Cancer Genome Research Articles

PDCL3 as a prognostic factor and associated with the VEGF signaling pathway in glioma.

New targeted drugs about angiogenesis could develop the treatment of glioma. We aimed to explore the role of phosducin like 3 (PDCL3) in angiogenesis of glioma. RNA sequencing data and matched clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. To screen for the reliable genes with the filtering analyses, survival, multivariate Cox, receiver operating characteristic (ROC) curve filtration, and clinical correlation analyses were performed. The PDCL3 gene was validated by immunohistochemistry as a reliable gene for further analysis. Then we used the combined data of TCGA and Genotype-Tissue Expression from UCSC to detect the differential gene expression of PDCL3. Related signal pathways in glioma were explored by the gene set enrichment analysis and co-expression analysis. Lastly, we performed in vitro experiments to verify the gene functions and related mechanisms. The three filtering analyses and immunostaining indicated that the expression of PDCL3 in glioma tissues was higher than the normal tissues. Gene function analysis showed that PDCL3 activated the vascular endothelial growth factor (VEGF) signal pathway, and its mechanism was related to pathways in cancer, like NOD like receptor signaling pathway, the RIG-I like receptor signaling pathway and the P53 signaling pathway by MAPK/AKT in gliomas. This suggested that the proliferation, migration and invasion of glioma cells might be inhibited by the downregulation of PDCL3 in vitro, which may be related to the activation of VEGF signaling pathway. We demonstrated that PDCL3 could function as an independent adverse prognostic marker in glioma. Its pro-oncogenic mechanism may be related to the VEGF signaling pathway.

Association of GATA3 expression in triple-positive breast cancer with overall survival and immune cell infiltration

Breast cancer remains a leading cause of cancer-related mortality among women, with triple-positive breast cancer (TPBC) being a particularly aggressive subtype. GATA binding protein 3 (GATA3) plays a crucial role in the luminal differentiation of breast epithelium and T-cell differentiation. However, the relationship between GATA3 and immune infiltration in TPBC remains unclear. This study collected and analyzed TPBC data from The Cancer Genome Atlas (TCGA), METABRIC, and GSE123845 databases. Univariate and multivariate Cox regression analyses, along with Kaplan–Meier survival analyses, were employed to assess the prognostic value of GATA3 and other clinical features. Subsequently, Gene Set Enrichment Analysis (GSEA) was conducted to explore the potential biological functions and regulatory mechanisms of GATA3 in TPBC. Additionally, ssGSEA analysis revealed the connection between GATA3 and immune infiltration. And the effects of neoadjuvant chemotherapy and immunotherapy on GATA3 expression were also explored. Finally, clinical samples were used to detect the relationship between GATA3 expression and tumor infiltrating lymphocyte (TIL) levels. Our results demonstrated that GATA3 was significantly overexpressed in TPBC tissues compared to normal tissues (P < 0.05). A positive correlation between GATA3 mRNA and protein levels was observed (R = 0.55, P < 0.05). Notably, high GATA3 expression was associated with poor overall survival (HR = 1.24, 95% confidence interval (CI) 1.25–11.76, P < 0.05). GSEA indicated significant enrichment of immune-related gene sets in low GATA3 expression groups. Furthermore, pathologic complete response (pCR) patients exhibited significantly lower GATA3 expression compared to residual disease (RD) patients. Mutation analysis revealed higher PIK3CA and TP53 mutation rates in high GATA3 expression groups. Finally, clinical validation data showed that the degree of TILs was significantly higher in the low GATA3 expression group. In conclusion, this study suggests that high GATA3 expression may be associated with poor prognosis and may reduce immune infiltration in TPBC.

FASTKD1 as a diagnostic and prognostic biomarker for STAD: Insights into m6A modification and immune infiltration.

Fas-activated serine/threonine kinase domain 1 (FASTKD1), a known modulator of mitochondrial-mediated cell death and survival processes, has garnered attention for its potential role in various biological contexts. However, its involvement in gastric cancer remains unclear. Thus, the present study aimed to investigate the relationship between FASTKD1 expression and key factors, including clinicopathological characteristics, immune infiltration and m6A modification in stomach adenocarcinoma (STAD). The expression of FASTKD1 was analyzed in STAD and normal adjacent tissues to assess its association with clinicopathological characteristics and survival prognosis. Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used in this study. Additionally, the findings were validated through immunohistochemical staining. Co-expression analysis of FASTKD1 was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and LinkedOmics database analysis. An in-depth analysis was conducted using databases, such as Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), GEO and TCGA to explore the potential correlation between FASTKD1 expression and immune infiltration and m6A modification in STAD. The results revealed that FASTKD1 was significantly upregulated across different tumor types, including STAD. Notably, FASTKD1 was able to distinguish between tumor and normal tissue samples with accuracy. Furthermore, the expression levels of FASTKD1 were significantly associated with clinical stage and survival. Through GO/KEGG enrichment analysis and GSEA, it was revealed that the genes co-expressed with FASTKD1 were active in a variety of biological processes. Within the TIMER, GEPIA and TCGA databases, a notable inverse correlation was observed between FASTKD1 expression and the abundance of immune cell subsets. Notably, significant correlations were established between FASTKD1 and m6A modification genes, YTHDF1 and LRPPRC, in both TCGA and GEO datasets. In conclusion, FASTKD1 may serve a significant role in m6A modification and immune infiltration processes, making it a potentially valuable diagnostic and prognostic biomarker in STAD.

Construction of lncRNA prognostic model related to disulfidptosis in lung adenocarcinoma

BackgroundLung cancer is one of the malignant tumors with the highest rates of morbidity and mortality worldwide. One of the most common histological types of lung cancer is lung adenocarcinoma (LUAD). Despite the fact that development in medicine has significantly improved some patients’ prognoses, the overall survival (OS) rate is still very low. In glucose-deficient SLC7A11-overexpressed cancer cells, the accumulation of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, interferes with their tissues, and eventually leads to actin network collapse and cell death. This mode of cell death is called disulfidptosis. Studies have shown that disulfidptosis may be a new target for cancer treatment. However, the role of disulfidptosis in LUAD is still unknown. MethodsLUAD transcriptome and clinical information from The Cancer Genome Atlas (TCGA) was downloaded. The co-expression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Cox regression analysis was performed to screen the disulfidptosis-related lncRNAs (DRLs) and build the prognostic model. Kaplan-Meier curve, Cox regression analysis, and receiver operating characteristic (ROC) curve was used to validate the model. Then a nomogram is made to predict the prognosis of LUAD patients. Finally, fresh-collected clinical samples were used to verify the expression of DRLs in LUAD. ResultsThe prognostic model with six DRLs was developed to predict the prognosis of LUAD, with superior prognosis value compared to other clinical variables. The Cox regression analysis revealed that T stage, N stage and the risk score were identified as independent variables that affected LUAD prognosis. ROC curve revealed that the model has a moderate diagnostic value, with an AUC of 1-year 0.684, 3-year 0.664, and 5-year 0.588. Moreover, nine medications connected to LUAD treatment were acquired through drug sensitivity analysis. LUAD tissue validation showed that AC012073.1, AC012615.1, EMSLR, and SNHG12 were highly expressed, while AL606834.1 and AL365181.2 with low expression. ConclusionSix DRLs were screened and verified to construct the prognostic model, which can accurately predict the LUAD prognosis. It establishes a basis for further exploration into the molecular mechanisms underlying LUAD and identification of potential biomarkers for diagnosis, prognosis, and therapeutic targets.

Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs.

Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs. The objective of this study was to launch a prediction model for therapeutic responses in OC patients. The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells. The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis. The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.

Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.

The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.

Pan-cancer analysis of SERPINE1 with a concentration on immune therapeutic and prognostic in gastric cancer.

The serine protease inhibitor clade E member 1 (SERPINE1) is a key modulator of the plasminogen/plasminase system and has been demonstrated to promote tumor progression and metastasis in various tumours. However, although much literature has explored the cancer-promoting mechanism of SERPINE1, the pan-cancer analyses of its predictive value and immune response remain unexplored. The differential expression, and survival analysis of SERPINE1 expression in multiple cancers were analysed using The Cancer Genome Atlasand Genotype-Tissue Expression database. Kaplan-Meier (K-M) plotter and survival data analysis were used to analyze the prognostic value of SERPINE1 expression, including overall survival (OS), disease-specific survival, disease-free interval and progression-free interval and investigated the relationship of SERPINE1 expression with microsatellite instability. We further analysed the correlation between the expression of SERPINE1 and immune infiltration. The Kyoto Encyclopaedia of Genes and Genomes pathway was used for enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) database was used to perform pathway analysis. Finally, invitro experiments demonstrated that knockdown or overexpression of SERPINE1 could alter the proliferation and migration of gastric cancer (GC) cells. The results indicated that SERPINE1 expression levels different significantly between cancer and normal tissues, meanwhile, it was highly expressed in various cancers. By analysing online data, it has been observed that the gene SERPINE1 exhibits heightened expression levels across a variety of human cancers, significantly impacting patient survival rates. Notably, the presence of SERPINE1 was strongly associated with decrease OS and disease-free survival in individuals diagnosed with GC. Furthermore, an observed link indicates that higher levels of SERPINE expression are associated with increased infiltration of immune cells in GC. Finally, invitro experiments showed that knockdown or overexpression of SERPINE1 inhibited the growth, and migration, of GC cells. SERPINE1expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in GC. This study shows that SERPINE1 is an oncogene that participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in GC. These findings underscore the importance of further investigating the role of SERPINE1 in cancer progression and offer a promising direction for the development of new therapeutic strategies.

Identification of an Exosome-relevant SNHG6-hsa-miR-429- CHRDL1/CCNA2 Axis for Lung Adenocarcinoma Prognosis Evaluation.

To explore an exosome-relevant molecular classification in lung adenocarcinoma (LUAD). Exosome genes or relevant non-coding RNAs are regulators of cancer treatment and prognosis, but their function in LUAD has not yet been determined. Unraveling a molecular classification applying exosome-related RNA networks for LUAD prognosis evaluation. MicroRNA sequencing data (miRNAs-seq) and RNA sequencing data (RNA- seq) were derived from The Cancer Genome Atlas (TCGA). The ConsensusCluster- Plus package was used for molecular typing in LUAD based on 121 Exosome-related genes. Then, a limma package was conducted to explore differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed lncRNAs (DElncRNAs) in molecular typing for constructing an Exosome-driven competing endogenous RNA network (ceRNA). Dominant miRNAs, as well as target mRNAs, were identified by COX modeling and Kaplan-Meier survival analysis. Two Exosome-associated molecular clusters classified in LUAD. The C2 cluster favored high clinicopathology and showed a trend toward poor prognosis. 29 lncRNA- miRNA and 12 miRNA-mRNA interaction pairs were identified. The hsa-miR-429 was the pivotal miRNA in the network that affected the prognosis of LUAD. According to the interaction relationship and LUAD prognostic role, SNHG6-hsa- miR-429-CHRDL1/CCNA2 was identified. SNHG6-hsa-miR-429-CHRDL1 exerts oncogenic effects, and SNHG6-hsa-miR-429- CCNA2 exerts pro-oncogenic effects. Overall, our study identified an Exosome-driven ceRNA network in LUAD, and the SNHG6-hsa-miR-429-CHRDL1/CCNA2 axis could be a new therapeutic target for LUAD and our study provides new insights into the molecular mechanisms of LUAD.

Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer.

This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis. Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed. In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1. The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

A New Ferroptosis-Related Long Non-Coding RNA Risk Model Predicts the Prognosis of Patients With Papillary Thyroid Cancer.

Ferroptosis is a novel form of regulated cell death that involves in cancer progression. However, the role of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC) remains to be elucidated. The purpose of this paper was to clarify the prognostic value of ferroptosis-related lncRNAs in PTC. The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between ferroptosis-related genes (FRGs) and lncRNA was determined using Pearson correlation analysis. Multivariate Cox regression model (P < 0.01) was performed to establish a ferroptosis-related lncRNAs risk model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, risk curve and nomograms were then performed to assess the accuracy and clinical applicability of prognostic models. The correlations between the prognosis model and clinicopathological variables, immune and m6A were analyzed. Finally, in vitro assays were performed to verify the role of LINC00900, LINC01614 and PARAL1 on the proliferation, migration and invasion in TPC-1 and BCPAP cells, as well as the relationship between three lncRNAs and ferroptosis. A five-ferroptosis-related lncRNAs (PARAL1, LINC00900, DPH6-DT, LINC01614, LPP-AS2) risk model was constructed. Based on the risk score, samples were divided into the high- and low-risk groups. Patients in the low-risk group had better prognosis than those in high-risk group. Compared to traditional clinicopathological features, risk score was more accurate in predicting prognosis in patients with PTC. Additionally, the difference of immune cell, function and checkpoints was observed between two groups. Moreover, experiments showed that LINC00900 promoted the proliferation, migration and invasion in TPC-1 and BCPAP cells, while LINC01614 and PARAL1 revealed opposite effects, all of which were related to ferroptosis. In summary, we identified a five-ferroptosis-related lncRNAs risk model to predict the prognosis of PTC. Furthermore, our study also revealed that LINC00900 functioned as a tumor suppressor lncRNA, LINC01614 and PARAL1 as an oncogenic lncRNA in PTC.

The association between thyroid differentiation score and survival outcomes in papillary thyroid carcinoma.

Thyroid differentiation score (TDS), calculated based on mRNA expression levels of 16 genes controlling thyroid metabolism and function, has been proposed as a measure to quantify differentiation in PTC. The objective of this study is to determine whether TDS is associated with survival outcomes across patient cohorts. Two independent cohorts of PTC patients were used: 1) the Cancer Genome Atlas (TCGA) thyroid cancer study (N=372), 2) MD Anderson Cancer Center (MDACC) cohort (N=111). The primary survival outcome of interest was progression-free interval (PFI). Association with overall survival (OS) was also explored. The Kaplan-Meier method and Cox proportional hazards models were used for survival analyses. In both cohorts, TDS was associated with tumor and nodal stage at diagnosis as well as tumor driver mutation status. High TDS was associated with longer PFI on univariable analyses across cohorts. After adjusting for overall stage, TDS remained significantly associated with PFI in the MDACC cohort only (aHR 0.67, 95%CI 0.52-0.85). In subgroup analyses stratified by tumor driver mutation status, higher TDS was most consistently associated with longer PFI in BRAFV600E-mutated tumors across cohorts after adjusting for overall stage (TCGA: aHR 0.60, 95% CI: 0.33-1.07; MDACC: aHR 0.59, 95% CI: 0.42-0.82). For OS, increasing TDS was associated with longer OS in the overall MDACC cohort (aHR=0.78, 95% CI:0.63-0.96), where the median duration of follow-up was 12.9 years. TDS quantifies the spectrum of differentiation status in PTC and may serve as a potential prognostic biomarker in PTC, mostly promisingly in BRAFV600E-mutated tumors.

Lung-MAP Next Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400)

IntroductionSquamous cell cancer (SqCC) is a lung cancer subtype with few targeted therapy options. Molecular characterization, i.e., by next generation sequencing (NGS), is needed to identify potential targets. Lung-MAP SWOG S1400 enrolled patients with previously treated stage IV or recurrent SqCC to assess NGS biomarkers for therapeutic substudies. MethodsTumors underwent NGS using Foundation Medicine’s FoundationOne research platform, which sequenced the exons and/or introns of 313 cancer-related genes. Mutually Exclusive Gene Set Analysis (MEGSA) and Selected Events Linked by Evolutionary Conditions across human Tumors (SELECT) were performed to identify mutually exclusive and co-occurring gene alterations. Comparisons were performed with data on 495 lung SqCC downloaded from The Cancer Genome Atlas. Cox proportional hazards models were used to examine associations between genetic variants and survival. ResultsNGS data are reported for 1672 patients enrolled on S1400 between 2014 and 2019. MEGSA identified two non-overlapping sets of mutually exclusive alterations with a false discovery rate < 15%: NFE2L2, KEAP1 and PARP4; and CDKN2A and RB1. PARP4, a relatively uncharacterized gene, showed three frequent mutations suggesting functional significance: 3116T>C (I1039T), 3176A>G (Q1059R) and 3509C>T (T1170I). NFE2L2 and KEAP1 alterations when taken together were associated with poorer survival. ConclusionsAs the largest dataset to-date of lung SqCC profiled on a clinical trial, the S1400 NGS dataset establishes a rich resource for biomarker discovery. Mutual exclusivity of PARP4 and NFE2L2 or KEAP1 alterations suggests that PARP4 may have an uncharacterized role in a key pathway known to impact oxidative stress response and treatment resistance.

Landscape of Concomitant Driver Alterations in Classical EGFR-Mutated Non-Small Cell Lung Cancer.

Next-generation sequencing (NGS) has enabled the detection of concomitant driver alterations in non-small cell lung cancer (NSCLC). However, the magnitude and clinical relevance of concomitant drivers remain to be explored. We profiled concomitant driver alterations of EGFR+ NSCLC by using targeted NGS. The associated genomic and clinical features were analyzed and validated in an independent The Cancer Genome Atlas cohort of patients with EGFR+ NSCLC. Out of the total patient population, 334 patients had EGFR mutations along with concomitant driver mutations, comprising 3.09% of the entire cohort. The most frequent co-occurring mutations with sensitizing EGFR mutations include KRAS at 53.9%, followed by ERBB2 at 24.3%, MET at 16.5%, and BRAF at 3.3%. KRAS mutations in concomitant drivers were frequently hyperexchange mutations (25.6% v 8.2%, P < .001), compared with KRAS single drivers. EGFR/ERBB2 drivers exhibited a higher incidence of ERBB2 amplification (40.7% v 16.5%, P < .001) and p.S310F/Y mutations (44.4% v 4.3%, P < .001) compared with ERBB2 alone. EGFR/MET drivers had a higher frequency of MET amplification (71.4% v 43.3%) than MET single drivers. At the genomic level, the median number of additional concurrent mutations was four, with TSC2 (4%), CD274 (1%), and TP53 (63%) being the most frequently coaltered genes in concomitant driver tumors. Interestingly, clonality analysis indicated that EGFR mutations were more likely to occur as clonal events, whereas the codrivers were more often subclonal. Patients with concomitant drivers or with concomitant MET amplification exhibited worse prognosis. These findings might aid in the selection of effective therapeutic regimens and facilitate the development of combination therapies.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma.

Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers.

Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.

Metastasis and basement membrane-related signature enhances hepatocellular carcinoma prognosis and diagnosis by integrating single-cell RNA sequencing analysis and immune microenvironment assessment

BackgroundHepatocellular carcinoma (HCC) is the most common type of primary liver cancer and second leading cause of cancer-related deaths worldwide. The heightened mortality associated with HCC is largely attributed to its propensity for metastasis, which cannot be achieved without remodeling or loss of the basement membrane (BM). Despite advancements in targeted therapies and immunotherapies, resistance and limited efficacy in late-stage HCC underscore the urgent need for better therapeutic options and early diagnostic biomarkers. Our study aimed to address these gaps by investigating and evaluating potential biomarkers to improve survival outcomes and treatment efficacy in patients with HCC.MethodIn this study, we collected the transcriptome sequencing, clinical, and mutation data of 424 patients with HCC from The Cancer Genome Atlas (TCGA) and 240 from the International Cancer Genome Consortium (ICGC) databases. We then constructed and validated a prognostic model based on metastasis and basement membrane-related genes (MBRGs) using univariate and multivariate Cox regression analyses. Five immune-related algorithms (CIBERSORT, QUANTISEQ, MCP counter, ssGSEA, and TIMER) were then utilized to examine the immune landscape and activity across high- and low-risk groups. We also analyzed Tumor Mutation Burden (TMB) values, Tumor Immune Dysfunction and Exclusion (TIDE) scores, mutation frequency, and immune checkpoint gene expression to evaluate immune treatment sensitivity. We analyzed integrin subunit alpha 3 (ITGA3) expression in HCC by performing single-cell RNA sequencing (scRNA-seq) analysis using the TISCH 2.0 database. Lastly, wound healing and transwell assays were conducted to elucidate the role of ITGA3 in tumor metastasis.ResultsPatients with HCC were categorized into high- and low-risk groups based on the median values, with higher risk scores indicating worse overall survival. Five immune-related algorithms revealed that the abundance of immune cells, particularly T cells, was greater in the high-risk group than in the low-risk group. The high-risk group also exhibited a higher TMB value, mutation frequency, and immune checkpoint gene expression and a lower tumor TIDE score, suggesting the potential for better immunotherapy outcomes. Additionally, scRNA-seq analysis revealed higher ITGA3 expression in tumor cells compared with normal hepatocytes. Wound healing scratch and transwell cell migration assays revealed that overexpression of the MBRG ITGA3 enhanced migration of HCC HepG2 cells.ConclusionThis study established a direct molecular correlation between metastasis and BM, encompassing clinical features, tumor microenvironment, and immune response, thereby offering valuable insights for predicting clinical outcomes and immunotherapy responses in HCC.

NCAPD2 augments the tumorigenesis and progression of human liver cancer via the PI3K‑Akt‑mTOR signaling pathway.

Non‑SMC condensin I complex subunit D2 (NCAPD2) is a newly identified oncogene; however, the specific biological function and molecular mechanism of NCAPD2 in liver cancer progression remain unknown. In the present study, the aberrant expression of NCAPD2 in liver cancer was investigated using public tumor databases, including TNMplot, The Cancer Genome Atlas and the International Cancer Genome Consortium based on bioinformatics analyses, and it was validated using a clinical cohort. It was revealed that NCAPD2 was significantly upregulated in liver cancer tissues compared with in control liver tissues, and NCAPD2 served as an independent prognostic factor and predicted poor prognosis in liver cancer. In addition, the expression of NCAPD2 was positively correlated with the percentage of Ki67+ cells. Finally, single‑cell sequencing data, gene‑set enrichment analyses and in vitro investigations, including cell proliferation assay, Transwell assay, wound healing assay, cell cycle experiments, cell apoptosis assay and western blotting, were carried out in human liver cancer cell lines to assess the biological mechanisms of NCAPD2 in patients with liver cancer. The results revealed that the upregulation of NCAPD2 enhanced tumor cell proliferation, invasion and cell cycle progression at the G2/M‑phase transition, and inhibited apoptosis in liver cancer cells. Furthermore, NCAPD2 overexpression was closely associated with the phosphatidylinositol 3‑kinase (PI3K)‑Akt‑mammalian target of rapamycin (mTOR)/c‑Myc signaling pathway and epithelial‑mesenchymal transition (EMT) progression in HepG2 and Huh7 cells. In addition, upregulated NCAPD2 was shown to have adverse effects on overall survival and disease‑specific survival in liver cancer. In conclusion, the overexpression of NCAPD2 was shown to lead to cell cycle progression at the G2/M‑phase transition, activation of the PI3K‑Akt‑mTOR/c‑Myc signaling pathway and EMT progression in human liver cancer cells.

Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer. Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210. The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens. The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

IntroductionTryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations.MethodsSequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort.ResultsRisk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells.DiscussionOur study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.

Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available. We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints. DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts. DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.