Background: Cholangiocarcinoma is characterized by significant cellular heterogeneity and complex intercellular communication, which contribute to its progression and therapeutic resistance. Therefore, unraveling this complexity is essential for the development of effective treatments. Methods: We employed single-cell RNA sequencing (scRNA-seq) to investigate cellular heterogeneity and intercellular communication in cholangiocarcinoma and adjacent normal tissues from two patients. Distinct cell types were identified, and gene ontology analyses were conducted to determine enriched pathways. Moreover, cell-cell communications were analyzed using CellChat, a computational framework. Additionally, we performed sub-clustering analysis of T cells and fibroblasts. Results: The scRNA-seq analysis revealed distinct cell clusters and diverse cellular compositions of cholangiocarcinoma. CellChat analysis underscored an amplified outgoing signal from fibroblasts within the tumor, suggesting their pivotal role in the tumor microenvironment. Furthermore, T cell sub-clustering analysis revealed an active immune response within the tumor and new tumor-specific T cell clonotypes, suggesting scope for targeted immunotherapies. Moreover, fibroblast sub-clustering analysis indicated distinct functional states and highlighted the role of activated fibroblasts in shaping intercellular communication, particularly via CD99 and FN1 signaling. Conclusion: Our findings reveal the intricate cellular heterogeneity and dynamic intercellular communication in cholangiocarcinoma, providing valuable insights into disease progression and potential therapeutic strategies.

Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC. We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery (n = 287) and validation (n = 28) cohorts, respectively. By univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p < 0.001). Combined with multiple clinical and pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096-6.006; p < 0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8 + T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group. In conclusion, we successfully established an individualized prognostic IRGPs signature, which can accurately assess and predict the OS of patients with PRCC.

No abstract available

Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10-7 to p = 1.76 × 10-5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available.

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Purpose: Demand is increasing for clinical genomic sequencing to provide diagnoses for patients presenting phenotypes indicative of genetic diseases, but for whom routine genetic testing failed to yield a diagnosis. DNA-based testing using high-throughput technologies often identifies variants with insufficient evidence to determine whether they are disease-causal or benign, leading to categorization as variants of uncertain significance (VUS). Methods: We used molecular modeling and simulation to generate specific hypotheses for the molecular effects of variants in the human glucose transporter, GLUT10 (SLC2A10). Similar to many disease-relevant membrane proteins, no experimentally derived 3D structure exists. An atomic model was generated and used to evaluate multiple variants, including pathogenic, benign, and VUS. Results: These analyses yielded detailed mechanistic data, not currently predictable from sequence, including altered protein stability, charge distribution of ligand binding surfaces, and shifts toward or away from transport-competent conformations. Consideration of the two major conformations of GLUT10 was important as variants have conformation-specific effects. We generated detailed molecular hypotheses for the functional impact of variants in GLUT10 and propose means to determine their pathogenicity. Conclusion: The type of workflow we present here is valuable for increasing the throughput and resolution with which VUS effects can be assessed and interpreted.

Colon cancer is a malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divided it into different subtypes to facilitate individualized treatment. With the rise in the use of immunotherapy, its value in the field of tumor has begun to emerge. From the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored the same.

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Background: Glycolysis is critical in the occurrence and development of tumors. Owing to the biological and clinical heterogeneity of patients with BRCA, the traditional predictive classification system is far from satisfactory. Survival and prognosis biomarkers related to glycolysis have broad application prospects for assessing the risk of patients and guiding their individualized treatment. Methods: The mRNA expression profiles and clinical information of patients with BRCA were obtained from TCGA database, and glycolysis-related genes were obtained by GSEA. Patients with BRCA were randomly divided into the training cohort and testing cohort. Univariate and multivariate Cox analyses were used to establish and validate a new mRNA signature for predicting the prognosis of patients with BRCA. Results: We established a four-gene breast cancer prediction signature that included PGK1, SDHC, PFKL, and NUP43. The patients with BRCA in the training cohort and testing cohort were divided into high-risk and low-risk groups based on the signature. The AUC values were 0.74 (training cohort), 0.806 (testing cohort) and 0.769 (entire cohort), thereby showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that four-gene signature could independently predict the prognosis of BRCA patients without being affected by clinical factors. Conclusion: We constructed a four-gene signature to predict the prognosis of patients with BRCA. This signature will aid in the early diagnosis and personalized treatment of breast cancer, but the specific associated biological mechanism requires further study.

Postoperative recurrence of liver cancer is the main obstacle to improving the survival rate of patients with liver cancer. We established an mRNA-based model to predict the risk of recurrence after hepatectomy for liver cancer and explored the relationship between immune infiltration and the risk of recurrence after hepatectomy for liver cancer. We performed a series of bioinformatics analyses on the gene expression profiles of patients with liver cancer, and selected 18 mRNAs as biomarkers for predicting the risk of recurrence of liver cancer using a machine learning method. At the same time, we evaluated the immune infiltration of the samples and conducted a joint analysis of the recurrence risk of liver cancer and found that B cell, B cell naive, T cell CD4+ memory resting, and T cell CD4+ were significantly correlated with the risk of postoperative recurrence of liver cancer. These results are helpful for early detection, intervention, and the individualized treatment of patients with liver cancer after surgical resection, and help to reveal the potential mechanism of liver cancer recurrence.

Autophagy is closely associated with the tumor immune microenvironment (TIME) and prognosis of patients with lung adenocarcinoma (LUAD). In the present study, we established a signature on the basis of long noncoding RNAs (lncRNAs) related to autophagy (ARlncRNAs) to investigate the TIME and survival of patients with LUAD. We selected ARlncRNAs associated with prognosis to construct a model and divided each sample into different groups on the basis of risk score. The ARlncRNA signature could be recognized as an independent prognostic factor for patients with LUAD, and patients in the low-risk group had a greater survival advantage. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis suggested that several immune functions and pathways were enriched in different groups. A high-risk score correlated significantly negatively with high abundance of immune cells and stromal cells around the tumor and high tumor mutational burden. Low-risk patients had a higher PD-1, CTLA-4, and HAVCR2 expression and had a better efficacy of immune checkpoint inhibitors, including PD-1/CTLA-4 inhibitor. A reliable signature on the basis of ARlncRNAs was constructed to explore the TIME and prognosis of patients with LUAD, which could provide valuable information for individualized LUAD treatment.

Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine.

The two aims of this study were (i) to describe and expand the phenotypic spectrum of PIGT deficiency in affected individuals harboring the c.1582G>A; p.Val528Met or the c.1580A > G; p.Asn527Ser variant in either homozygous or compound heterozygous state, and (ii) to identify potential genotype-phenotype correlations and any differences in disease severity among individuals with and without the PIGT variants. The existing literature was searched to identify individuals with and without the two variants. A detailed phenotypic assessment was performed of 25 individuals (both novel and previously published) with the two PIGT variants. We compared severity of disease between individuals with and without these PIGT variants. Twenty-four individuals carried the PIGT variant Val528Met in either homozygous or compound heterozygous state, and one individual displayed the Asn527Ser variant in a compound heterozygous state. Disease severity in the individual with the Asn527Ser variant was compatible with that in the individuals harboring the Val528Met variant. While individuals without the Asn527Ser or Val528Met variant had focal epilepsy, profound developmental delay (DD), and risk of premature death, those with either of the two variants had moderate to severe DD and later onset of epilepsy with both focal and generalized seizures. Individuals homozygous for the Val528Met variant generally became seizure-free on monotherapy with antiepileptic drugs, compared to other PIGT individuals who were pharmaco-resistant. Two patients were diagnosed with myoclonic-atonic seizures, and a single patient was diagnosed with eyelid myoclonia. Our comprehensive analysis of this large cohort of previously published and novel individuals with PIGT variants broadens the phenotypical spectrum and shows that both Asn527Ser and Val528Met are associated with a milder phenotype and less severe outcome. Our data show that PIGT is a new candidate gene for myoclonic atonic epilepsy. Our genotype-phenotype correlation will be useful for future genetic counseling. Natural history studies of this mild spectrum of PIGT-related disorder may shed light on hitherto unknown aspects of this rare disorder.

Background: Chemotherapy resistance based on fluorouracil and cisplatin is one of the most encountered postoperative clinical problems in patients diagnosed with gastric cancer (GC), resulting in poor prognosis. Aim of the Study: This study aimed to combine autophagy-related genes (ARGs) to investigate the susceptibility patients with GC to postoperative chemotherapy. Methods: Based on The Cancer Genome Atlas (TCGA) database, gene expression data for GC patients undergoing chemotherapy were integrated and analyzed. Prognostic genes were screened based on univariate and multivariate analysis regression analysis. Subjects were divided into high-risk and low-risk groups according to the median risk score. Kaplan-Meier method was used to evaluate OS and DFS. The accuracy of the prediction was determined by the subject operating characteristic curve analysis. In addition, stratified analyses based on different clinical variables was performed to assess the correlation between risk scores and clinical variables. Quantitative real-time (qRT) PCR was used to verify the expression of CXCR4 in GC tissues and cell lines. Results: A total of nine ARGs related to the prognosis of chemotherapy patients were screened out. Compared with normal gastric mucosa cell, CXCR4 showed elevated expression in GC and was significantly associated with survival. Based on GEO and TCGA databases, the model accurately predicted DFS and OS after chemotherapy. Conclusion: This study established prognostic markers based on nine genes, predicting that ARGs are related to chemotherapy susceptibility of GC patients, which can provide better individualized treatment regimens for clinical practice.

Bladder cancer (BC) is the most common neoplasia of the urothelial tract. Due to its high incidence, prevalence, recurrence and mortality, it remains an unsolved clinical and social problem. The treatment of BC is challenging and, although immunotherapies have revealed potential benefit in a percentage of patients, it remains mostly an incurable disease at its advanced state. Epigenetic alterations, including aberrant DNA methylation, altered chromatin remodeling and deregulated expression of non-coding RNAs are common events in BC and can be driver events in BC pathogenesis. Accordingly, these epigenetic alterations are now being used as potential biomarkers for these disorders and are being envisioned as potential therapeutic targets for the future management of BC. In this review, we summarize the recent findings in these emerging and exciting new aspects paving the way for future clinical treatment of this disease.

Immunotherapy has been a milestone for muscle-invasive bladder cancer (MIBC), but only a small portion of patients can benefit from it. Therefore, it is crucial to develop a robust individualized immune-related signature of MIBC to identify patients potentially benefiting from immunotherapy. The current study identified patients from the Cancer Genome Atlas (TCGA) and immune genes from the ImmPort database, and used improved data analytical methods to build up a 45 immune-related gene pair signature, which could classify patients into high-risk and low-risk groups. The signature was then independently validated by a Gene Expression Omnibus (GEO) dataset and IMvigor210 data. The subsequent analysis confirmed the worse survival outcomes of the high-risk group in both training (p < 0.001) and validation cohorts (p = 0.018). A signature-based risk score was proven to be an independent risk factor of overall survival (p < 0.001) and could predict superior clinical net benefit compared to other clinical factors. The CIBERSORT algorithm revealed the low-risk group had increased CD8+ T cells plus memory-activated CD4+ T-cell infiltration. The low-risk group also had higher expression of PDCD1 (PD-1), CD40, and CD27, and lower expression of CD276 (B7-H3) and PDCD1LG2 (PD-L2). Importantly, IMvigor210 data indicated that the low-risk group had higher percentage of "inflamed" phenotype plus less "desert" phenotype, and the survival outcomes were significantly better for low-risk patients after immunotherapy (p = 0.014). In conclusion, we proposed a novel and promising prognostic immune-related gene pair (IRGP) signature of MIBC, which could provide us a panoramic view of the tumor immune microenvironment of MIBC and independently identify MIBC patients who might benefit from immunotherapy.

Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.