Synthetic lethality through combinatorial targeting DNA damage response (DDR) pathways provides exciting anticancer therapeutic benefit. Currently, the long noncoding RNAs (lncRNAs) have been implicated in tumor drug resistance; however, their potential significance in DDR is still largely unknown. Here, we report that a human lncRNA, CTD-2256P15.2, encodes a micropeptide, named PAR-amplifying and CtIP-maintaining micropeptide (PACMP), with a dual function to maintain CtIP abundance and promote poly(ADP-ribosyl)ation. PACMP not only prevents CtIP from ubiquitination through inhibiting the CtIP-KLHL15 association but also directly binds DNA damage-induced poly(ADP-ribose) chains to enhance PARP1-dependent poly(ADP-ribosyl)ation. Targeting PACMP alone inhibits tumor growth by causing a synthetic lethal interaction between CtIP and PARP inhibitions and confers sensitivity to PARP/ATR/CDK4/6 inhibitors, ionizing radiation, epirubicin, and camptothecin. Our findings reveal that a lncRNA-derived micropeptide regulates cancer progression and drug resistance by modulating DDR, whose inhibition could be employed to augment the existing anticancer therapeutic strategies.

Increasing studies have highlighted the effects of the tumor immune micro-environment (TIM) on colon cancer (CC) tumorigenesis, prognosis, and metastasis. However, there is no reliable molecular marker that can effectively estimate the immune infiltration and predict the CC relapse risk. Here, we leveraged the gene expression profile and clinical characteristics from 1430 samples, including four gene expression omnibus database (GEO) databases and the cancer genome atlas (TCGA) database, to construct an immune risk signature that could be used as a predictor of survival outcome and immune activity. A risk model consisting of 10 immune-related genes were screened out in the Lasso-Cox model and were then aggregated to generate the immune risk signature based on the regression coefficients. The signature demonstrated robust prognostic ability in discovery and validation datasets, and this association remained significant in the multivariate analysis after controlling for age, gender, clinical stage, or microsatellite instability status. Leukocyte subpopulation analysis indicated that the low-risk signature was enriched with cytotoxic cells (activated CD4/CD8⁺ T cell and NK cell) and depleted of myeloid-derived suppressor cells (MDSC) and regulatory T cells. Further analysis indicated patients with a low-risk signature harbored higher tumor mutation loads and lower mutational frequencies in significantly mutated genes of APC and FBXW7. Together, our constructed signature could predict prognosis and represent the TIM of CC, which promotes individualized treatment and provides a promising novel molecular marker for immunotherapy.

A sort of major malignant disease, cancer can compromise human health wherever. Some mechanisms of the occurrence and evolution of cancer still seem elusive even now. Consequently, the therapeutic strategies for cancer must continually evolve. The hedgehog signaling pathway, a critical mediator in the normal development of numerous organs and the pathogenesis of cancer, is typically quiescent but is aberrantly activated in several malignancies. Extensive research has delineated that the aberrant activity of the hedgehog signaling pathway, whether autocrine or paracrine, is implicated in the initiation and progression of various neoplasms, including medulloblastoma (MB), basal cell carcinoma (BCC) and so on. Thus, notably Smo inhibitors, the opening of inhibitors of the hedgehog signaling pathway has become a topic of research attention. This review aims to summarize four aberrant activation pathways and the influence of hedgehog signaling pathway associated chemicals on tumor formation and development. Additionally, it will explore the therapeutic potential of targeted interventions in the hedgehog signaling pathway for cancer treatment.

Recent studies have highlighted the biological significance of cuproptosis in disease occurrence and development. However, it remains unclear whether cuproptosis signaling also has potential impacts on tumor initiation and prognosis of gastric cancer (GC). In this study, 16 cuproptosis‐related genes (CRGs) transcriptional profiles were harnessed to perform the regularized latent variable model‐based clustering in GC. A cuproptosis signature risk scoring (CSRS) scheme, based on a weighted sum of principle components of the CRGs, was used to evaluate the prognosis and risk of individual tumors of GC. Four distinct cuproptosis signature‐based clusters, characterized by differential expression patterns of CRGs, were identified among 1136 GC samples across three independent databases. The four clusters were also associated with different clinical outcomes and tumor immune contexture. Based on the CSRS, GC patients can be divided into CSRS‐High and CSRS‐Low subtypes. We found that DBT, MTF1, and ATP7A were significantly elevated in the CSRS‐High subtype, while SLC31A1, GCSH, LIAS, DLAT, FDX1, DLD, and PDHA1 were increased in the CSRS‐Low subtype. Patients with CSRS‐Low score were characterized by prolonged survival time. Further analysis indicated that CSRS‐Low score also correlated with greater tumor mutation burden (TMB) and higher mutation rates of significantly mutated genes (SMG) in GC. In addition, the CSRS‐High subtype harbored more significantly amplified focal regions related to tumorigenesis (3q27.1, 12p12.1, 11q13.3, etc.) than the CSRS‐Low tumors. Drug sensitivity analyses revealed the potential compounds for the treatment of gastric cancer with CSRS‐High score, which were experimentally validated using GC cells. This study highlights that cuproptosis signature‐based subtyping is significantly associated with different clinical features and molecular landscape of GC. Quantitative evaluation of the CSRS of individual tumors will strengthen our understanding of the occurrence and development of cuproptosis and the treatment progress of GC.

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Recent studies highlighted the clinicopathologic importance of the tumor microenvironment (TME) in delineating molecular attributes and therapeutic potentials. However, the overall TME cell infiltration landscape in nonsquamous non–small cell lung cancer (NSCLC) has not been comprehensively characterized. In this study, we used consensus non-negative matrix factorization molecular subtyping to determine TME cell infiltration patterns and identified 3 TME clusters (TME-C1, -C2, -C3) characterized by distinct clinicopathologic features, infiltrating cells, and biological processes. Proteomics analyses revealed that cyclic GMP-AMP–stimulator of interferon genes immune signaling–mediated protein and phosphorylation levels were significantly upregulated in inflammation-related TME-C2 clusters. The score extracted from the TME-related signature (TMEsig-score) divided patients with NSCLC into high- and low-score subgroups, where a high score was associated with favorable prognosis and immune infiltration. The genomic landscape revealed that patients with low TMEsig-score harbored more somatic copy number alterations and higher mutation frequency of driver genes involving STK11 , KEAP1 , SMARCA4 , and others. Drug sensitivity analyses suggested that tumors with high TMEsig-score were responsible for favorable clinical response to immune checkpoint inhibitor treatment. In summary, this study highlights that comprehensive recognizing of the TME cell infiltration landscape will contribute to enhancing our understanding of TME immune regulation and promote effectiveness of precision biotherapy strategies.