GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data

Background

Multiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment.

Methods

A total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors.

Results

We found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p <.01).

Conclusion

These data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.

KDM7A-DT induces genotoxic stress, tumorigenesis, and progression of p53 missense mutation-associated invasive breast cancer

Stress-induced promoter-associated and antisense lncRNAs (si-paancRNAs) originate from a reservoir of oxidative stress (OS)-specific promoters via RNAPII pausing-mediated divergent antisense transcription. Several studies have shown that the KDM7A divergent transcript gene (KDM7A-DT), which encodes a si-paancRNA, is overexpressed in some cancer types. However, the mechanisms of this overexpression and its corresponding roles in oncogenesis and cancer progression are poorly understood. We found that KDM7A-DT expression is correlated with highly aggressive cancer types and specific inherently determined subtypes (such as ductal invasive breast carcinoma (BRCA) basal subtype). Its regulation is determined by missense TP53 mutations in a subtype-specific context. KDM7A-DT transcribes several intermediate-sized ncRNAs and a full-length transcript, exhibiting distinct expression and localization patterns. Overexpression of KDM7A-DT upregulates TP53 protein expression and H2AX phosphorylation in nonmalignant fibroblasts, while in semi-transformed fibroblasts, OS superinduces KDM7A-DT expression in a TP53-dependent manner. KDM7A-DT knockdown and gene expression profiling in TP53-missense mutated luminal A BRCA variant, where it is abundantly expressed, indicate its significant role in cancer pathways. Endogenous over-expression of KDM7A-DT inhibits DNA damage response/repair (DDR/R) via the TP53BP1-mediated pathway, reducing apoptosis and promoting G2/M checkpoint arrest. Higher KDM7A-DT expression in BRCA is associated with KDM7A-DT locus gain/amplification, higher histologic grade, aneuploidy, hypoxia, immune modulation scores, and activation of the c-myc pathway. Higher KDM7A-DT expression is associated with relatively poor survival outcomes in patients with luminal A or Basal subtypes. In contrast, it is associated with favorable outcomes in patients with HER2+ER- or luminal B subtypes. KDM7A-DT levels are coregulated with critical transcripts and proteins aberrantly expressed in BRCA, including those involved in DNA repair via non-homologous end joining and epithelial-to-mesenchymal transition pathway. In summary, KDM7A-DT and its si-lncRNA exhibit several intrinsic biological and clinical characteristics that suggest important roles in invasive BRCA and its subtypes. KDM7A-DT-defined mRNA and protein subnetworks offer resources for identifying clinically relevant RNA-based signatures and prospective targets for therapeutic intervention.

Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations

Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, particularly in women. EGFR mutations and anaplastic lymphoma kinase (ALK) fusions are major genetic alterations observed in NSLA, and NSLA with these alterations have been well studied and can be treated with targeted therapies. To provide insights into the molecular profile of NSLA without EGFR and ALK alterations (NENA), we selected 141 NSLA tissues and performed proteogenomic characterization, including whole genome sequencing (WGS), transcriptomic, methylation EPIC array, total proteomic, and phosphoproteomic analyses. Forty patients with NSLA harboring EGFR and ALK alterations and seven patients with NENA with microsatellite instability were excluded. Genome analysis revealed that TP53 (25%), KRAS (22%), and SETD2 (11%) mutations and ROS1 fusions (14%) were the most frequent genetic alterations in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broad effects on cancer-associated genes in NENA. DNA copy number alteration analysis identified 22 prognostic proteins that influenced transcriptomic and proteomic changes. Gene set enrichment analysis revealed estrogen signaling as the key pathway activated in NENA. Increased estrogen signaling was associated with proteogenomic alterations, such as copy number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was identified as a potential drug for targeting activated estrogen signaling in NENA and was experimentally validated in vitro. Collectively, this study enhanced our understanding of NENA NSLA by elucidating the proteogenomic landscape and proposed saracatinib as a potential treatment for this patient population that lacks effective targeted therapies.

SIGNIFICANCE

The proteogenomic landscape in never-smoker lung cancer without known driver mutations reveals prognostic proteins and enhanced estrogen signaling that can be targeted as a potential therapeutic strategy to improve patient outcomes.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma

BACKGROUND

Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures.

METHODS

In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells.

RESULTS

Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients.

CONCLUSIONS

ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Integrated analyses of the genetic and clinicopathological features of cholangiolocarcinoma: cholangiolocarcinoma may be characterized by mismatch-repair deficiency

Cholangiolocarcinoma (CLC) is a primary liver carcinoma that resembles the canals of Hering and that has been reported to be associated with stem cell features. Due to its rarity, the nature of CLC remains unclear, and its pathological classification remains controversial. To clarify the positioning of CLC in primary liver cancers and identify characteristics that could distinguish CLC from other liver cancers, we performed integrated analyses using whole-exome sequencing (WES), immunohistochemistry, and a retrospective review of clinical information on eight CLC cases and two cases of recurrent CLC. WES demonstrated that CLC includes IDH1 and BAP1 mutations, which are characteristic of intrahepatic cholangiocarcinoma (iCCA). A mutational signature analysis showed a pattern similar to that of iCCA, which was different from that of hepatocellular carcinoma (HCC). CLC cells, including CK7, CK19, and EpCAM, were positive for cholangiocytic differentiation markers. However, the hepatocytic differentiation marker AFP and stem cell marker SALL4 were completely negative. The immunostaining patterns of CLC with CD56 and epithelial membrane antigen were similar to those of the noncancerous bile ductules. In contrast, mutational signature cluster analyses revealed that CLC formed a cluster associated with mismatch-repair deficiency (dMMR), which was separate from iCCA. Therefore, to evaluate MMR status, we performed immunostaining of four MMR proteins (PMS2, MSH6, MLH1, and MSH2) and detected dMMR in almost all CLCs. In conclusion, CLC had highly similar characteristics to iCCA but not to HCC. CLC can be categorized as a subtype of iCCA. In contrast, CLC has characteristics of dMMR tumors that are not found in iCCA, suggesting that it should be treated distinctly from iCCA. © 2024 The Pathological Society of Great Britain and Ireland.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

A comprehensive comparison of molecular and phenotypic profiles between hepatitis B virus (HBV)-infected and non-HBV-infected hepatocellular carcinoma by multi-omics analysis

Hepatitis B virus (HBV) infection is a major etiology of hepatocellular carcinoma (HCC). An interesting question is how different are the molecular and phenotypic profiles between HBV-infected (HBV+) and non-HBV-infected (HBV-) HCCs? Based on the publicly available multi-omics data for HCC, including bulk and single-cell data, and the data we collected and sequenced, we performed a comprehensive comparison of molecular and phenotypic features between HBV+ and HBV- HCCs. Our analysis showed that compared to HBV- HCCs, HBV+ HCCs had significantly better clinical outcomes, higher degree of genomic instability, higher enrichment of DNA repair and immune-related pathways, lower enrichment of stromal and oncogenic signaling pathways, and better response to immunotherapy. Furthermore, in vitro experiments confirmed that HBV+ HCCs had higher immunity, PD-L1 expression and activation of DNA damage response pathways. This study may provide insights into the profiles of HBV+ and HBV- HCCs, and guide rational therapeutic interventions for HCC patients.

ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer

Background

Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment.

Methods

We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression.

Results

Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes.

Conclusion

ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.

Integrative multi-omics characterization of hepatocellular carcinoma in Hispanic patients

Background

The incidence and mortality rates of hepatocellular carcinoma (HCC) among Hispanics in the United States are much higher than those of non-Hispanic whites. We conducted comprehensive multi-omics analyses to understand molecular alterations in HCC among Hispanic patients.

Methods

Paired tumor and adjacent non-tumor samples were collected from 31 Hispanic HCC in South Texas (STX-Hispanic) for genomic, transcriptomic, proteomic, and metabolomic profiling. Additionally, serum lipids were profiled in 40 Hispanic and non-Hispanic patients with or without clinically diagnosed HCC.

Results

Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1 in STX Hispanic HCCs, suggesting a predominant activation of the Wnt/β-catenin pathway. The TERT promoter mutation frequency was also remarkably high in the Hispanic cohort. Cell cycles and liver functions were identified as positively- and negatively-enriched, respectively, with gene set enrichment analysis. Gene sets representing specific liver metabolic pathways were associated with dysregulation of corresponding metabolites. Negative enrichment of liver adipogenesis and lipid metabolism corroborated with a significant reduction in most lipids in the serum samples of HCC patients. Two HCC subtypes from our Hispanic cohort were identified and validated with the TCGA liver cancer cohort. The subtype with better overall survival showed higher activity of immune and angiogenesis signatures, and lower activity of liver function-related gene signatures. It also had higher levels of immune checkpoint and immune exhaustion markers.

Conclusions

Our study revealed some specific molecular features of Hispanic HCC and potential biomarkers for therapeutic management of HCC and provides a unique resource for studying Hispanic HCC.

Pan-cancer analysis of polo-like kinase family genes reveals polo-like kinase 1 as a novel oncogene in kidney renal papillary cell carcinoma

Background

Polo-like kinases (PLKs) are a kinase class of serine/threonine with five members that play crucial roles in cell cycle regulation. However, their biological functions, regulation, and expression remain unclear. This study revealed the molecular properties, oncogenic role, and clinical significance of PLK genes in pan-cancers, particularly in kidney renal papillary cell carcinoma (KIRP).

Methods

We evaluated the mutation landscape, expression level, and prognostic values of PLK genes using bioinformatics analyses and explored the association between the expression level of PLK genes and tumor microenvironment (TME), immune subtype, cancer immunotherapy, tumor stemness, and drug sensitivity. Finally, we verified the prognostic value in patients with KIRP through univariate and multivariate analyses and nomogram construction.

Results

PLK genes are extensively altered in pan-cancer, which may contribute to tumorigenesis. These genes are aberrantly expressed in some types of cancer, with PLK1 being overexpressed in 31 cancers. PLK expression is closely associated with the prognosis of various cancers. The expression level of PLK genes is related with sensitivity to diverse drugs and cancer immunity as well as cancer immunotherapy. Importantly, we verified that PLK1 was overexpressed in KIRP tissues and could be an unfavorable prognostic biomarker in patients with KIRP. Hence, PLK1 may serve as an oncogenic gene in KIRP and should be explored in future studies.

Conclusions

Our study comprehensively reports the molecular characteristics and biological functions of PLK family gens across human cancers and recommends further investigation of these genes as potential biomarkers and therapeutic targets, especially in KIRP.

The S100 family is a prognostic biomarker and correlated with immune cell infiltration in pan-cancer

BACKGROUND

The S100 protein family is a group of small molecular EF-hand calcium-binding proteins that play critical roles in various biological processes, including promotion of growth, metastasis and immune evasion of tumor. However, the potential roles of S100 protein family expression in tumor microenvironment (TME) cell infiltration in pan-cancer remain elusive.

METHODS

Herein, we conducted a comprehensive assessment of the expression patterns of the S100 protein family in pan-cancer, meticulously examining their correlation with characteristics of TME cell infiltration. The S100 score was constructed to quantify S100 family expression patterns of individual tumors.

RESULTS

The S100 family was a potent risk factor in many cancers. Clustering analysis based on the transcriptome patterns of S100 protein family identified two cancer clusters with distinct immunophenotypes and clinical characteristics. Cluster A, with lower S100 expression, exhibited lower immune infiltration, whereas, Cluster B, with higher S100 expression, featured higher immune infiltration. Interestingly, Cluster B had a poorer prognosis, likely due to an immune-excluded phenotype resulting from stromal activation. The analysis revealed robust enrichment of the TGFb and EMT pathways in the cohort exhibiting high S100 score, alongside a positive correlation between the S100 score and Treg levels, suggesting the manifestation of an immune-excluded phenotype in this group. Moreover, S100 families were associated with the prognosis of 22 different cancers and a noteworthy association was observed between high S100 score and an unfavorable response to anti-PD-1/L1 immunotherapy. Consistent findings across two independent immunotherapy cohorts substantiated the advantageous therapeutic outcomes and clinical benefits in patients displaying lower S100score.

CONCLUSION

Our analysis demonstrated the role of S100 family in formation of TME diversity and complexity, enabling deeper cognition of TME infiltration characterization and the development of personalized immunotherapy strategies targeting S100 family for unique tumor types.

NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes

OBJECTIVE

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics.

METHOD

Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.

RESULTS

We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development.

CONCLUSION

Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

A glycolytic metabolite bypasses "two-hit" tumor suppression by BRCA2

Knudson's "two-hit" paradigm posits that carcinogenesis requires inactivation of both copies of an autosomal tumor suppressor gene. Here, we report that the glycolytic metabolite methylglyoxal (MGO) transiently bypasses Knudson's paradigm by inactivating the breast cancer suppressor protein BRCA2 to elicit a cancer-associated, mutational single-base substitution (SBS) signature in nonmalignant mammary cells or patient-derived organoids. Germline monoallelic BRCA2 mutations predispose to these changes. An analogous SBS signature, again without biallelic BRCA2 inactivation, accompanies MGO accumulation and DNA damage in Kras-driven, Brca2-mutant murine pancreatic cancers and human breast cancers. MGO triggers BRCA2 proteolysis, temporarily disabling BRCA2's tumor suppressive functions in DNA repair and replication, causing functional haploinsufficiency. Intermittent MGO exposure incites episodic SBS mutations without permanent BRCA2 inactivation. Thus, a metabolic mechanism wherein MGO-induced BRCA2 haploinsufficiency transiently bypasses Knudson's two-hit requirement could link glycolysis activation by oncogenes, metabolic disorders, or dietary challenges to mutational signatures implicated in cancer evolution.

High clonal diversity and spatial genetic admixture in early prostate cancer and surrounding normal tissue

Somatic copy number alterations (SCNAs) are pervasive in advanced human cancers, but their prevalence and spatial distribution in early-stage, localized tumors and their surrounding normal tissues are poorly characterized. Here, we perform multi-region, single-cell DNA sequencing to characterize the SCNA landscape across tumor-rich and normal tissue in two male patients with localized prostate cancer. We identify two distinct karyotypes: 'pseudo-diploid' cells harboring few SCNAs and highly aneuploid cells. Pseudo-diploid cells form numerous small-sized subclones ranging from highly spatially localized to broadly spread subclones. In contrast, aneuploid cells do not form subclones and are detected throughout the prostate, including normal tissue regions. Highly localized pseudo-diploid subclones are confined within tumor-rich regions and carry deletions in multiple tumor-suppressor genes. Our study reveals that SCNAs are widespread in normal and tumor regions across the prostate in localized prostate cancer patients and suggests that a subset of pseudo-diploid cells drive tumorigenesis in the aging prostate.

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer

BACKGROUND

Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those related to mitochondrial function, is crucial for its development and progression. However, more research is needed to investigate the significance of the interaction between mitochondrial function and GC cell death.

METHODS

We employed a robust computational framework to investigate the role of mitochondria-associated proteins in the progression of GC in a cohort of 1,199 GC patients. Ten machine learning algorithms were utilized and combined into 101 unique combinations. Ultimately, we developed a Mitochondrial-related-Score (MitoScore) using the machine learning model that exhibited the best performance. We observed the upregulation of LEMT2 and further explored its function in tumor progression. Mitochondrial functions were assessed by measuring mitochondrial ATP, mitochondrial membrane potential, and levels of lactate, pyruvate, and glucose.

RESULTS

MitoScore showed significant correlations with GC immune and metabolic functions. The higher MitoScore subgroup exhibited enriched metabolic pathways and higher immune activity. Overexpression of LETM2 (leucine zipper and EF-hand containing transmembrane protein 2) significantly enhanced tumor proliferation and metastasis. LETM2 plays a role in promoting GC cell proliferation by activating the mTOR pathway, maintaining mitochondrial homeostasis, and promoting glycolysis.

CONCLUSION

The powerful machine learning framework highlights the significant potential of MitoScore in providing valuable insights and accurate assessments for individuals with GC. This study also enhances our understanding of LETM2 as an oncogene signature in GC. LETM2 may promote tumor progression by maintaining mitochondrial health and activating glycolysis, offering potential targets for diagnosis, treatment, and prognosis of GC.

Integrated mutational landscape analysis of poorly differentiated high-grade neuroendocrine carcinoma of the uterine cervix

High-grade neuroendocrine cervical cancers (NETc) are exceedingly rare, highly aggressive tumors. We analyzed 64 NETc tumor samples by whole-exome sequencing (WES). Human papillomavirus DNA was detected in 65.6% (42/64) of the tumors. Recurrent mutations were identified in PIK3CA, KMT2D/MLL2, K-RAS, ARID1A, NOTCH2, and RPL10. The top mutated genes included RB1, ARID1A, PTEN, KMT2D/MLL2, and WDFY3, a gene not yet implicated in NETc. Somatic CNV analysis identified two copy number gains (3q27.1 and 19q13.12) and five copy number losses (1p36.21/5q31.3/6p22.2/9q21.11/11p15.5). Also, gene fusions affecting the ACLY-CRHR1 and PVT1-MYC genes were identified in one of the eight samples subjected to RNA sequencing. To resolve evolutionary history, multiregion WES in NETc admixed with adenocarcinoma cells was performed (i.e., mixed-NETc). Phylogenetic analysis of mixed-NETc demonstrated that adenocarcinoma and neuroendocrine elements derive from a common precursor with mutations typical of adenocarcinomas. Over one-third (22/64) of NETc demonstrated a mutator phenotype of C > T at CpG consistent with deficiencies in MBD4, a member of the base excision repair (BER) pathway. Mutations in the PI3K/AMPK pathways were identified in 49/64 samples. We used two patient-derived-xenografts (PDX) (i.e., NET19 and NET21) to evaluate the activity of pan-HER (afatinib), PIK3CA (copanlisib), and ATR (elimusertib) inhibitors, alone and in combination. PDXs harboring alterations in the ERBB2/PI3K/AKT/mTOR/ATR pathway were sensitive to afatinib, copanlisib, and elimusertib (P < 0.001 vs. controls). However, combinations of copanlisib/afatinib and copanlisib/elimusertib were significantly more effective in controlling NETc tumor growth. These findings define the genetic landscape of NETc and suggest that a large subset of these highly lethal malignancies might benefit from existing targeted therapies.

Somatic CDKN2A copy number variations are associated with the prognosis of esophageal squamous cell dysplasia

BACKGROUND

Somatic copy number variations (SCNVs) in the CDKN2A gene are among the most frequent events in the dysplasia-carcinoma sequence of esophageal squamous cell carcinoma. However, whether CDKN2A SCNVs are useful biomarkers for the risk stratification and management of patients with esophageal squamous cell dysplasia (ESCdys) is unknown. This study aimed to investigate the characteristics and prognostic value of CDKN2A SCNVs in patients with mild or moderate (m/M) ESCdys.

METHODS

This study conducted a prospective multicenter study of 205 patients with a baseline diagnosis of m/M ESCdys in five high-risk regions of China (Ci County, Hebei Province; Yanting, Sichuan Province; Linzhou, Henan Province; Yangzhong, Jiangsu Province; and Feicheng, Shandong Province) from 2005 to 2019. Genomic DNA was extracted from paraffin biopsy samples and paired peripheral white blood cells from patients, and a quantitative polymerase chain reaction assay, P16-Light, was used to detect CDKN2A copy number. The cumulative regression and progression rates of ESCdys were evaluated using competing risk models.

RESULTS

A total of 205 patients with baseline m/M ESCdys were enrolled. The proportion of ESCdys regression was significantly lower in the CDKN2A deletion cohort than in the diploid and amplification cohorts (18.8% [13/69] vs. 35.0% [28/80] vs. 51.8% [29/56], P  <0.001). In the univariable competing risk analysis, the cumulative regression rate was statistically significantly lower ( P = 0.008), while the cumulative progression rate was higher ( P = 0.017) in ESCdys patients with CDKN2A deletion than in those without CDKN2A deletion. CDKN2A deletion was also an independent predictor of prognosis in ESCdys ( P = 0.004) in the multivariable analysis.

CONCLUSION

The results indicated that CDKN2A SCNVs are associated with the prognosis of ESCdys and may serve as potential biomarkers for risk stratification.

Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored. Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptor expression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Mutational landscape of inflammatory breast cancer

BACKGROUND

Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples.

METHODS

We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes.

RESULTS

The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1-2, ESCAT LOE I-II), similarly to patients with non-IBC.

CONCLUSIONS

We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets

Introduction

Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies.

Methods

Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified.

Results

Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified.

Conclusion

In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention.

Haploinsufficiency of phosphodiesterase 10A activates PI3K/AKT signaling independent of PTEN to induce an aggressive glioma phenotype

Glioblastoma is universally fatal and characterized by frequent chromosomal copy number alterations harboring oncogenes and tumor suppressors. In this study, we analyzed exome-wide human glioblastoma copy number data and found that cytoband 6q27 is an independent poor prognostic marker in multiple data sets. We then combined CRISPR-Cas9 data, human spatial transcriptomic data, and human and mouse RNA sequencing data to nominate PDE10A as a potential haploinsufficient tumor suppressor in the 6q27 region. Mouse glioblastoma modeling using the RCAS/tv-a system confirmed that Pde10a suppression induced an aggressive glioma phenotype in vivo and resistance to temozolomide and radiation therapy in vitro. Cell culture analysis showed that decreased Pde10a expression led to increased PI3K/AKT signaling in a Pten-independent manner, a response blocked by selective PI3K inhibitors. Single-nucleus RNA sequencing from our mouse gliomas in vivo, in combination with cell culture validation, further showed that Pde10a suppression was associated with a proneural-to-mesenchymal transition that exhibited increased cell adhesion and decreased cell migration. Our results indicate that glioblastoma patients harboring PDE10A loss have worse outcomes and potentially increased sensitivity to PI3K inhibition.

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

1 Background

In lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices.

2 Methods

We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups.

3 Results

We identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy.

4 Conclusion

The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome

Background

Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed.

Methods

Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)-as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS).

Results

Based on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS.

Conclusion

CIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.

Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer

Inter- and intra-tumor heterogeneity is a major hurdle in primary liver cancer (PLC) precision therapy. Here, we establish a PLC biobank, consisting of 399 tumor organoids derived from 144 patients, which recapitulates histopathology and genomic landscape of parental tumors, and is reliable for drug sensitivity screening, as evidenced by both in vivo models and patient response. Integrative analysis dissects PLC heterogeneity, regarding genomic/transcriptomic characteristics and sensitivity to seven clinically relevant drugs, as well as clinical associations. Pharmacogenomic analysis identifies and validates multi-gene expression signatures predicting drug response for better patient stratification. Furthermore, we reveal c-Jun as a major mediator of lenvatinib resistance through JNK and β-catenin signaling. A compound (PKUF-01) comprising moieties of lenvatinib and veratramine (c-Jun inhibitor) is synthesized and screened, exhibiting a marked synergistic effect. Together, our study characterizes the landscape of PLC heterogeneity, develops predictive biomarker panels, and identifies a lenvatinib-resistant mechanism for combination therapy.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

The correlation between clinical outcomes and genomic analysis with high risk factors for the progression of osteosarcoma

Osteosarcoma (OS) is a rare but aggressive malignancy. Despite previous reports, molecular characterization of this disease is not well understood, and little is known regarding OS in Chinese patients. Herein, we analyzed the genomic signatures of 73 Chinese OS cases. TP53, NCOR1, LRP1B, ATRX, RB1, and TFE3 were the most frequently mutated gene in our OS cohort. In addition, the genomic analysis of Western OS patients was performed. Notably, there were remarkable disparities in mutational landscape, base substitution pattern, and tumor mutational burden between the Chinese and Western OS cohorts. Specific molecular mechanisms, including DNA damage repair (DDR) gene mutations, copy number variation (CNV) presence, aneuploidy, and intratumoral heterogeneity, were associated with disease progression. Additionally, 30.1% of OS patients carried clinically actionable alterations, which were mainly enriched in PI3K, MAPK, DDR, and RTK signaling pathways. A specific molecular subtype incorporating DDR alterations and CNVs was significantly correlated with distant metastasis-free survival and event-free survival, and this correlation was observed in all subgroups with different characteristics. These findings comprehensively elucidated the genomic profile and revealed novel prognostic factors in OS, which would contribute to understanding this disease and promoting precision medicine of this population.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

An integrative pan-cancer analysis of the molecular characteristics of dietary restriction in tumour microenvironment

BACKGROUND

Dietary restriction (DR), a general term for dieting, has been demonstrated as an effective intervention in reducing the occurrence of cancers. Molecular activities associated with DR are crucial in mediating its anti-cancer effects, yet a comprehensive exploration of the landscape of these activities at the pan-cancer level is still lacking.

METHODS

We proposed a computational approach for quantifying DR-related molecular activities and delineating the landscape of these activities across 33 cancer types and 30 normal tissues within 27,320 samples. We thoroughly examined the associations between DR-related molecular activities and various factors, including the tumour microenvironment, immunological phenotypes, genomic features, and clinical prognosis. Meanwhile, we identified two DR genes that show potential as prognostic predictors in hepatocellular carcinoma and verified them by immunohistochemical assays in 90 patients.

FINDINGS

We found that DR-related molecular activities showed a close association with tumour immunity and hold potential for predicting immunotherapy responses in various cancers. Importantly, a higher level of DR-related molecular activities is associated with improved overall survival and cancer-specific survival. FZD1 and G6PD are two DR genes that serve as biomarkers for predicting the prognosis of patients with hepatocellular carcinoma.

INTERPRETATION

This study presents a robust link between DR-related molecular activities and tumour immunity across multiple cancer types. Our research could open the path for further investigation of DR-related molecular processes in cancer treatment.

FUNDING

National Natural Science Foundation of China (Grant No. 82000628) and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (Grant No. 2023LSYS001).

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer

PURPOSE

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures.

METHODS

In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs).

RESULTS

We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level.

CONCLUSION

Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

SPCS, a Novel Classifier System Based on Senescence Axis Regulators Reveals Tumor Microenvironment Heterogeneity and Guides Frontline Therapy for Clear Cell Renal Carcinoma

RATIONALE

The emerging evidence suggested that senescence regulator genes were involved in multi cancers, which may be utilized as new targets for cancers. However, the dysregulation and clinical impact of senescence regulator genes in clear cell renal cell cancer (ccRCC) were still in foggy.

METHODS

Using multiomics data from TCGA-KIRC and other datasets, we comprehensively investigated the function of senescence regulator genes in ccRCC. ccRCC patients could be remodeled into 2 significant different groups basing on senescence regulators expression: senescence-pattern cancer subtype1 (SPCS1) and subtype2 (SPCS2). We further explored clinical characteristics, functional analysis, tumor immune microenvironment, immunotherapy response, genomic mutation and drug sensitivity between the 2 subtypes. Besides, senescence-pattern related risk model was established to determine the patient's prognosis of ccRCC. Finally, the overview of MECP2 function was investigated in multi cancers.

RESULTS

ccRCC patients could be divided into SPCS1 (normal aging group) and SPCS2 (Aging disorder group). The 2 subtypes showed significant different clinical characteristics and biological process in ccRCC. SPCS2, an aggressive subtype, comprised higher clinical stage and worse prognosis of ccRCC patients. SPCS2 subtype indicated activated oncogenic signaling pathway and metabolic signatures to prompt cancer expansion. SPCS2 subgroup owned immunocompromised status, which induced immune dysfunction and low ICI therapy response. The genome-copy numbers of SPCS2, including arm-gain and arm-loss was significantly more frequent than SPCS1. In addition, the 2 subtypes argue contrasting drug sensitivity profiles in clinical specimens and matched cell lines. Finally, we constructed a prognostic risk model consisted of each subtype's leading biomarkers, which exerted a satisfied performance for ccRCC patients.

CONCLUSION

Senescence regulator-related signature could modify functional pathways and tumor immune microenvironment by genome mutation and pathway interaction. Senescence regulator-related molecular subtype strengthen the understanding of ccRCC' characterization and guide clinical treatment. Targeting senescence regulators may be regard as a proper way in ccRCC.

TTN Mutation in Endometrial Endometrioid Carcinoma Is Associated with Poor Clinical Outcomes and High Tumor Mutation Burden

Endometrioid endometrial carcinoma (EEC) stands as a prevalent gynecologic malignancy in developed regions. However, predicting relapse cases remains challenging, necessitating the identification of a novel biomarker for EEC relapse. The assessment of tumor mutational burden (TMB) is pivotal for immunotherapy in EEC patients. However, both whole-exome sequencing (WES) and targeted sequencing encountered application-related difficulties. In light of this, standardized and simplified techniques for TMB measurement are imperative. In this study, we employed WES on 25 EEC patients (12 relapsed cases and 13 non-relapsed cases) who accepted hysterectomy surgery (CHCAMS cohort). We additionally obtained a total of 391 tumor samples with clinicopathological features from TCGA website to broaden the study cohort. In the CHCAMS cohort, the TTN mutant group showed shorter progression-free survival (p < 0.001) and overall survival (p < 0.001) than TTN wild-type group. Additionally, we discovered that the number of TTN mutations per sample was significantly linked with TMB-WES in CHCAMS cohort and TCGA cohort (p < 0.05). And the number of TTN mutations per sample in POLE mutant group was greater than in the POLE wild-type group (p < 0.0001). In conclusion, TTN mutation may serve as a biomarker for EEC prognosis. TTN mutation is also associated with WES-TMB, and could be a simplified TMB measurement technique.

Investigation of GPR143 as a promising novel marker for the progression of skin cutaneous melanoma through bioinformatic analyses and cell experiments

BACKGROUND

Skin cutaneous melanoma (SKCM) is an aggressive and life-threatening skin cancer. G-protein coupled receptor 143 (GPR143) belongs to the superfamily of G protein-coupled receptors.

METHODS

We used the TCGA, GTEx, CCLE, and the Human Protein Atlas databases to examine the mRNA and protein expression of GPR143. In addition, we performed a survival analysis and evaluated the diagnostic efficacy using the Receiver-Operating Characteristic (ROC) curve. Through CIBERSORT, R programming, TIMER, Gene Expression Profiling Interactive Analysis, Sangerbox, and Kaplan-Meier plotter database analyses, we explored the relationships between GPR143, immune infiltration, and gene marker expression of immune infiltrated cells. Furthermore, we investigated the proteins that potentially interact with GPR143 and their functions using R programming and databases including STRING, GeneMANIA, and GSEA. Meanwhile, the cBioPortal, UALCNA, and the MethSurv databases were used to examine the genomic alteration and methylation of GPR143 in SKCM. The Connectivity Map database was used to discover potentially effective therapeutic molecules against SKCM. Finally, we conducted cell experiments to investigate the potential role of GPR143 in SKCM.

RESULTS

We demonstrated a significantly high expression level of GPR143 in SKCM compared with normal tissues. High GPR143 expression and hypomethylation status of GPR143 were associated with a poorer prognosis. ROC analysis showed that the diagnostic efficacy of the GPR143 was 0.900. Furthermore, GPR143 expression was significantly correlated with immune infiltration in SKCM. We identified 20 neighbor genes and the pathways they enriched were anabolic process of pigmentation, immune regulation, and so on. Genomic alteration analysis revealed significantly different copy number variations related to GPR143 expression in SKCM, and shallow deletion could lead to high expression of GPR143. Ten potential therapeutic drugs against SKCM were identified. GPR143 knockdown inhibited melanoma cell proliferation, migration, and colony formation while promoting apoptosis.

CONCLUSIONS

Our findings suggest that GPR143 serves as a novel diagnostic and prognostic biomarker and is associated with the progression of SKCM.

Pan-cancer analyses suggest kindlin-associated global mechanochemical alterations

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial-mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.

The genomic and evolutionary landscapes of anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

NAB2::STAT6 fusions and genome-wide DNA methylation profiling: Predictors of patient outcomes in meningeal solitary fibrous tumors

Meningeal solitary fibrous tumors (SFT) are rare and have a high frequency of local recurrence and distant metastasis. In a cohort of 126 patients (57 female, 69 male; mean age at surgery 53.0 years) with pathologically confirmed meningeal SFTs with extended clinical follow-up (median 9.9 years; range 15 days-43 years), we performed extensive molecular characterization including genome-wide DNA methylation profiling (n = 80) and targeted TERT promoter mutation testing (n = 98). Associations were examined with NAB2::STAT6 fusion status (n = 101 cases; 51 = ex5-7::ex16-17, 26 = ex4::ex2-3; 12 = ex2-3::exANY/other and 12 = no fusion) and placed in the context of 2021 Central Nervous System (CNS) WHO grade. NAB2::STAT6 fusion breakpoints (fusion type) were significantly associated with metastasis-free survival (MFS) (p = 0.03) and, on multivariate analysis, disease-specific survival (DSS) when adjusting for CNS WHO grade (p = 0.03). DNA methylation profiling revealed three distinct clusters: Cluster 1 (n = 38), Cluster 2 (n = 22), and Cluster 3 (n = 20). Methylation clusters were significantly associated with fusion type (p < 0.001), with Cluster 2 harboring ex4::ex2-3 fusion in 16 (of 20; 80.0%), nearly all TERT promoter mutations (7 of 8; 87.5%), and predominantly an "SFT" histologic phenotype (15 of 22; 68.2%). Clusters 1 and 3 were less distinct, both dominated by tumors having ex5-7::ex16-17 fusion (respectively, 25 of 33; 75.8%, and 12 of 18; 66.7%) and with variable histological phenotypes. Methylation clusters were significantly associated with MFS (p = 0.027), but not overall survival (OS). In summary, NAB2::STAT6 fusion type was significantly associated with MFS and DSS, suggesting that tumors with an ex5::ex16-17 fusion may have inferior patient outcomes. Methylation clusters were significantly associated with fusion type, TERT promoter mutation status, histologic phenotype, and MFS.

DNA hypomethylation patterns and their impact on the tumor microenvironment in colorectal cancer

BACKGROUND

Recent research underscores the pivotal role of immune checkpoints as biomarkers in colorectal cancer (CRC) therapy, highlighting the dynamics of resistance and response to immune checkpoint inhibitors. The impact of epigenetic alterations in CRC, particularly in relation to immune therapy resistance, is not fully understood.

METHODS

We integrated a comprehensive dataset encompassing TCGA-COAD, TCGA-READ, and multiple GEO series (GSE14333, GSE37892, GSE41258), along with key epigenetic datasets (TCGA-COAD, TCGA-READ, GSE77718). Hierarchical clustering, based on Euclidean distance and Ward's method, was applied to 330 primary tumor samples to identify distinct clusters. The immune microenvironment was assessed using MCPcounter. Machine learning algorithms were employed to predict DNA methylation patterns and their functional enrichment, in addition to transcriptome expression analysis. Genomic mutation profiles and treatment response assessments were also conducted.

RESULTS

Our analysis delineated a specific tumor cluster with CpG Island (CGI) methylation, termed the Demethylated Phenotype (DMP). DMP was associated with metabolic pathways such as oxidative phosphorylation, implicating increased ATP production efficiency in mitochondria, which contributes to tumor aggressiveness. Furthermore, DMP showed activation of the Myc target pathway, known for tumor immune suppression, and exhibited downregulation in key immune-related pathways, suggesting a tumor microenvironment characterized by diminished immunity and increased fibroblast infiltration. Six potential therapeutic agents-lapatinib, RDEA119, WH.4.023, MG.132, PD.0325901, and AZ628-were identified as effective for the DMP subtype.

CONCLUSION

This study unveils a novel epigenetic phenotype in CRC linked to resistance against immune checkpoint inhibitors, presenting a significant step toward personalized medicine by suggesting epigenetic classifications as a means to identify ideal candidates for immunotherapy in CRC. Our findings also highlight potential therapeutic agents for the DMP subtype, offering new avenues for tailored CRC treatment strategies.

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma

INTRODUCTION

Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive.

METHODS

We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining.

RESULTS

Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB.

CONCLUSION

Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BACKGROUND

Glioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1-4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.

RESULTS

Our analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.

CONCLUSION

Our findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.

Follicular lymphoma B cells exhibit heterogeneous transcriptional states with associated somatic alterations and tumor microenvironments

Follicular lymphoma (FL) is an indolent non-Hodgkin lymphoma of germinal center origin, which presents with significant biologic and clinical heterogeneity. Using RNA-seq on B cells sorted from 87 FL biopsies, combined with machine-learning approaches, we identify 3 transcriptional states that divide the biological ontology of FL B cells into inflamed, proliferative, and chromatin-modifying states, with relationship to prior GC B cell phenotypes. When integrated with whole-exome sequencing and immune profiling, we find that each state was associated with a combination of mutations in chromatin modifiers, copy-number alterations to TNFAIP3, and T follicular helper cells (Tfh) cell interactions, or primarily by a microenvironment rich in activated T cells. Altogether, these data define FL B cell transcriptional states across a large cohort of patients, contribute to our understanding of FL heterogeneity at the tumor cell level, and provide a foundation for guiding therapeutic intervention.

Correlation between S100A7 and immune characteristics, methylation, tumor stemness and tumor heterogeneity in pan-cancer and its role in chemotherapy resistance in breast cancer

OBJECTIVE

To explore the relationships between S100A7 and the immune characteristics, tumor heterogeneity, and tumor stemness pan-cancer as well as the effect of S100A7 on chemotherapy sensitivity in breast cancer.

METHODS

TCGA-BRCA and TCGA-PANCANCER RNA-seq data and clinical follow-up survival data were collected from the University of California Santa Cruz database. Survival analyses were performed to explore the relationship between S100A7 expression and pan-cancer prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were used to identify the potential pathways related to the differentially expressed genes in breast cancer. Spearman's and Wilcoxon's tests were used to investigate the relationships between S100A7 expression and immune characteristics, methylation, tumor heterogeneity, and tumor stemness. The potential functions of S100A7 and its influence on chemotherapy sensitivity in breast cancer were elucidated using reverse transcription-quantitative PCR, Cell Counting Kit-8 (CCK-8) assay, Transwell assay, and wound healing assay.

RESULTS

S100A7 was highly expressed in most types of tumors and was associated with poor prognosis. S100A7 was closely associated with immunomodulators, immune checkpoint and immune cell infiltration. Further, S100A7 was related to tumor mutational burden, tumor heterogeneity, methylation and tumor stemness in breast cancer. High S100A7 expression was associated with the invasiveness, migration, proliferation and chemotherapy resistance of breast cancer cells in vitro experiments.

CONCLUSION

High S100A7 expression was related with poor prognosis and chemotherapy resistance in breast cancer, making it a potential immune and chemotherapy resistance biomarker.

Comprehensive analysis of zinc and ring finger 3 in prognostic value and pan-cancer immunity

Zinc and ring finger 3 (ZNRF3) is a negative suppressor of Wnt signal and newly identified as an important regulator in tumorigenesis and development. However, the pan-cancer analysis of ZNRF3 has not been reported. We found that ZNRF3 was significantly decreased in six tumors including CESC, KIRP, KIRC, SKCM, OV, and ACC, but increased in twelve tumors, namely LGG, ESCA, STES, COAD, STAD, LUSC, LIHC, THCA, READ, PAAD, TGCT, and LAML. Clinical outcomes of cancer patients were closely related to ZNRF3 expression in ESCA, GBM, KIRC, LUAD, STAD, UCEC, LGG, and SARC. The highest genetic alteration frequency of ZNRF3 occurred in ACC. Abnormal expression of ZNRF3 could be attributed to the differences of copy number variation (CNV) and DNA methylation as well as ZNRF3-interacting proteins. Besides, ZNRF3 were strongly associated with tumor heterogeneity, tumor stemness, immune score, stromal score and ESTIMATE score in certain cancers. In terms of immune cell infiltration, ZNRF3 was positively correlated to infiltration of cancer-associated fibroblasts in CESC, HNSC, OV, PAAD, PRAD, and THYM, but negatively associated with infiltration of CD8 T cells in HNSC, KIRC, KIRP and THYM. Moreover, ZNRF3 expression was correlated with most immune checkpoint genes in SARC, LUSC, LUAD, PRAD, THCA, UVM, TGCT, and OV, and associated with overwhelming majority of immunoregulatory genes in almost all cancers. Most RNA modification genes were also remarkably related to ZNRF3 level in KIRP, LUAD, LUSC, THYM, UVM, PRAD, and UCEC, indicating that ZNRF3 might have an important effect on cancer epigenetic regulation. Finally, we verified the expression and role of ZNRF3 in clinical specimens and cell lines of renal cancer and liver cancer. This study provides a comprehensive pan-cancer analysis of ZNRF3 and reveals the complexity of its carcinogenic effect.

Multiomics-based classifier to decipher immune landscape of uveal melanoma and predict patient outcomes

Uveal melanoma (UVM) prognosis and the possibilities for targeted therapy depend on a thorough understanding of immune infiltration features and the analysis of genomic and immune signatures. Leveraging multi-omics data from The Cancer Genome Atlas and GEO datasets, we employed an unsupervised clustering algorithm to categorize UVM into immune-related subgroups. Subsequent multi-omics analysis revealed two distinct UVM subtypes, each characterized by unique genomic mutations and immune microenvironment disparities. The aggressive UMCS2 subtype exhibited higher TNM stage and poorer survival, marked by elevated metabolism and increased immune infiltration. However, UMCS2 displayed heightened tumor mutational burden and immune dysfunction, leading to reduced responsiveness to immunotherapy. Importantly, these subtypes demonstrated differential sensitivity to targeted drugs due to significant variances in metabolic and immune environments, with UMCS2 displaying lower sensitivity. We developed a robust, subtype-specific marker-based risk scoring system. This system's diagnostic accuracy was validated through ROC curves, decision curve analysis, and calibration curves, all yielding satisfactory results. Additionally, cell experiments identified the pivotal function of HTR2B, the most crucial factor in this risk model. Knocking down HTR2B significantly reduced the activity, proliferation, and invasion ability of the UVM cell line. These findings underscored the impact of gene and immune microenvironment alterations in driving distinct molecular subtypes, emphasizing the need for precise treatment strategies. The molecular subtyping-based risk assessment system not only aids in predicting patient prognosis but also guides the identification of populations suitable for combined treatment. Molecules represented by HTR2B in the model may serve as effective therapeutic targets for UVM.Communicated by Ramaswamy H. Sarma.

Identification and Analysis of Sex-Biased Copy Number Alterations

Background: Sex difference has long been recognized at cancer incidence, outcomes, and responses to therapy. Analyzing the somatic mutation profiles of large-scale cancer samples between the sexes have revealed several potential drivers of cancer with sex difference. However, it is still a demand for in-depth scrutinizing the sex-biased characteristics of genome instability to link the clinical differences for individual cancer type. Methods: Here, we utilized a published framework devised to specifically compare the copy number profiles between 2 groups to identify the sex-biased copy number alterations (CNAs) across 16 cancer types from the The Cancer Genome Atlas Program database, and dissected the impact of those CNAs. Results: Totally, 81 male-biased CNA regions and 23 female-biased CNA regions in 16 cancer types were found. Functional annotation analysis showed that several critical biological functions associated with sex-biased CNAs are shared in multiple cancer types, including immune-related pathways and regulation of cellular signaling. Most sex-biased CNAs have a substantial effect on transcriptional consequence, where the average of over 68% of genes have a linear relationship with CNAs across cancer types, and 14% of those genes are affected by the combination of the sex and copy number. Furthermore, 29 sex-biased CNA regions show latent capacity to be sex-specific prognostic biomarker such as CNA on 11q13.4 for head and neck cancer and lung cancer. Conclusions: This analysis offers new insights into the role of sex in cancer etiology and prognosis through a detailed characterization of sex differences in genome instability of diverse cancers.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.