantly poorer overall survival (OS) compared to cluster 1. Moreover, cluster 2 was markedly associated with clinical-pathological features indicative of poor prognosis. Following this, univariate Cox regression analysis revealed 207 fatty acid metabolism genes (FMGs) correlated with patient OS. A risk signature based on 35 FMGs was constructed using LASSO-Cox regression analysis, demonstrating significant predictive accuracy and discrimination in both the training and testing sets. The risk signature emerged as an independent prognostic factor and was integrated with multiple clinical-pathological features to develop a nomogram. In the immune landscape analysis, the high-risk group displayed a compromised antigen presentation mechanism, reduced infiltration levels of various immune cells, and escaping of CD8 + T cells and NK cells. Additionally, different risk groups could exhibit different responsiveness to immune checkpoint inhibitors. Lastly, potential chemotherapeutic agents for each risk group were predicted.

CONCLUSION

The novel risk signature, derived from FMGs, demonstrated promising efficacy in predicting the prognosis of NB patients, elucidating their immune landscape, and guiding therapeutic strategies.

A PANoptosis-Based Signature for Survival and Immune Predication in Glioblastoma Multiforme

OBJECTIVE

PANoptosis is a concept of total cell death characterized by pyroptosis, apoptosis, and necroptosis. We aimed to explore the clinical significance of PANoptosis-related genes (PARGs) in glioblastoma multiforme (GBM).

METHODS

Expression profiles of GBM were downloaded from the XENA database as a training dataset to construct a differentially expressed PARGs (DE-PARGs)-based risk score (RS) model, and the prognostic prediction role was validated in the CGGA database and GSE108474 using Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve. Meanwhile, independent prognostic clinical factors were screened, and their prognosis predictive activity was evaluated by a nomogram model. Furthermore, the relationships between key DE-PARGs and immune cell infiltration, as well as chemotherapy drug sensitivity were analyzed.

RESULTS

The RS model consisting of five DE-PARGs was constructed, including NOD2, NLRP2, NLRP7, GATA3, and TERT. ROC and KM curves confirmed the good potency of the RS prognostic model both in XENA database and GSE108474. Three clinical prognostic factors, including chemotherapy, pharmaceutical therapy, and RS model, were selected as individual prognostic factors. The nomogram model showed RS contributed most to survival probability, followed by chemotherapy and pharmaceutical therapy. In high- and low-risk groups, B cell memory, NK cell resting, and macrophage M1 had significant differences. As compared with the immune checkpoint therapy non-responder group, the responder involved a higher ratio of patients sub-grouped into the low-risk group. Three drugs between high- and low-risk groups had significant differences, including Cisplatin, Gefitinib, and Vorinostat.

INTERPRETATION

Our data exhibit the prognostic value of PARGs in GBM and offer new insights for GBM pathogenesis and immune treatment.

Construction and evaluation of a prognostic model based on the expression of the metabolism-related signatures in patients with osteosarcoma

BACKGROUND

The aim of this study was to screen three major substance metabolism-related genes and establish a prognostic model for osteosarcoma.

METHODS

RNA-seq expression data for osteosarcoma were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases. Differentially expressed (DE) RNAs were selected, followed by the selection of metabolic-related DE mRNAs. Using Cox regression analysis, prognostic DE RNAs were identified to construct a prognostic model. Subsequently, independent prognostic clinical factors were screened, and the functions of the long non-coding RNAs (lncRNAs) were analyzed. Finally, the expression of signature genes was further tested in osteosarcoma cells using quantitative reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting.

RESULTS

A total of 432 DE RNAs, comprising 79 DE lncRNAs and 353 DE mRNAs were obtained, and then 107 metabolic-related DE mRNAs. Afterwards signature genes (LINC00545, LINC01537, FOXC2-AS1, CYP27B1, PFKFB4, PHKG1, PHYKPL, PXMP2, and XYLB) served as optimal combinations, and a prognostic score model was successfully proposed. Three verification datasets (GSE16091, GSE21257, and GSE39055) showed that the model had high specificity and sensitivity. In addition, two independent prognostic clinical factors (age and tumor metastasis) were identified. Finally, the concordance rate between the in silico analysis, qRT-PCR, and western blotting analysis was 88.89% (8/9), suggesting the robustness of our analysis.

CONCLUSIONS

The prognostic model based on the nine signature genes accurately predicted the prognosis of patients with osteosarcoma; CYP27B1, PFKFB4, PHKG1, PHYKPL, PXMP2, and XYLB may serve as metabolism-related biomarkers in osteosarcoma.

Identification and validation of the TRHDE-AS1/hsa-miR-449a/ADAMTS5 axis as a novel prognostic biomarker in prostate cancer

Despite advancements in cancer research, the prognostic implications of competing endogenous RNA (ceRNA) networks in prostate cancer (PCa) remain incompletely understood. This study aimed to elucidate the prognostic relevance of ceRNA networks in PCa, utilizing a comprehensive bioinformatics approach alongside experimental validation. After searching The Cancer Genome Atlas (TCGA) database, RNA sequencing (RNA-Seq) data were extracted to identify differentially expressed RNAs (DERs) between 491 PCa samples and 51 normal prostate tissues, following which a comprehensive bioinformatics strategy was implemented to construct a ceRNA network. An optimal prognostic signature comprising these DERs was then established and validated using TCGA data. In addition, functional validation was performed through RNA pull-down, dual-luciferase reporter assays, quantitative real-time PCR, and western blot analysis conducted in PC-3 and DU145 cell lines, thereby complementing the bioinformatics analysis. A total of 613 DERs, comprising 103 long noncoding RNAs (lncRNAs), 60 microRNAs (miRNAs), and 450 messenger RNAs (mRNAs), were identified and utilized in constructing a ceRNA network, which encompassed 23 lncRNAs, 9 miRNAs, and 52 mRNAs. An optimal prognostic signature was established, including VPS9D1 antisense RNA 1 (VPS9D1-AS1), miR-449a, cyclin-dependent kinase 5 regulatory subunit 1 (CDK5R1), targeting protein for Xklp2 (TPX2), solute carrier family 7 member 11 (SLC7A11), copine7 (CPNE7), and maternal embryonic leucine zipper kinase (MELK), yielding area under the curve (AUC) values exceeding 0.8 across training, validation, and entire datasets. Our experiments results revealed an interaction between lncRNA TRHDE antisense RNA 1 (TRHDE-AS1) and miR-449a and that miR-449a could target the ADAM metallopeptidase with thrombospondin type 1 motif 5 (ADAMTS5) mRNA. Knockdown of miR-449a significantly impeded cell proliferation, G1/S transition, migration and invasion, and promoted apoptosis in PC-3 and DU145 cells. Furthermore, knockdown of miR-449a notably downregulated protein expression of CDK4, cyclin D1, N-cadherin and vimentin, while upregulating protein expression of cleaved caspase-3 and E-cadherin. This study contributes to a deeper understanding of the prognostic-linked ceRNA network in PCa, providing fundamental insights that could improve diagnostic and therapeutic approaches for PCa management.

The RAAS system SNPs polymorphism is associated with essential hypertension risk in rural areas in northern China

Objectives: Epidemiological evidence has shown that genetics and environment are associated with the risk of hypertension. However, the specific SNP effects of a cluster of crucial genes in the RAAS system on the risk of hypertension are unclear. Methods: A case-control study was performed on the baseline participants of Environment and Chronic Disease in Rural Areas of Heilongjiang China (ECDRAHC) study. According to the inclusion and exclusion criteria, 757 subjects (428 hypertensive patients) were enrolled. A total of 32 SNP sites and related haplotypes, involved in AGT (angiotensinogen), ACE (angiotensin-converting enzyme), AGTR1, CYP11B2 (aldosterone-synthase), LDLR (low-density lipoprotein receptor), LRP5 (low-density lipoprotein receptor associated protein 5), LRP6 (low-density lipoprotein receptor associated protein 6), PPARG (peroxisome proliferator-activated receptor gamma) and ACE2 (angiotensin-converting enzyme 2) genes which exert important roles in renin-angiotensin-aldosterone system (RAAS) system were analyzed. Furthermore, a polygenic scoring model was established to assess individual risk of developing hypertension based on the comprehensive SNPs effects in genes related the RAAS system. Results: After controlling the impact of confounding factors, multivariate logistic regression analysis revealed that the distribution of AGT/rs5046, LRP6/rs12823243 and ACE2/rs2285666 was associated with susceptibility to essential hypertension. In genetic score model, the score > -0.225 had a higher risk, the OR (95%CI) was 1.229 (1.110, 1.362). Conclusions: To the best of our knowledge, this is the first time a hypertension risk scoring model on RAAS associated gene cluster has been constructed, which will provide a novel approach for prevention and control of essential hypertension.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model

Background

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis.

Methods

LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power.

Results

Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses.

Conclusions

This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

Identification of a prognosis-related phagocytosis regulator gene signature in medulloblastoma

Objectives

The aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes.

Methods

Differentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed.

Results

In total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages.

Conclusion

Ten key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

Ferroptosis-related gene signature and clinical prognostic factors as prognostic marker for colon adenocarcinoma

Aim

To build a ferroptosis-related prognostic model for patients with colon adenocarcinoma (COAD).

Methods

COAD expression profiles from The Cancer Genome Atlas were used as the training set and GSE39582 from Gene Expression Omnibus as the validation set. Differentially expressed ferroptosis-related genes between patients with COAD and normal controls were screened, followed by tumor subtype exploration based on ferroptosis-related gene expression levels. A ferroptosis score (FS) model was constructed using least absolute shrinkage and selection operator penalized Cox analysis. Based on FS, patients were subgrouped into high- and low-risk subgroups and overall survival was predicted. The potential prognostic value of the FS model and the clinical characteristics were investigated using receiver operating characteristic curves.

Results

Twenty-four differentially expressed ferroptosis-related genes were identified, four of which (CYBB, PRNP, ACSL4, and ACSL6) were included in the prognostic signature. Moreover, age, pathological T stage, and tumor recurrence were independent prognostic factors for COAD. The FS model combined with three independent prognostic factors showed the best prognostic value (The Cancer Genome Atlas: area under the curve = 0.897; GSE39582: area under the curve = 0.858).

Conclusion

The novel prognostic model for patients with COAD constructed by pairing the FS model with three important independent prognostic factors showed promising clinical predictive value.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Predictive Role of a Novel Ferroptosis-Related lncRNA Pairs Model in the Prognosis of Papillary Thyroid Carcinoma

This study aimed to evaluate the potential prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid carcinoma (PTC). Based on The TCGA database, lncRNAs and ferroptosis-related genes with differential expression levels in PTC tumors vs. normal tissues were screened. After the co-expression network construction, ferroptosis-related lncRNAs (FRLs) were screened. Kaplan-Meier analysis was conducted to compare the survival performance of patients with PTC in the high- and low-risk groups. Furthermore, a nomogram was created to enhance PTC prognosis. CIBERSORT was used to investigate the infiltration of various immune cells in high- and low-risk groups. In total, 10 lncRNA pairs with differential expression levels were obtained. There were significant differences in the histological subtype and pathological stage between the high- and low-risk groups, and age (P = 7.39E-13) and FRLM model status (P = 1.09E-04) were identified as independent prognostic factors. Subsequently, the nomogram survival model showed that the predicted one-, three-, and five-year survival rates were similar to the actual one- (c-index = 0.8475), three- (c-index = 0.7964), and five-year (c-index = 0.7555) survival rates. Subjects in the low-risk group had significantly more CD4 + memory T cells and resting myeloid dendritic cells, and subjects in the high-risk group had more plasma B cells and monocytes. The risk assessment model constructed using FRLs showed good predictive value for the prognosis of patients with PTC.

Biomarker discovery and validation for gastrointestinal tumors: A comprehensive review of colorectal, gastric, and liver cancers

Gastrointestinal (GI) malignancies, encompassing gastric, hepatic, colonic, and rectal cancers, are prevalent forms of cancer globally and contribute substantially to cancer-related mortality. Although there have been improvements in methods for diagnosing and treating GI cancers, the chances of survival for these types of cancers are still extremely low. According to the World Cancer Research International Fund's most recent figures, stomach cancer was responsible for roughly one million deaths worldwide in 2020. This emphasizes the importance of developing more effective tools for detecting, diagnosing, and predicting the outcome of these cancers at an early stage. Biomarkers, quantitative indications of biological processes or disease states, have emerged as promising techniques for enhancing the diagnosis and prognosis of GI malignancies. Recently, there has been a considerable endeavor to discover and authenticate biomarkers for various GI cancers by the utilization of diverse methodologies, including genomics, proteomics, and metabolomics. This review provides a thorough examination of the current state of biomarker research in the field of gastrointestinal malignancies, with a specific emphasis on colorectal, stomach, and liver cancers. A thorough literature search was performed on prominent databases such as PubMed, Scopus, and Web of Science to find pertinent papers published until November, 2023 for the purpose of compiling this review. The diverse categories of biomarkers, encompassing genetic, epigenetic, and protein-based biomarkers, and their potential utility in the fields of diagnosis, prognosis, and treatment selection, are explored. Recent progress in identifying and confirming biomarkers, as well as the obstacles that persist in employing biomarkers in clinical settings are emphasized. The utilization of biomarkers in GI cancers has significant potential in enhancing patient outcomes. Ongoing research is expected to uncover more efficient biomarkers for the diagnosis and prognosis of these cancers.

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

PURPOSE

This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD).

METHODS

PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan-Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes.

RESULTS

The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine-cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration.

CONCLUSIONS

This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

Mitophagy-related gene signature for predicting the prognosis of multiple myeloma

Aims

The aims of this study were to explore the molecular mechanism of mitophagy in multiple myeloma (MM) and to develop an effective prognostic signature for the disease based on mitophagy-related genes (MRGs).

Methods

Three gene sets from the Reactome database were used to explore MRGs, following which those that were differentially expressed between MM and normal samples were investigated using the data from the Genomic Data Commons-Multiple Myeloma Research Foundation-CoMMpass Study. Mitophagy-related molecular subtypes of MM were identified and their immune infiltration, associated patient survival rates, immune checkpoint genes, and mitophagy scores were compared. Prognostic genes for MM were identified, and a prognostic model was constructed. Additionally, a nomogram was constructed using the prognostic model and prognosis-related clinical features. Finally, the drug sensitivity and correlation analyses of the subtypes were performed between the two risk groups.

Results

We identified two MM molecular subtypes that exhibited significant differences in mitophagy scores, associated patient survival rates, immune infiltration, and immune checkpoint genes. An MRG-based prognostic signature was constructed using six genes (TRIP13, KIF7, GPR63, CRIP2, DNTT, and HSPB8), which had high predictive prognostic value. A nomogram was constructed by screening five indicators (risk score, subtype, age, sex, and stage) that could predict the 1-, 3-, and 5-year survival probabilities of patients with MM. The two risk groups displayed significant differences in their IC50 values of 33 drugs, such as bleomycin. Patients in the high-risk group tended to fall within Mitophagy_cluster_A.

Conclusion

Our MRG-based signature is a promising prognostic biomarker for MM.

Exploring the mechanism of LncRNA CASC15 affecting hepatocellular carcinoma through miRNA

This study aimed to determine the potential mechanisms through which long noncoding (Lnc) RNA cancer susceptibility candidate 15 (CASC15) affects hepatocellular carcinoma (HCC). We retrieved HCC RNA-seq and clinical information from the UCSC Xena database. The differential expression (DE) of CASC15 was detected. Overall survival was analyzed using Kaplan-Meier (K-M) curves. Molecular function and signaling pathways affected by CASC15 were determined using Gene Set Enrichment Analysis. Associations between CASC15 and the HCC microenvironment were investigated using immuno-infiltration assays. A differential CASC15-miRNA-mRNA network and HCC-specific CASC15-miRNA-mRNA ceRNA network were constructed. The overexpression of CASC15 in HCC tissues was associated with histological grade, clinical stage, pathological T stage, poor survival, more complex immune cell components, and 12 immune checkpoints. We identified 27 DE miRNAs and 270 DE mRNAs in the differential CASC15-miRNA-mRNA network, and 10 key genes that were enriched in 12 cancer-related signaling pathways. Extraction of the HCC-specific CASC15-miRNA-mRNA network revealed that IGF1R, MET, and KRAS were associated with HCC progression and occurrence. Our bioinformatic findings confirmed that CASC15 is a promising prognostic biomarker for HCC, and elevated levels in HCC are associated with the tumor microenvironment. We also constructed a disease-specific CASC15-miRNA-mRNA regulatory ceRNA network that provides a new perspective for the precise indexing of patients with elevated levels of CASC15.

Immune Cell Infiltration Types as Biomarkers for the Recurrence Diagnosis and Prognosis of Bladder Cancer

This study aimed to investigate the role of infiltrating immune cell types in diagnosing and predicting bladder cancer recurrence. This study mainly applied some algorithms, including Estimate the Proportion of Immune and Cancer Cells (EPIC), support vector machine-recursive feature elimination (SVM-RFE), random forest out-of-bag (RF-OOB) and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. We found six immune infiltrating cell types significantly associated with recurrence prognosis and two independent clinical prognostic factors. Infiltrating immune cell types (IICTs) based on the prognostic immune risk score (pIRS) models may provide significant biomarkers for the diagnosis and prognostic prediction of bladder cancer recurrence.

Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients

BACKGROUND/AIMS

The purpose of this study is to screen the feature genes related to gut microflora and explore the role of the genes in predicting the prognosis of patients with gastric cancer.

MATERIALS AND METHODS

We downloaded the gene profile of gastric cancer from the University of California Santa Cruz, the gut microflora related to gastric cancer from The Cancer Microbiome Atlas. The GSE62254 dataset was downloaded from National Center for Biotechnology Information Gene Expression Omnibus as a validation dataset. A correlation network between differentially expressed genes and gut microflora was constructed using Cytoscape. The optimized prognostic differentially expressed genes were identified through least absolute shrinkage and selection operator (LASSO) algorithm and univariate Cox regression analysis. The risk score model was established and then measured via Kaplan-Meier and area under the curve. Finally, the nomogram model was constructed according to the independent clinical factors, which was evaluated using C-index.

RESULTS

A total of 754 differentially expressed genes and 8 gut microflora were screened, based on which we successfully constructed the correlation network. We obtained 9 optimized prognostic differentially expressed genes, including HSD17B3, GNG7, CHAD, ARHGAP8, NOX1, YY2, GOLGA8A, DNASE1L3, and ABCA8. Moreover, Kaplan-Meier curves indicated the risk score model correctly predicted the prognosis of gastric cancer in both University of California Santa Cruz and GSE62254 dataset (area under the curve >0.8; area under the curve >0.7). Finally, we constructed the nomogram, in which the C index of 1, 3, and 5 years was 0.824, 0.772, and 0.735 representing that the nomogram was consistent with the actual situation.

CONCLUSIONS

These results indicate the 9 differentially expressed genes related to gut microflora might predict the survival time of patients with gastric cancer. Both risk signature and nomogram could effectively predict the prognosis for patients with gastric cancer.

A Prognostic Model Based on Cisplatin-Resistance Related Genes in Oral Squamous Cell Carcinoma

PURPOSE

To screen for the cisplatin resistance-related prognostic signature in oral squamous cell carcinoma (OSCC) and assess its correlation with the immune microenvironment.

MATERIALS AND METHODS

The gene expression data associated with OSCC and cisplatin-resistance were downloaded from TCGA and GEO databases. Cisplatin-resistant genes were selected through taking the intersection of differentially expressed genes (DEGs) between tumor and control groups as well as between cisplatin-resistant samples and parental samples. Then, prognosis-related cisplatin-resistant genes were further selected by univariate Cox regression and LASSO regression analyses to construct a survival prognosis model. A GSEA (gene set enrichment analysis) between two risk groups was conducted with the MSigDB v7.1 database. Finally, the immune landscape of the sample was studied using CIBERSORT. The IC50 values of 57 drugs were predicted using pRRophetic 0.5.

RESULTS

A total 230 candidate genes were obtained. Then 7 drug-resistant genes were selected for prognostic risk-score (RS) signature construction using LASSO regression analysis, including STC2, TBC1D2, ADM, NDRG1, OLR1, PDGFA and ANO1. RS was an independent prognostic factor. Additionally, a nomogram model was established to predict the 1-, 2-, and 3-year survival rates of samples. The GSEA identified several differential pathways between two risk groups, such as EMT, hypoxia, and oxidative phosphorylation. Fifteen immune cells were statistically significantly different in infiltration level between the two groups, such as macrophages M2, and resting NK cells. A total of 57 drugs had statistically significantly different IC50 values between two risk groups.

CONCLUSION

These model genes and immune cells may play a role in predicting the prognosis and chemoresistance in OSCC.

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps

Purpose

This study identified subtypes and prognostic signature of stage I and stage II gastric cancer based on neutrophil extracellular trap (NET)-related genes.

Methods

The gene expression data associated with stage I and stage II gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. NET-related genes were obtained from previous reference. Differentially expressed NET-related genes were selected by consensus cluster analysis. The differences in immune infiltration between two subtypes were analyzed. Prognosis-related genes were further screened by univariate Cox regression analysis. Gene Set Enrichment Analysis (GSEA) of prognostic signatures was conducted with clusterprofiler. Finally, a miRNA-mRNA-transcription factor (TF) network was constructed.

Results

Total 43 differential NET-related genes were obtained and two subtypes were obtained based on these genes. Patients of cluster 2 had a better prognosis compared to cluster 1. Eight types of immune cells were differential in infiltration level between two subtypes. Following univariate Cox regression analysis, two genes of CXC chemokine receptor 4 (CXCR4) and nuclear factor, erythroid 2-like 2 (NFE2L2) significantly related to patient survival were selected. GSEA of single gene revealed that CXCR4 was associated with allograft rejection and NFE2L2 was associated with drug metabolism-cytochrome P450. A network with 421 miRNA-mRNA-TF regulatory pairs was constructed.

Conclusion

The present study identified two subtypes and a prognostic signature for stage I and stage II gastric cancer based on NET-related genes.

Identification of Metabolism-Related Prognostic Biomarkers and Immune Features of Head and Neck Squamous Cell Carcinoma

We aimed to identify an effective metabolic subtype and risk score to predict survival and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). Data were obtained from an online database. We screened significant prognostic metabolism-related genes between the normal and tumor groups using a series of bioinformatics methods. Based on the selected prognostic genes, we conducted a subtype analysis to identify significantly different subtypes in HNSCC. We then investigated survival, immune features, and hallmark differences among different subtypes. LASSO was utilized to identify optimal genes for the risk score model construction. Finally, distribution of the risk score samples was analyzed for different subtypes. A total of 32 significantly prognostic metabolism-related genes were screened, and all samples were grouped into two subtypes: cluster 1 and cluster 2. Cluster 1 had worse survival. Different immune cell infiltration (CD8 T cells, macrophages, and regulatory T cells) and immune checkpoint gene expression (PD-1 and CLAT-4) were observed between the two clusters. Twelve optimal genes were involved in risk score model, and high-risk group had poorer survival. Cluster 1 contained more high-risk samples (60%). Finally, four genes CAV1, GGT6, PYGL, and HS3ST1 were identified as significantly related to immune cells, and these genes were differentially expressed in the normal oral epithelial cells and HNSCC cells. The subtypes and risk score model in the study provide a promising biomarker for prognosis and immunotherapy response.

Screening of Immune-related lncRNAs in Lung Adenocarcinoma and Establishing a Survival Prognostic Risk Prediction Model

OBJECTIVE

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs).

METHODS

LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples.

RESULTS

In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples.

CONCLUSION

We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

A Comprehensive Prognostic Model for Colon Adenocarcinoma Depending on Nuclear-Mitochondrial-Related Genes

Background: Colon adenocarcinoma (COAD) has increasing incidence and is one of the most common malignant tumors. The mitochondria involved in cell energy metabolism, oxygen free radical generation, and cell apoptosis play important roles in tumorigenesis and progression. The relationship between mitochondrial genes and COAD remains largely unknown. Methods: COAD data including 512 samples were set out from the UCSC Xena database. The nuclear mitochondrial-related genes (NMRGs)-related risk prognostic model and prognostic nomogram were constructed, and NMRGs-related gene mutation and the immune environment were analyzed using bioinformatics methods. Then, a liver metastasis model of colorectal cancer was constructed and protein expression was detected using Western blot assay. Results: A prognostic model for COAD was constructed. Comparing the prognostic model dataset and the validation dataset showed considerable correlation in both risk grouping and prognosis. Based on the risk score (RS) model, the samples of the prognostic dataset were divided into high risk group and low risk group. Moreover, pathologic N and T stage and tumor recurrence in the two risk groups were significantly different. The four prognostic factors, including age and pathologic T stage in the nomogram survival model also showed excellent predictive performance. An optimal combination of nine differentially expressed NMRGs was finally obtained, including LARS2, PARS2, ETHE1, LRPPRC, TMEM70, AARS2, ACAD9, VARS2, and ATP8A2. The high-RS group had more inflamed immune features, including T and CD4+ memory cell activation. Besides, mitochondria-associated LRPPRC and LARS2 expression levels were increased in vivo xenograft construction and liver metastases assays. Conclusion: This study established a comprehensive prognostic model for COAD, incorporating nine genes associated with nuclear-mitochondrial functions. This model demonstrates superior predictive performance across four prognostic factors: age, pathological T stage, tumor recurrence, and overall prognosis. It is anticipated to be an effective model for enhancing the prognosis and treatment of COAD.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer

OBJECTIVES

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

METHODS

We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

RESULTS

Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

CONCLUSIONS

The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

KIF20B Correlates with LUAD Progression and Is an Independent Risk Factor

OBJECTIVE

Kinesin family proteins (KIFs) play crucial roles in human tumorigenesis and progression. This study aimed to investigate the expression and association of Kinesin family member 20B (KIF20B) with lung adenocarcinoma (LUAD).

METHODS

RNA-seq data from LUAD patients (n = 535) were extracted from TCGA. KIF20B expression was compared between tumor tissues and controls, and between different stages of the disease. Survival and Cox regression analyses were performed, as well as in vitro cellular experiments on A549 cells.

RESULTS

KIF20B is upregulated in LUAD tumor tissues compared with controls and is higher in advanced stages. Patients with high expression of KIF20B have shorter survival times. KIF20B is an independent risk factor for the prognosis of LUAD. High KIF20B expression samples were enriched in signaling pathways related to tumor progression. si-KIF20B transfection reduced migration and invasion of A549 cells and increased apoptosis. The expression of p53 and Bax proteins was upregulated by si-KIF20B, while Bcl-2 was down-regulated.

DISCUSSION

This study reveals that high KIF20B expression is an independent risk factor for the poor prognosis of LUAD. The inhibition of KIF20B might be of great value for suppressing LUAD progression.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients

BACKGROUND

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers.

METHODS

ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation.

RESULTS

WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.

CONCLUSIONS

ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

Endoplasmic reticulum stress-related signature predicts prognosis and immune infiltration analysis in acute myeloid leukemia

OBJECTIVES

To construct an endoplasmic reticulum stress-related prognostic risk score (RS) model to predict prognosis and perform a preliminary analysis of immune infiltration in patients with acute myeloid leukemia (AML).

METHODS

The whole-genome expression data for AML and endoplasmic reticulum stress (ER stress)-related genes were downloaded from the GEO and GSEA databases, respectively. The samples were divided into death and survival groups, combined with clinical prognosis information. LASSO regression was used to construct a prognostic RS model. The Kaplan-Meier curve method was used to evaluate the association between different risk groups and actual survival prognosis information. A cox regression analysis was used to screen for independent survival prognostic clinical factors and construct a nomogram. CIBERSORT and ssGSEA was used for immune-related analysis.

RESULTS

Eighteen ER-stress related genes were identified and a comprehensive network was constructed. Further, 5 CC, 8 MF, 17 BP, and 2 KEGG pathways were enriched. Ten optimal DEGs were obtained and a prognostic risk model was constructed. Compared to the low RS group, the OS values of the high RS group were significantly lower. A significant correlation between the different risk groups and the actual prognosis was demonstrated. Ten immune cells with significantly different distributions in different risk groups were screened. KEGG enrichment analysis showed that there were 5 signaling pathways in the high-risk group.

CONCLUSIONS

The RS model can effectively predict the prognosis and has clinical implications for the prognosis of AML, combined with the correlation between different RS groups and the immune microenvironment.

Comprehensive analysis of miRNA-mRNA regulatory pairs associated with colorectal cancer and the role in tumor immunity

BACKGROUND

MicroRNA (miRNA) which can act as post-transcriptional regulators of mRNAs via base-pairing with complementary sequences within mRNAs is involved in processes of the complex interaction between immune system and tumors. In this research, we elucidated the profiles of miRNAs and target mRNAs expression and their associations with the phenotypic hallmarks of colorectal cancers (CRC) by integrating transcriptomic, immunophenotype, methylation, mutation and survival data.

RESULTS

We conducted the analysis of differential miRNA/mRNA expression profile by GEO, TCGA and GTEx databases and the correlation between miRNA and targeted mRNA by miRTarBase and TarBase. Then we detected using qRT-PCR and validated the diagnostic value of miRNA-mRNA regulator pairs by the ROC, calibration curve and DCA. Phenotypic hallmarks of regulatory pairs including tumor-infiltrating lymphocytes, tumor microenvironment, tumor mutation burden, global methylation and gene mutation were also described. The expression levels of miRNAs and target mRNAs were detected in 80 paired colon tissue samples. Ultimately, we picked up two pivotal regulatory pairs (miR-139-5p/ STC1 and miR-20a-5p/ FGL2) and verified the diagnostic value of the complex model which is the combination of 4 signatures above-mentioned in 3 testing GEO datasets and an external validation cohort.

CONCLUSIONS

We found that 2 miRNAs by targeting 2 metastasis-related mRNAs were correlated with tumor-infiltrating macrophages, HRAS, and BRAF gene mutation status. Our results established the diagnostic model containing 2 miRNAs and their respective targeted mRNAs to distinguish CRCs and normal controls and displayed their complex roles in CRC pathogenesis especially tumor immunity.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

A potential prognostic prediction model for metastatic osteosarcoma based on bioinformatics analysis

Osteosarcoma (OS) is a malignant primary bone tumor with a high incidence. This study aims to construct a prognostic prediction model by screening the prognostic mRNA of metastatic OS. Data on four eligible expression profiles from the National Center for Biotechnology Information Gene Expression Omnibus repository were obtained based on inclusion criteria and defined as the training set or the validation set. The differentially expressed genres (DEGs) between meta- static and non-metastatic OS samples in the training set were first identified, and DEGs related to prognosis were screened by univariate Cox regression analysis. In total, 107 DEGs related to the prognosis of metastatic OS were identified. Then, 46 DEGs were isolated as the optimized prognostic gene signature, and a metastatic-OS discriminating classifier was constructed, which had a high accuracy in distinguishing metastatic from non-metastatic OS samples. Furthermore, four optimized prognostic gene signatures (ALOX5AP, COL21A1, HLA-DQB1, and LDHB) were further screened, and the prognostic prediction model for metastatic OS was constructed. This model possesses a relatively satisfying prediction ability both in the training set and validation set. The prognostic prediction model that was constructed based on the four prognostic mRNA signatures has a high predictive ability for the prognosis of metastatic OS.

Development of a novel, clinically relevant anoikis-related gene signature to forecast prognosis in patients with prostate cancer

Introduction: Anoikis is a specific form of programmed cell death and is related to prostate cancer (PC) metastasis. This study aimed to develop a reliable anoikis-related gene signature to accurately forecast PC prognosis. Methods: Based on anoikis-related genes and The Cancer Genome Atlas (TCGA) data, anoikis-related molecular subtypes were identified, and their differences in disease-free survival (DFS), stemness, clinical features, and immune infiltration patterns were compared. Differential expression analysis of the two subtypes and weighted gene co-expression network analysis (WGCNA) were employed to identify clinically relevant anoikis-related differentially expressed genes (DEGs) between subtypes, which were then selected to construct a prognostic signature. The clinical utility of the signature was verified using the validation datasets GSE116918 and GSE46602. A nomogram was established to predict patient survival. Finally, differentially enriched hallmark gene sets were revealed between the different risk groups. Results: Two anoikis-related molecular subtypes were identified, and cluster 1 had poor prognosis, higher stemness, advanced clinical features, and differential immune cell infiltration. Next, 13 clinically relevant anoikis-related DEGs were identified, and five of them (CKS2, CDC20, FMOD, CD38, and MSMB) were selected to build a prognostic signature. This gene signature had a high prognostic value. A nomogram that combined Gleason score, T stage, and risk score could accurately predict patient survival. Furthermore, gene sets closely related with DNA repair were differentially expressed in the different risk groups. Conclusion: A novel, clinically relevant five-anoikis-related gene signature was a powerful prognostic biomarker for PC.

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.

Construction of lncRNA-m6A gene-mRNA regulatory network to identify m6A-related lncRNAs associated with the progression of lung adenocarcinoma

BACKGROUND

We evaluated the prognostic value of m6A-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD).

METHODS

The expression levels of lncRNAs and mRNAs in LUAD and normal adjacent tissues from The Cancer Genome Atlas dataset were analyzed using the limma package. m6A enzyme-related differentially expressed lncRNAs and mRNAs were identified and used to construct a regulatory network. Survival analysis was performed and the correlation between lncRNAs, m6A regulators, and mRNAs was analyzed; followed by functional enrichment analysis.

RESULTS

A comparison of LUAD samples and normal tissues identified numerous differentially expressed lncRNAs and mRNAs, demonstrating that a comprehensive network was established. Two lncRNAs and six mRNAs were selected as prognosis related factors including SH3PXD2A-AS1, MAD2L1, CCNA2, and CDC25C. The pathological stage and recurrence status were identified as independent clinical factors (P < 0.05). The expression levels of these RNAs in the different clinical groups were consistent with those in the different risk groups. The interactions of m6A proteins, two lncRNAs, and six mRNAs were predicted, and functional analysis showed that m6A target mRNAs were involved in the cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis pathways.

CONCLUSIONS

These m6A target lncRNAs and mRNAs may be promising biomarkers for predicting clinical prognosis, and the lncRNA-m6A regulator-mRNA regulatory network could improve our understanding of m6A modification in LUAD progression.

Prognostic model based on telomere-related genes predicts the risk of oral squamous cell carcinoma

BACKGROUND

This study investigated a potential prognostic model based on telomere-related genes (TRGs) for the clinical prediction of oral squamous cell carcinoma (OSCC).

METHODS

Gene expression data and associated clinical phenotypes were obtained from online databases. Differentially expressed (DE)-TRGs were identified between OSCC and normal samples, followed by protein-protein interaction and enrichment analyses. Subsequently, the prognostic genes explored based on the DE-TRGs and survival data were applied in the establishment of the current prognostic model, and an integrated analysis was performed between high- and low-risk groups using a prognostic model. The expression of certain prognostic genes identified in the present study was validated using qPCR analysis and/or western blot in OSCC cell lines and clinical samples.

RESULTS

169 DE-TRGs were identified between the OSCC samples and controls. DE-TRGs are mainly involved in functions such as hypoxia response and pathways such as the cell cycle. Eight TRGs (CCNB1, PDK4, PLOD2, RACGAP1, MET, PLK1, KPNA2, and CCNA2) associated with OSCC survival and prognosis were used to construct a prognostic model. qPCR analysis and western blot showed that most of the eight prognostic genes were consistent with the current bioinformatics results. Analysis of the high- and low-risk groups for OSCC determined by the prognostic model showed that the current prognostic model was reliable.

CONCLUSIONS

A novel prognostic model for OSCC was constructed by TRGs. PLOD2 and APLK1 may participate in the progression of OSCC via responses to hypoxia and cell cycle pathways, respectively. TRGs, including KPNA2 and CCNA2, may serve as novel prognostic biomarkers for OSCC.

A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma

BACKGROUND

We aimed to establish an osteosarcoma prognosis prediction model based on a signature of endoplasmic reticulum stress-related genes.

METHODS

Differentially expressed genes (DEGs) between osteosarcoma with and without metastasis from The Cancer Genome Atlas (TCGA) database were mapped to ERS genes retrieved from Gene Set Enrichment Analysis to select endoplasmic reticulum stress-related DEGs. Subsequently, we constructed a risk score model based on survival-related endoplasmic reticulum stress DEGs and a nomogram of independent survival prognostic factors. Based on the median risk score, we stratified the samples into high- and low-risk groups. The ability of the model was assessed by Kaplan-Meier, receiver operating characteristic curve, and functional analyses. Additionally, the expression of the identified prognostic endoplasmic reticulum stress-related DEGs was verified using real-time quantitative PCR (RT-qPCR).

RESULTS

In total, 41 endoplasmic reticulum stress-related DEGs were identified in patients with osteosarcoma with metastasis. A risk score model consisting of six prognostic endoplasmic reticulum stress-related DEGs (ATP2A3, ERMP1, FBXO6, ITPR1, NFE2L2, and USP13) was established, and the Kaplan-Meier and receiver operating characteristic curves validated their performance in the training and validation datasets. Age, tumor metastasis, and the risk score model were demonstrated to be independent prognostic clinical factors for osteosarcoma and were used to establish a nomogram survival model. The nomogram model showed similar performance of one, three, and five year-survival rate to the actual survival rates. Nine immune cell types in the high-risk group were found to be significantly different from those in the low-risk group. These survival-related genes were significantly enriched in nine Kyoto Encyclopedia of Genes and Genomes pathways, including cell adhesion molecule cascades, and chemokine signaling pathways. Further, RT-qPCR results demonstrated that the consistency rate of bioinformatics analysis was approximately 83.33%, suggesting the relatively high reliability of the bioinformatics analysis.

CONCLUSION

We established an osteosarcoma prediction model based on six prognostic endoplasmic reticulum stress-related DEGs that could be helpful in directing personalized treatment.

Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma

This study aimed to investigate Hippo pathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas and European Bioinformatics Institute array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P < 0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic value with an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.

Identification of a novel Immune-Related prognostic model for patients with colorectal cancer based on 3 subtypes

BACKGROUND

The mechanism of immunity in the development of colorectal cancer (CRC) has been studied in-depth, but knowledge of its role in the treatment of CRC is limited.

OBJECTIVE

This study aimed to classify CRC based on immunology and construct an immune-related prognostic model.

METHODS

Nine expression profile datasets of CRC, comprising 1640 samples, were downloaded from the NCBI GEO database. Immune infiltration of CRC was estimated using 5 algorithms. Based on the relative infiltration level of immune cells, immune score, and stromal score, immunosubtype analysis of tumors was conducted. Differentially expressed genes (DEGs) between the two subtypes were screened, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Hematoxylin eosin (HE) staining, immunohistochemical (IHC) staining and qPCR were used to verify the correlation between DEGs and differentiation degree of cancer and the expression of Ki67. Subsequently, a risk signature was constructed based on the least absolute shrinkage and selection operator (LASSO) model.

RESULTS

Based on the infiltration level, immune score, and stromal score of each immune cell, CRC was divided into three immune cell subtypes. Most immune checkpoint genes showed highly significant differences among the three cell subtypes, and most of the co-stimulatory and co-inhibitory molecules were lower in cluster 1 and the highest in cluster 3. Next, 50 common DEGs were determined from the intersections of the different subtypes. Among these common DEGs, 25 were identified to be relevant to the prognosis of CRC patients. The mRNA expressions of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 was related to clinical prognostic indicators. Finally, these 5 DEGs were included in a prognostic risk signature model, which effectively identified high-risk groups among CRC patients in both the training and validation sets.

CONCLUSION

In this study, CRCs were divided into three subtypes based on immunology, and the different subtypes led to different prognosis. Additionally, a prognostic model was constructed based on five immune-related DEGs to distinguish the three subtypes.

Construction and evaluation of an aging-associated genes-based model for pancreatic adenocarcinoma prognosis and therapies

Objectives: Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor. Despite extensive research, the precise role of aging-related genes in the initiation, microenvironment regulation, and progression of PAAD remains unclear.Methods: Patients with PAAD were selected from the International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA) cohorts and the cell senescence-associated genes were obtained from CellAge. ConsensusClusterPlus was utilized for cluster identification. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a prognosis prediction model.Results: We identified three clusters (C1, C2, and C3) based on aging-associated gene profiles. The C1 cluster had a shorter overall survival time, advanced clinical grades, lower immune ESTIMATE score, and tumor immune dysfunction and exclusion (TIDE) score than the C3 subgroup. Moreover, signaling pathways for cell cycle activation were enriched in the C1 cluster. We also identified eight hub genes and constructed a risk model. The high cellular senescence-related signature (CSRS) score subtype exhibited poor prognosis, advanced clinical grades, M2 macrophage infiltration, higher immune checkpoint gene expression, and lower immunotherapeutic benefits.Conclusion: Our risk score model shows high prediction accuracy and survival prediction ability in individual clinical prognosis and pre-immunotherapy evaluation.

Identification of prognostic values of the transcription factor-CpG-gene triplets in lung adenocarcinoma: A narrative review

OBJECTIVE

Abnormal DNA methylation can regulate carcinogenesis in lung adenocarcinoma (LUAD), while transcription factors (TFs) mediate methylation in a site-specific manner to affect downstream transcriptional regulation and tumor progression. Therefore, this study aimed to explore the TF-methylation-gene regulatory relationships that influence LUAD prognosis.

METHODS

Differential analyses of methylation sites and genes were generated by integrating transcriptome and methylome profiles from public databases. Through target gene identification, motif enrichment in the promoter region, and TF prediction, TF-methylation and methylation-gene relation pairs were obtained. Then, the prognostic TF-methylation-gene network was constructed using univariate Cox regression analysis. Prognostic models were constructed based on the key regulatory axes. Finally, Kaplan-Meier curves were created to evaluate the model efficacy and the relationship between candidate genes and prognosis.

RESULTS

A total of 1878 differential expressed genes and 1233 differential methylation sites were screened between LUAD and normal samples. Then 10 TFs were predicted to bind 144 enriched motifs. After integrating TF-methylation and methylation-gene relations, a prognostic TF-methylation-gene network containing 4 TFs, 111 methylation sites, and 177 genes was constructed. In this network, ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were selected to construct the prognostic models, which showed robust abilities in predicting 1-, 3-, and 5-year survival probabilities. Finally, ERG and MTURN were downregulated in LUAD samples, whereas FOXM1 and PTPR were upregulated. Their expression levels were related to LUAD prognosis.

CONCLUSION

ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were proposed as potential biomarkers for predicting the prognosis of LUAD.

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

BACKGROUND

To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).

METHODS

The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.

RESULTS

Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05).

CONCLUSION

Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine-cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

The regulation role and diagnostic value of fibrinogen-like protein 1 revealed by pan-cancer analysis

Although the role of fibrinogen-like protein 1 (FGL1) in tumorigenesis is well known, a pan-cancer analysis of FGL1 lacks. We used bioinformatics techniques to analyze cancer data from publicly available datasets from The Cancer Genome Atlas, UALCAN, TIMER, Gene Expression Profiling Interactive Analysis, cBioPortal, Search Tool for the Retrieval of Interacting Genes, and DAVID. FGL1 expression was significantly regulated in various common tumors than in normal tissues; it was increased in lung adenocarcinoma and decreased in colon adenocarcinoma. Cox regression analysis demonstrated that the upregulation of FGL1 expression was correlated with poor overall survival (OS) and disease-free survival (DFS) in stomach adenocarcinoma, brain low-grade glioma, cervical squamous cell carcinoma, and endocervical adenocarcinoma. Decreased FGL1 methylation levels were observed in majority of tumor types. FGL1 expression was significantly associated with the levels of immune cell subtypes and immune checkpoint genes. Deep deletion was the most common genetic mutation in FGL1 that led to frame-shift mutations, which was closely associated with poor progression-free interval, disease-specific survival, and OS in patients with FGL1 mutations. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that FGL1-related genes participate in diverse pathways. Ubiquitin-mediated proteolysis is significantly correlated to the function of FGL1, which was identified for the first time in the present study. This pan-cancer study provides a deep understanding of the functions of FGL1 in progression of many tumors and demonstrates that FGL1 may be a potential biomarker for the diagnosis, prognosis, and immune infiltration in cancer.

Development and validation of a prognostic 9-gene signature for colorectal cancer

INTRODUCTION

Colorectal cancer (CRC) is one of the most prevalent cancers globally with a high mortality rate. Predicting prognosis using disease progression and cancer pathologic stage is insufficient, and a prognostic factor that can accurately evaluate patient prognosis needs to be developed. In this study, we aimed to infer a prognostic gene signature to identify a functional signature associated with the prognosis of CRC patients.

METHODS

First, we used univariate Cox regression, least absolute shrinkage and selection operator (lasso) regression, and multivariate Cox regression analyses to screen genes significantly associated with CRC patient prognosis, from colorectal cancer RNA sequencing data in The Cancer Genome Atlas (TCGA) database. We then calculated the risk score (RS) for each patient based on the expression of the nine candidate genes and developed a prognostic signature.

RESULTS

Based on the optimal cut-off on the receiver operating characteristic (ROC) curve, patients were separated into high- and low-risk groups, and the difference in overall survival between the two groups was examined. Patients in the low-risk group had a better overall survival rate than those in the high-risk group. The results were validated using the GSE72970, GSE39582, and GSE17536 Gene Expression Omnibus (GEO) datasets, and the same conclusions were reached. ROC curve test of the RS signature also indicated that it had excellent accuracy. The RS signature was then compared with traditional clinical factors as a prognostic indicator, and we discovered that the RS signature had superior predictive ability.

CONCLUSION

The RS signature developed in this study has excellent predictive power for the prognosis of patients with CRC and broad applicability as a prognostic indicator for patients.

Construction of a 10-gene prognostic score model of predicting recurrence for laryngeal cancer

We constructed a prognostic score (PS) model to predict the recurrence risk in patients previously diagnosed with laryngeal cancer (LC). Here the training dataset, consisting of 82 LC samples, was downloaded from The Cancer Genome Atlas (TCGA). The PS model then divided the LC samples into high- and low-risk groups, which predicted well the survival time of LC in three datasets (TCGA dataset: AUC = 0.899; GSE27020: AUC = 0.719; and GSE25727: AUC = 0.662). Therefore, the PS model based on the 10 genes and its nomogram is proposed to help predict the recurrence risk in patients with LC.

A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Background

In recent years, it has been proved that necroptosis plays an important role in the occurrence, development, invasion, metastasis and drug resistance of malignant tumors. Hence, further evaluation and targeting of necroptosis may be of clinical benefit for gynecologic cancers (GCs).

Methods

To compare consistency and difference, we explored the expression pattern and prognostic value of necroptosis-related genes (NRGs) in pan-GC analysis through Linear regression and Empirical Bayesian, Univariate Cox analysis, and public databases from TCGA and Genotype-Tissue Expression (GTEx), including CESC, OV, UCEC, and UCS. We explored the copy number variation (CNV), methylation level and enrichment pathways of NRGs in the four GCs. Based on LASSO Cox regression analysis or principal component analysis, we established the prognostic NRG-signature or necroptosis-score for the four GCs. In addition, we predicted and compared functional pathways, tumor mutational burden (TMB), somatic mutation features, immunity status, immunotherapy, chemotherapeutic drug sensitivity of the NRG-signature based on NRGs. We also examined the expression level of several NRGs in OV samples that we collected using Quantitative Real-time PCR.

Results

We confirmed the presence of NRGs in expression, prognosis, CNV, and methylation for four GCs, thus comparing the consistency and difference among the four GCs. The prognosis and independent prognostic value of the risk signatures based on NRGs were determined. Through the results of subclass mapping, we found that GC patients with lower risk score may be more sensitive to PDL1 response and more sensitive to immune checkpoint blockade therapy. Drug susceptibility analysis showed that, 51, 45, 64, and 29 drugs with differences between risk groups were yielded in CESC, OV, UCEC, and UCS respectively. For OV, the expression differences of several NRGs in the tissues we collected were similar to that in TCGA.

Conclusion

Our comprehensive analysis of NRGs and NRG-signature demonstrated their similarity and difference, as well as their potential roles in prognosis and could guide therapeutic strategies, thus improving the outcome of GC patients.

Identification of Prognostic Factors in Cholangiocarcinoma Based on Integrated ceRNA Network Analysis

This study is aimed at screening prognostic biomarkers in cholangiocarcinoma (CHOL) based on competitive endogenous RNA (ceRNA) regulatory network analysis. Microarray data for lncRNAs, mRNA, and miRNAs were downloaded from the GEO and TCGA databases. Differentially expressed RNAs (DERs) were identified in CHOL and normal liver tissue samples. WGCNA was used to identify disease-related gene modules. By integrating the information from the starBase and DIANA-LncBasev2 databases, we constructed a ceRNA network. Survival analysis was performed, and a prognostic gene-based prognostic score (PS) model was generated. The correlation between gene expression and immune cell infiltration or immune-related feature genes was analyzed using TIMER. Finally, real-time quantitative PCR (RT-qPCR) was used to verify the expression of the 10 DERs with independent prognosis. A large cohort of DERs was identified in the CHOL and control samples. The ceRNA network consisted of 6 lncRNAs, 2 miRNAs, 90 mRNAs, and 98 nodes. Ten genes were identified as prognosis-related genes, and a ten-gene signature PS model was constructed, which exhibited a good prognosis predictive ability for risk assessment of CHOL patients (AUC value = 0.975). Four genes, ELF4, AGXT, ABCG2, and LDHD, were associated with immune cell infiltration and closely correlated with immune-related feature genes (CD14, CD163, CD33, etc.) in CHOL. Additionally, the consistency rate of the RT-qPCR results and bioinformatics analysis was 80%, implying a relatively high reliability of the bioinformatic analysis results. Our findings suggest that the ten-signature gene PS model has significant prognostic predictive value for patients with CHOL. These four immune-related DERs are involved in the progression of CHOL and may be useful prognostic biomarkers for CHOLs.

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Backgrounds

Pyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear.

Methods

Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs.

Results

A total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated.

Conclusion

PRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Background

The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).

Methods

The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.

Results

We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.

Conclusion

These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

Development of an alarm symptom-based risk prediction score for localized oesophagogastric adenocarcinoma (VIOLA score)

BACKGROUND

Gastroesophageal adenocarcinoma is a major contributor to global disease burden with poor prognosis even in resectable, regionally limited stages. Feasible prognostic tools are crucial to improve patient management, yet scarce.

PATIENTS AND METHODS

Disease-related symptoms, patient, tumour, treatment as well as laboratory parameters at initial diagnosis and overall survival (OS) of patients with stage II and III gastroesophageal adenocarcinoma, who were treated between 1990 and 2020 at the Medical University of Vienna, were evaluated in a cross-validation model to develop a feasible risk prediction score.

RESULTS

In total, 628 patients were included in this single-centre analysis. The final score ranked from 0 to 10 and included the factors sex (female +1), age, years (30-59 +1, >60 +2), underweight classified by body mass index (+2), location of the tumour (stomach +1), stage (III +2), stenosis in endoscopy (+1) and weight loss (+1). The score was grouped into low- (0-3), medium- (4-6) and high-risk (7+) subgroups. The median OS were 70.3 [95% confidence interval (CI) 51.2-111.8], 23.4 (95% CI 21.2-26.7) and 12.6 (7.0-16.1) months, respectively. The 1-year survival probabilities were 0.88 (95% CI 0.83-0.93), 0.75 (95% CI 0.70-0.79) and 0.54 (95% CI 0.39-0.74), whereas the 5-year survival probabilities were 0.57 (95% CI 0.49-0.66), 0.24 (95% CI 0.20-0.28) and 0.09 (95% CI 0.03-0.28), respectively.

CONCLUSIONS

The VIennese risk prediction score for Oesophagogastric Localized Adenocarcinoma (VIOLA) risk prediction score poses a feasible tool for the estimation of OS in patients with regionally limited gastroesophageal adenocarcinoma and, thus, may improve patient management in clinical routine. Prospective analyses should be carried out to confirm our findings.