The pollution of indoor environments and the consequent health risks associated with thirdhand smoke (THS) are increasingly recognized in recent years. However, the carcinogenic potential of THS and its underlying mechanisms have yet to be thoroughly explored. In this study, we examined the effects of short-term THS exposure on the development of gastric cancer (GC) in vitro and in vivo. In a mouse model of spontaneous GC, CC036, we observed a significant increase in gastric tumor incidence and a decrease in tumor-free survival upon THS exposure as compared to control. RNA sequencing of primary gastric epithelial cells derived from CC036 mice showed that THS exposure increased expression of genes related to the extracellular matrix and cytoskeletal protein structure. We then identified a THS exposure-induced 91-gene expression signature in CC036 and a homologous 84-gene signature in human GC patients that predicted the prognosis, with secreted phosphoprotein 1 (SPP1) and tribbles pseudokinase 3 (TRIB3) emerging as potential targets through which THS may promote gastric carcinogenesis. We also treated human GC cell lines in vitro with media containing various concentrations of THS, which, in some exposure dose range, significantly increased their proliferation, invasion, and migration. We showed that THS exposure could activate the epithelial-mesenchymal transition (EMT) pathway at the transcript and protein level. We conclude that short-term exposure to THS is associated with an increased risk of GC and that activation of the EMT program could be one potential mechanism. Increased understanding of the cancer risk associated with THS exposure will help identify new preventive and therapeutic strategies for tobacco-related disease as well as provide scientific evidence and rationale for policy decisions related to THS pollution control to protect vulnerable subpopulations such as children.

BACKGROUND: Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development.
OBJECTIVE: To construct a robust prognostic signature, we used developed and validated across datasets.
METHODS: After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature score with immune cell infiltration and cell-cell interactions were analyzed. The correlations between the signature score and the sensitivity to different drugs were also predicted.
RESULTS: In the TCGA cohort, patients in the low-risk group according to the signature score had longer survival than those in the high-risk group, and this finding was validated in the validation datasets. The signature was a prognostic factor independent of age and sex and was correlated with stage and PD-1/PD-L1 expression. Area under the receiving operating characteristic curve was 0.72. Genomic association analyses revealed that samples from high-risk patients exhibited chromosomal instability. Transcriptomic analyses revealed that the signature score was significantly associated with multiple cellular pathways. Bulk RNA-seq and single-cell sequencing data revealed that the signature reflected differences in infiltrating immune cell-tumor cell interactions, especially for macrophages. The signature also predicted the putative drug sensitivity of CRC samples.
CONCLUSIONS: The signature is a valuable biomarker for predicting CRC prognosis and reflects multiple features of CRC, especially macrophage infiltration in the microenvironment.

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were \'radiosensitivity\' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 \'radiosensitivity\' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

UNASSIGNED: Breast cancer gene expression signatures are developing rapidly and are expected to better understand the intrinsic features of the tumor, and also to optimize the treatment strategy in clinical practice. This review is to summarize the controversy and consensus in clinical practice of gene expression signatures, and to provide our perspective on these issues as well as recommendation for future direction.
UNASSIGNED: We reviewed English publications in PubMed related to breast cancer gene expression signatures from 2002 to 2022.
UNASSIGNED: Five mature commercial gene expression signatures: Oncotype, MammaPrint, Prosigna/PAM50, EndoPredict and Breast Cancer Index (BCI) are available to provide the prognostic and predictive assessment. Although they could help to evaluate the risk of recurrence and to predict the benefits of certain treatments, their applications remain challenging. Treatment decisions should be determined by a combination of related clinical pathological factors in clinical practice.
UNASSIGNED: Gene expression signatures could assist in the determination of the adjuvant therapy of early-stage breast cancer. The prospective randomized clinical trials showed that chemotherapy may be exempted in low-risk patients. More sufficient data are expected for the application in radiotherapy, extended endocrine therapy, and neoadjuvant treatment. The treatment cannot be determined by a single factor but by comprehensive assessments of clinicopathological factors, test purpose, and cost-effectiveness. Patients will benefit from personalized treatments with the publication of further evidence.

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

OBJECTIVE: Patients with triple-negative breast cancer (TNBC) have a high rate of recurrence within 3 years of diagnosis and a high rate of death within 5 years compared to other subtypes. The number of clinical trials investigating various new agents and combination therapies has recently increased; however, current strategies benefit only a minority of patients. This study aimed to identify specific genes that predict patients at high risk of recurrence and the immune status of the tumor microenvironment at an early stage, thereby providing insight into potential therapeutic targets to improve clinical outcomes in TNBC patients.
METHODS: We evaluated the prognostic significance of microarray mRNA expression of 20,603 genes in 233 TNBC patients from the METABRIC dataset and further validated the results using RNA-seq mRNA expression data in 143 TNBC patients from the GSE96058 dataset.
RESULTS: Eighteen differentially expressed genes (AKNA, ARHGAP30, CA9, CD3D, CD3G, CD6, CXCR6, CYSLTR1, DOCK10, ENO1, FLT3LG, IFNG, IL2RB, LPXN, PRKCB, PVRIG, RASSF5, and STAT4) identified in both datasets were found to be reliable biomarkers for predicting TNBC recurrence and progression. Notably, the genes whose low expression was associated with increased risk of recurrence and death were immune-related genes, with significant differences in levels of immune cell infiltration in the tumor microenvironment between high- and low- expression groups.
CONCLUSIONS: Genes reported herein may be effective biomarkers to identify TNBC patients who will and will not benefit from immunotherapy and may be particularly important genes for developing future treatment strategies, including immunotherapy.

OBJECTIVE: Brain metastasis (BM) in colorectal cancer (CRC) is a rare event with poor prognosis. Apart from (K)RAS status and lung and bone metastasis no biomarkers exist to identify patients at risk. This study aimed to identify a gene expression signature associated with colorectal BM.
METHODS: Three patient groups were formed: 1. CRC with brain metastasis (BRA), 2. exclusive liver metastasis (HEP) and, 3. non-metastatic disease (M0). RNA was extracted from primary tumors and mRNA expression was measured using a NanoString Panel (770 genes). Expression was confirmed by qPCR in a validation cohort. Statistical analyses including multivariate logistic regression followed by receiver operating characteristic (ROC) analysis were performed.
RESULTS: EMILIN3, MTA1, SV2B, TMPRSS6, ACVR1C, NFAT5 and SMC3 were differentially expressed in BRA and HEP/M0 groups. In the validation cohort, differential NFAT5, ACVR1C and SMC3 expressions were confirmed. BRA patients showed highest NFAT5 levels compared to HEP/M0 groups (global p = 0.02). High ACVR1C expression was observed more frequently in the BRA group (42.9%) than in HEP (0%) and M0 (7.1%) groups (global p = 0.01). High SMC3 expressions were only detectable in the BRA group (global p = 0.003). Only patients with BM showed a combined high expression of NFAT5, ACVR1C or SMC3 as well as of all three genes. ROC analysis revealed a good prediction of brain metastasis by the three genes (area under the curve (AUC)  = 0.78).
CONCLUSIONS: The NFAT5, ACVR1C and SMC3 gene expression signature is associated with colorectal BM. Future studies should further investigate the importance of this biomarker signature.

OBJECTIVE: To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure.
METHODS: Multicentric, prospective study.
METHODS: Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory.
METHODS: Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis.
METHODS: Endometrial biopsies collected in the mid-secretory phase.
METHODS: Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure.
RESULTS: Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation.
CONCLUSIONS: The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

OBJECTIVE: Medulloblastoma is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer.
METHODS: In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%.
RESULTS: The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott\'s NanoString assay were highly comparable with a concordance rate of 96.4%.
CONCLUSIONS: In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical.

Isoniazid is a first-line drug in antitubercular therapy. Isoniazid is one of the most commonly used drugs that can cause liver injury or acute liver failure, leading to death or emergency liver transplantation. Therapeutic approaches for the prevention of isoniazid-induced liver injury are yet to be established. In this study, we identified the gene expression signature for isoniazid-induced liver injury using a public transcriptome dataset, focusing on the differences in susceptibility to isoniazid in various mouse strains. We predicted that lansoprazole is a potentially protective drug against isoniazid-induced liver injury using connectivity mapping and an adverse event reporting system. We confirmed the protective effects of lansoprazole against isoniazid-induced liver injury using zebrafish and patients\' electronic health records. These results suggest that lansoprazole can ameliorate isoniazid-induced liver injury. The integrative approach used in this study may be applied to identify novel functions of clinical drugs, leading to drug repositioning.