BACKGROUND: Patients with non-small cell lung cancer (NSCLC) with liver metastasis have a poor prognosis, and there are no reliable biomarkers for predicting disease progression. Currently, no recognized and reliable prediction model exists to anticipate liver metastasis in NSCLC, nor have the risk factors influencing its onset time been thoroughly explored.
METHODS: This study conducted a retrospective analysis of 434 NSCLC patients from two hospitals to assess the association between the risk and timing of liver metastasis, as well as several variables.
RESULTS: The patients were divided into two groups: those without liver metastasis and those with liver metastasis. We constructed a nomogram model for predicting liver metastasis in NSCLC, incorporating elements such as T stage, N stage, M stage, lack of past radical lung cancer surgery, and programmed death ligand 1 (PD-L1) levels. Furthermore, NSCLC patients with wild-type EGFR, no prior therapy with tyrosine kinase inhibitors (TKIs), and no prior radical lung cancer surgery showed an elevated risk of early liver metastasis.
CONCLUSIONS: In conclusion, the nomogram model developed in this study has the potential to become a simple, intuitive, and customizable clinical tool for assessing the risk of liver metastasis in NSCLC patients following validation. Furthermore, it provides a framework for investigating the timing of metachronous liver metastasis.

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

Aneuploidy, the presence of an aberrant number of chromosomes, has been associated with tumorigenesis for over a century. More recently, advances in karyotyping techniques have revealed its high prevalence in cancer: About 90% of solid tumors and 50-70% of hematopoietic cancers exhibit chromosome gains or losses. When analyzed at the level of specific chromosomes, there are strong patterns that are observed in cancer karyotypes both pan-cancer and for specific cancer types. These specific aneuploidy patterns correlate strongly with outcomes for tumor initiation, progression, metastasis formation, immune evasion and resistance to therapeutic treatment. Despite their prominence, understanding the basis underlying aneuploidy patterns in cancer has been challenging. Advances in genetic engineering and bioinformatic analyses now offer insights into the genetic determinants of aneuploidy pattern selection. Overall, there is substantial evidence that expression changes of particular genes can act as the positive selective forces for adaptation through aneuploidy. Recent findings suggest that multiple genes contribute to the selection of specific aneuploid chromosomes in cancer; however, further research is necessary to identify the most impactful driver genes. Determining the genetic basis and accompanying vulnerabilities of specific aneuploidy patterns is an essential step in selectively targeting these hallmarks of tumors.

Hepatocellular carcinoma (HCC) is a widespread primary liver cancer with a high fatality rate. Despite several genes with oncogenic effects in HCC have been identified, many remain undiscovered. In this study, we conducted a comprehensive computational analysis to explore the involvement of genes within the same families as known driver genes in HCC. Specifically, we expanded the concept beyond single-gene mutations to encompass gene families sharing homologous structures, integrating various omics data to comprehensively understand gene abnormalities in cancer. Our analysis identified 74 domains with an enriched mutation burden, 404 domain mutation hotspots, and 233 dysregulated driver genes. We observed that specific low-frequency somatic mutations may contribute to HCC occurrence, potentially overlooked by single-gene algorithms. Furthermore, we systematically analyzed how abnormalities in the ubiquitinated proteasome system (UPS) impact HCC, finding that abnormal genes in E3, E2, DUB families, and Degron genes often result in HCC by affecting the stability of oncogenic or tumor suppressor proteins. In conclusion, expanding the exploration of driver genes to include gene families with homologous structures emerges as a promising strategy for uncovering additional oncogenic alterations in HCC.

The hyperplasia-carcinoma sequence is a stepwise tumourigenic programme towards endometrial cancer in which normal endometrial epithelium becomes neoplastic through non-atypical endometrial hyperplasia (NAEH) and atypical endometrial hyperplasia (AEH), under the influence of unopposed oestrogen. NAEH and AEH are known to exhibit polyclonal and monoclonal cell growth, respectively; yet, aside from focal PTEN protein loss, the genetic and epigenetic alterations that occur during the cellular transition remain largely unknown. We sought to explore the potential molecular mechanisms that promote the NAEH-AEH transition and identify molecular markers that could help to differentiate between these two states. We conducted target-panel sequencing on the coding exons of 596 genes, including 96 endometrial cancer driver genes, and DNA methylome microarrays for 48 NAEH and 44 AEH lesions that were separately collected via macro- or micro-dissection from the endometrial tissues of 30 cases. Sequencing analyses revealed acquisition of the PTEN mutation and the clonal expansion of tumour cells in AEH samples. Further, across the transition, alterations to the DNA methylome were characterised by hypermethylation of promoter/enhancer regions and CpG islands, as well as hypo- and hyper-methylation of DNA-binding regions for transcription factors relevant to endometrial cell differentiation and/or tumourigenesis, including FOXA2, SOX17, and HAND2. The identified DNA methylation signature distinguishing NAEH and AEH lesions was reproducible in a validation cohort with modest discriminative capability. These findings not only support the concept that the transition from NAEH to AEH is an essential step within neoplastic cell transformation of endometrial epithelium but also provide deep insight into the molecular mechanism of the tumourigenic programme. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

OBJECTIVE: To evaluate the incidence, clinical laboratory characteristics, and gene mutation spectrum of Ph-negative MPN patients with atypical variants of JAK2, MPL, or CALR.
METHODS: We collected a total of 359 Ph-negative MPN patients with classical mutations in driver genes JAK2, MPL, or CALR, and divided them into two groups based on whether they had additional atypical variants of driver genes JAK2, MPL, or CALR: 304 patients without atypical variants of driver genes and 55 patients with atypical variants of driver genes. We analyzed the relevant characteristics of these patients.
RESULTS: This study included 359 patients with Ph-negative MPNs with JAK2, MPL, or CALR classical mutations and found that 55 (15%) patients had atypical variants of JAK2, MPL, or CALR. Among them, 28 cases (51%) were male, and 27 (49%) were female, with a median age of 64 years (range, 21-83). The age of ET patients with atypical variants was higher than that of ET patients without atypical variants [70 (28-80) vs. 61 (19-82), p = 0.03]. The incidence of classical MPL mutations in ET patients with atypical variants was higher than in ET patients without atypical variants [13.3% (2/15) vs. 0% (0/95), p = 0.02]. The number of gene mutations in patients with atypical variants of driver genes PV, ET, and Overt-PMF is more than in patients without atypical variants of PV, ET, and Overt-PMF [PV: 3 (2-6) vs. 2 (1-7), p < 0.001; ET: 4 (2-8) vs. 2 (1-7), p < 0.05; Overt-PMF: 5 (2-9) vs. 3 (1-8), p < 0.001]. The incidence of SH2B3 and ASXL1 mutations were higher in MPN patients with atypical variants than in those without atypical variants (SH2B3: 16% vs. 6%, p < 0.01; ASXL1: 24% vs. 13%, p < 0.05).
CONCLUSIONS: These data indicate that classical mutations of JAK2, MPL, and CALR may not be completely mutually exclusive with atypical variants of JAK2, MPL, and CALR. In this study, 30 different atypical variants of JAK2, MPL, and CALR were identified, JAK2 G127D being the most common (42%, 23/55). Interestingly, JAK2 G127D only co-occurred with JAK2V617F mutation. The incidence of atypical variants of JAK2 in Ph-negative MPNs was much higher than that of the atypical variants of MPL and CALR. The significance of these atypical variants will be further studied in the future.

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.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

In recent years, rapid advancement in gene/protein analysis technology has resulted in target molecule identification that may be useful in cancer treatment. Therefore, \"Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition\" was published in Japan in September 2021. These guidelines were established to align the clinical usefulness of external diagnostic products with the evaluation criteria of the Pharmaceuticals and Medical Devices Agency. The guidelines were scoped for each tumor, and a clinical questionnaire was developed based on a serious clinical problem. This guideline was based on a careful review of the evidence obtained through a literature search, and recommendations were identified following the recommended grades of the Medical Information Network Distribution Services (Minds). Therefore, this guideline can be a tool for cancer treatment in clinical practice. We have already reported the review portion of \"Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition\" as Part 1. Here, we present the English version of each part of the Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition.

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.