Prognostic Marker Genes Research Articles

A Literature Review of the Role of Candida albicans in the Occurrence and Development of Several Cancers

Candida albicans (C. albicans) is an opportunistic fungus that usually colonizes specific parts of the human body and tends to infect hosts with immunocompromised function, including cancer patients. Several studies have pointed to the direct or indirect involvement of (C. albicans) in oral, esophageal, gastric, pancreatic, colorectal, liver, breast, and skin cancers. So, this article reviews the relationship between C. albicans and various cancers and describes the mechanisms by which this fungus may be involved in the occurrence and development of these cancers. For this reason, keywords such as: "Candida," "cancer," "oral cancer," "esophageal cancer," "gastric cancer," "colorectal cancer," "pancreatic cancer," "liver cancer," "breast cancer," "skin cancer", "risk factors" and "epidemiology" were searched. Articles published in scientific databases, such as Google Scholar, PubMed/MEDLINE, Elsevier, and Scopus, were used. In these articles, it is mentioned that C. albicans may play a role in the occurrence and development of various cancers via several mechanisms, such as modulation of the immune system, induction of matrix metalloproteinases, over-expression of prognostic marker genes related to metastatic events, damaging mucosal epithelium, microbiome changes, activation of oncogenic signalling pathways, induction of chronic inflammation and production of carcinogenic metabolites including nitrosamine and acetaldehyde.

439. INTEGRATION OF SCRNA-SEQ AND BULK RNA-SEQ TO VALIDATE A PROGNOSTIC ASSOCIATED SIGNATURE IN ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Background Considerable evidence has identified important targets for esophageal squamous cell carcinoma (ESCC), but its efficacy and the heterogeneity of ESCC are the main causes of treatment failure. Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity at the single-cell level and helps to better identify potential therapeutic targets. Methods ESCC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database. Single-sample gene set enrichment analysis (ssGSEA) was performed using the limma package to analyze ESCC-associated prognostic marker genes from ESCC transcriptome data downloaded from the TCGA database and the results were validated with transcriptomic data from 56 ESCC patients in our hospital. KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of prognostic marker genes were performed by GSEA. Then, univariate and multivariate Cox and lasso regressions were performed to further build signatures. Finally, qPCR was used to assess the expression of prognostic marker genes in 5 pairs ESCC samples. Results Integrate scRNA-seq (GSE53624) datasets to obtain 13 cell clusters. We characterized single-cell expression profiles of 24076 cell samples and identified 4 marker genes associated with prognosis. Four key prognostic marker genes were trained and validated on the TCGA-ESCA dataset and in 55 patients from our in-hospital cohort to evaluate predictive overall survival (OS) models. Correlation analysis showed that prognostic marker genes were independent of risk clinical characteristics. In addition, we performed Cox regression analysis, suggesting that the 4 prognostic marker genes screened were independent risk factors related to the prognosis of ESCC patients. In addition, qPCR showed that the expression of IER3 and SAA1 was decreased and the expression of CDKN3 was increased in ESCC tissues, while the expression of TINP3 was not significantly different. Conclusion We constructed prognostic marker genes by scRNA-seq and validated the model in a large patient population as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

ADH4—a potential prognostic marker for hepatocellular carcinoma with possible immune-related implications

ObjectiveThis study aims to explore ADH4 expression in hepatocellular carcinoma (HCC), its prognostic impact, and its immune correlation to provide novel insights into HCC prognostication and treatment.MethodsHCC prognostic marker genes were rigorously selected using GEO database, Lasso regression, GEPIA, Kaplan-Meier and pROC analyses. The expression of interested markers (ADH4, DNASE1L3, RDH16, LCAT, HGFAC) in HCC and adjacent tissues was assessed by Immunohistochemistry (IHC). We observed that ADH4 exhibited low expression levels in liver cancer tissues and high expression levels in normal liver tissues. However, the remaining four genes did not manifest any statistically significant differences between hepatocellular carcinoma (HCC) tissue and adjacent non-cancerous tissue. Consequently, ADH4 became the primary focus of our research. ADH4 expression was validated by signed-rank tests and unpaired Wilcoxon rank sum tests across pan-cancer and HCC datasets. Clinical significance and associations with clinicopathological variables were determined using Kaplan-Meier, logistic regression and Cox analyses on TCGA data. The ADH4-related immune responses were explored by Spearman correlation analysis using TIMER2 data. CD68, CD4, and CD19 protein levels were confirmed by IHC in HCC and non-cancerous tissues.ResultsADH4 showed significant downregulation in various cancers, particularly in HCC. Moreover, low ADH4 expression was associated with clinicopathological variables and served as an independent prognostic marker for HCC patients. Additionally, ADH4 affects a variety of biochemical functions and may influence cancer development, prognosis, and treatment by binding to immune cells. Furthermore, at the immune level, the low expression pattern of ADH4 is TME-specific, indicating that ADH4 has the potential to be used as a target for cancer immunotherapy.ConclusionThis study highlights the diagnostic, prognostic and immunomodulatory roles of ADH4 in HCC. ADH4 could serve as a valuable biomarker for HCC diagnosis and prognosis, as well as a potential target for immunotherapeutic interventions.

Directly selecting cell-type marker genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ Tcell types and potential prognostic marker genes.

Prediction of prognosis of patients with radical resection of intrahepatic cholangiocarcinoma based on single cell omics

Objectives: To analyze the survival benefit of intrahepatic cholangiocarcinoma (ICC) radical resection based on single cell omics. Methods: This is a retrospective case-series study. ICC single-cell sequencing was integrated from four data sets in the Gene Expression Omnibus Database, with a total of 46 patients undergoing radical resection, to explore the characteristics of the microenvironment. Microarray data of 100 ICC cases was analyzed in the EMBI database with survival data. The infiltration abundance of each epithelial cell cluster was calculated in each microarray data sample using the ssGSEA algorithm. The key epithelial cell cluster associated with poor patient outcomes was explored. The clinical value of key marker genes in this subgroup was studied. Prognostic marker genes were selected using the univariate and multivariate Cox proportional hazards(CoxPH) model. The The CoxPH model was constructed by the target genes and a nomogram was drawn. Kaplan-Meier survival analysis was used to verify the relationship between score and prognosis of patients. The predictive power of the model was evaluated by receiver operating characteristic(ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Epithelial cell clusters infiltrated almost exclusively in tumor tissue. The MT2A+ epithelial cell subset was associated with a poorer prognosis for patients with a high invasion abundance and patients characterized by infiltration of this group were defined as antioxidant. After screening marker genes in this cluster using a univariate and multivariate CoxPH model, the following genes were found to be independent prognostic factors: FILPIL, NFKBIA, PEG10, SERPINB5. The CoxPH model was constructed using the four gene expression levels, and the survival rate of patients in the high-risk group was significantly lower than those in the low-risk group (all P<0.05). The constructed nomogram had good discrimination and validity. The ROC curve showed that the predicted area under the curve was 0.779, 0.844 and 0.845 at 1, 3 and 5 years, respectively. Compared to clinical indicators, the model had better predictive power using the calibration curve and the DCA test. Conclusions: The MT2A+ epithelial cell group may be associated with the prognosis of patients with ICC, and the concept of ICC tissue typing of antioxidant and non-antioxidant types is proposed. The type of antioxidant may predict the poor prognosis of the patients, and postoperative adjuvant therapy and other means could be considered to improve the survival of the patients.

Development of lymph node metastasis-related prognostic markers in breast cancer

BackgroundLymph node metastasis (LNM) from Breast cancer (BC) is commonly seen in BC progression. Currently, the identification of genes linked with LNM in BC remains in mystery. MethodsGenes related to BC LNM were screened, and a risk model was constructed based on LASSO-Cox analysis. Combined with the Kaplan-Meier curve, the ability of riskscore to distinguish different baseline characteristics was evaluated, and model was verified by the receiver operating characteristic (ROC) curve. The expression levels of prognostic marker genes were analyzed by qRT-PCR and western blot (WB). ResultsA higher survival rate and longer survival time in low-risk BC patients. The 1, 3 and 5 year AUC values of the training set were 0.79, 0.74, and 0.73, respectively. Results for the validation set was similar to the training set. The differentially expressed genes between the high- and low-risk groups were significantly enriched in immune pathways. In addition, the low-risk group had higher levels of immune infiltration. qRT-PCR and WB results showed that in BC, CDH10, SMR3A, POU3F2, and FABP7 were down-regulated, and LHX1 was up-regulated. ConclusionsWe built a prognostic model of BC based on LNM-related genes, proffering evaluation for prognosis and precise cure of BC. SignificanceAt present, the genes related to lymph node metastasis in BC are still largely unknown and need to be further explored. Searching for potential lymph node metastasis-related genes of BC will provide meaningful biomarkers for BC treatment. Based on TCGA-BRCA data, we established an effective 11-gene prognostic risk model that could predict patient outcomes independently. Our model could classify BC patients and distinguish patients with poor prognosis effectively. Besides, the feature genes we identified might exert a predictive function in immunotherapy. The results of this study provide a new reference for the prognosis and treatment of BC patients with lymph node metastasis.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

Screening of renal clear cell carcinoma prognostic marker genes based on TCGA and GTEx chip data and construction of transcription factor-related regulatory networks

This study aimed to identify prognostic marker genes for renal clear cell carcinoma (RCCC) and construct a regulatory network of transcription factors and prognostic marker genes. Three hundred eighty-six genes were significantly differentially expressed in RCCC, with functional enrichment analysis suggesting a relationship between these genes and kidney function and development. Cox and Lasso regression analyses revealed 10 prognostic marker genes (RNASET2, MSC, DPEP1, FGF1, ATP1A1, CLDN10, PLG, SLC44A1, PCSK1N, and LGI4) that accurately predicted RCCC patient prognosis. Upstream transcription factors of these genes were also identified, and in vitro experiments suggested that ATP1A1 may play a key role in RCCC patient prognosis. The findings of this study provide important insights into the molecular mechanisms of RCCC and may have implications for personalized treatment strategies.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma.

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Heterogeneity and Differentiation Trajectories of Infiltrating CD8+ T Cells in Lung Adenocarcinoma.

CD8+ T cells infiltrating the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) are critical for establishing antitumor immunity. Nevertheless, the global landscape of their numbers, functional status, and differentiation trajectories remains unclear. In the single-cell RNA-sequencing (scRNA-seq) dataset GSE131907 of LUAD, the CD8+T cells were selected for TSNE clustering, and the results showed that they could be divided into ten subsets. The cell differentiation trajectory showed the presence of abundant transition-state CD8+ T cells during the differentiation of naive-like CD8+ T cells into cytotoxic CD8+ T cells and exhausted CD8+ T cells. The differentially expressed marker genes among subsets were used to construct the gene signature matrix, and the proportion of each subset was identified and calculated in The Cancer Genome Atlas (TCGA) samples. Survival analysis showed that the higher the proportion of the exhausted CD8+ T lymphocyte (ETL) subset, the shorter the overall survival (OS) time of LUAD patients (p = 0.0098). A total of 61 genes were obtained by intersecting the differentially expressed genes (DEGs) of the ETL subset, and the DEGs of the TCGA samples were divided into a high and a low group according to the proportion of the ETL subset. Through protein interaction network analysis and survival analysis, four hub genes that can significantly affect the prognosis of LUAD patients were finally screened, and RT-qPCR and Western blot verified the differential expression of the above four genes. Our study further deepens the understanding of the heterogeneity and functional exhaustion of infiltrating CD8+ T cells in LUAD. The screened prognostic marker genes provide potential targets for targeted therapy and immunotherapy in LUAD patients.

Single Nucleotide Variants in KIF14 Gene May Have Prognostic Value in Breast Cancer.

Human kinesin 14 (KIF14) is one of the 70 prognostic marker genes (so-called Amsterdam profile) previously identified by the microarray of breast carcinomas, and its high transcript expression in tumor specimens indicates a poor prognosis for patients. We performed a pilot study to explore the prognostic and predictive meaning of KIF14 germline genetic variability in breast cancer patients. KIF14 coding sequence, including 5' and 3' untranslated regions and overlaps to introns for identification of splicing sites, was analyzed using next-generation sequencing in the testing set of blood DNA samples from 105 breast cancer patients with clinical follow-up. After rigorous evaluation of major allele frequency, haplotype blocks, in silico predicted functional aspects, expression quantitative trait loci, and clinical associations, eight single nucleotide variants were subsequently validated in the evaluation set of 808 patients. Carriers of minor alleles G (rs17448931) or T (rs3806362) had significantly shorter overall survival than wild type homozygotes (p = 0.010 and p = 0.023, respectively) thus successfully replicating the results of the testing set. Both associations remained significant in the multivariate Cox regression analysis, including molecular subtype and stage as covariates (hazard ratio, HR = 1.7, 95% confidence interval (CI) = 1.1-2.8 for rs17448931 and HR = 1.9, CI 1.2-3.0 for rs3806362). In conclusion, our preliminary data suggest that minor alleles in rs17448931 and rs3806362 of KIF14 represent candidate biomarkers of poor prognosis of breast cancer patients. After pending validation in independent populations and eventual functional characterization, these candidates might become useful biomarkers in the clinics.

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (<1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens <1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer.

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning.

Simple SummaryUsing a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated.Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.

A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma.

Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis.Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses.Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients.Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.

Characterizing the Metabolic and Immune Landscape of Non-small Cell Lung Cancer Reveals Prognostic Biomarkers Through Omics Data Integration.

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. The development of high-throughput single-cell RNA-sequencing (RNA-seq) technology and the advent of multi-omics have provided a solid basis for a systematic understanding of the heterogeneity in cancers. Although numerous studies have revealed the molecular features of NSCLC, it is important to identify and validate the molecular biomarkers related to specific NSCLC phenotypes at single-cell resolution. In this study, we analyzed and validated single-cell RNA-seq data by integrating multi-level omics data to identify key metabolic features and prognostic biomarkers in NSCLC. High-throughput single-cell RNA-seq data, including 4887 cellular gene expression profiles from NSCLC tissues, were analyzed. After pre-processing, the cells were clustered into 12 clusters using the t-SNE clustering algorithm, and the cell types were defined according to the marker genes. Malignant epithelial cells exhibit individual differences in molecular features and intra-tissue metabolic heterogeneity. We found that oxidative phosphorylation (OXPHOS) and glycolytic pathway activity are major contributors to intra-tissue metabolic heterogeneity of malignant epithelial cells and T cells. Furthermore, we constructed T-cell differentiation trajectories and identified several key genes that regulate the cellular phenotype. By screening for genes associated with T-cell differentiation using the Lasso algorithm and Cox risk regression, we identified four prognostic marker genes for NSCLC. In summary, our study revealed metabolic features and prognostic markers of NSCLC at single-cell resolution, which provides novel findings on molecular biomarkers and signatures of cancers.

Identification of prognostic marker genes in head and neck squamous cell carcinoma: A study based on The Cancer Genome Atlas database and experimental validation.

Early detection and prognostic prediction are crucial in improving the survival of patients with head and neck squamous cell carcinoma (HNSCC). Therefore, we provided potential molecular markers in this study for early diagnosis and prognosis of this cancer based on The Cancer Genome Atlas (TCGA) database analysis and experimental validations. Differentially expressed genes (DEGs) between HNSCC tumor and normal samples were identified by TCGA database-based analyses. Univariate and multivariate Cox regression analyses were applied, respectively, to identify survival-related DEGs and independent prognostic factors in HNSCC. Further, RT-qPCR was employed to verify expression of DEGs in cancer and adjacent tissues from HNSCC patients recruited in our hospital, in which we also clarified the correlation between candidate genes and clinicopathological characteristics and prognosis of HNSCC patients. TCGA data analyses yielded 59 DEGs. Cox analyses identified 13 candidate genes closely related to prognosis of HNSCC patients and established a five-gene signature comprising AC103702.2, LINC00941, RPL29, FOXL2, and CCL11. This five-gene signature could classify patients into high- and low-risk groups. The survival rate of the high-risk group was significantly lower than that of the low-risk group. Clinical tissue experiments further confirmed that AC103702.2, LINC00941, CCL11, and RPL29P19 genes were inversely associated with the prognosis of HNSCC patients, while CCL11 gene was positively associated. We also found that high-risk HNSCC patients presented a higher incidence of lymph node metastasis. Five prognostic marker genes (AC103702.2, LINC00941, CCL11, RPL29P19, and FOXL2) as a gene cluster may serve as prognostic marker genes in HNSCC.

Identification of genes associated with cancer prognosis in glioma: In silico data analyses of Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA).

Glioma is one particular type of brain malignancy which is highly complex and usually has a poor prognosis. Despite the limited diagnostic level of glioma, the survival time of affected patients broadly varies. Here, we conducted a detailed analysis, regarding the differences in patient survival time, to discover potential survival-related genes in glioma as well as their putative regulatory mechanisms. To contextualize the acquisition of these potential prognosis markers in large populations, particularly in China, we combined CGGA and The Cancer Genome Atlas (TCGA) databases to properly identify genes that are significantly related to survival. Our workflow combined a series of analytical approaches, including differential analysis, survival time, co-expression, clinical correlation analysis, ROC curve evaluation and prediction ability. Our results indicate that the four particular genes - PLAT, IGFBP2, BCAT1, SERPINH1 could be used as independent prognostic marker genes. These genes have also shown good prognostic ability in distinct populations, reiterating the robustness and value of these prognostic markers.

Knockout of immunotherapy prognostic marker genes eliminates the effect of the anti-PD-1 treatment

The efficacy of immunotherapy is largely patient-specific due to heterogeneity in tumors. Combining statistic power from a variety of immunotherapies across cancer types, we found four biological pathways significantly correlated with patient survival following immunotherapy. The expression of immunotherapy prognostic marker genes (IPMGs) in these pathways can predict the patient survival with high accuracy not only in the TCGA cohort (89.36%) but also in two other independent cohorts (80.91%), highlighting that the activity of the IPMGs can reflect the sensitivity of the tumor immune microenvironment (TIME) to immunotherapies. Using mouse models, we show that knockout of one of the IPMGs, MALT1, which is critical for the T-cell receptor signaling, can eliminate the antitumor effect of anti-PD-1 treatment completely by impairing the activation of CD8+ T cells. Notably, knockout of another IPMG, CLEC4D, a C-type lectin receptor that expressed on myeloid cells, also reduced the effect of anti-PD-1 treatment potentially through maintaining the immunosuppressive effects of myeloid cells. Our results suggest that priming TIME via activating the IPMGs may increase the response rate and the effect of immune checkpoint blockers.

Relevance of 2'-O-Methylation and Pseudouridylation for the Malignant Melanoma.

Simple SummaryThis study investigates the expression, the histological localization, and the influence of the factors involved in 2′-O-methylation and pseudouridylation on prognostic relevant markers, proliferation markers, overall survival, molecular immune surveillance and evasion mechanisms within the malignant melanoma. Statistically significant positive correlations to the expression of markers involved in cell proliferation were observed. The upregulation of the RNA modifying factors was of prognostic relevance in this tumor disease with a negative impact on the overall survival of melanoma patients. Furthermore, the factors involved in 2′-O-methylation and pseudouridylation were statistically significant negative correlated to the expression of human leukocyte antigen class I genes as well as of components of the antigen processing machinery.The two RNA modifications 2′-O-methylation and pseudouridylation occur on several RNA species including ribosomal RNAs leading to an increased translation as well as cell proliferation associated with distinct functions. Using malignant melanoma (MM) as a model system the proteins mediating these RNA modifications were for the first time analyzed by different bioinformatics tools and public available databases regarding their expression and histological localization. Next to this, the impact of these RNA-modifying factors on prognostic relevant processes and marker genes of malignant melanoma was investigated and correlated to immune surveillance and evasion strategies. The RNA modifying factors exerted statistically significant positive correlations to the expression of genes involved in cell proliferation and were statistically significant negative correlated to the expression of human leukocyte antigen class I genes as well as of components of the antigen processing machinery in malignant melanoma. Upregulation of the RNA modifying proteins was of prognostic relevance in this tumor disease with a negative impact on the overall survival of melanoma patients. Furthermore, the expression of known oncogenic miRs, which are induced in malignant melanoma, directly correlated to the expression of factors involved in these two RNA modifications.