The discovery that mutations in the EGFR gene are detected in up to 50% of lung adenocarcinoma patients, along with the development of highly efficacious epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs), has revolutionized the treatment of this frequently occurring lung malignancy. Indeed, the clinical success of these TKIs constitutes a critical milestone in targeted cancer therapy. Three generations of EGFR-TKIs are currently approved for the treatment of EGFR mutation-positive non-small cell lung cancer (NSCLC). The first-generation TKIs include erlotinib, gefitinib, lapatinib, and icotinib; the second-generation ErbB family blockers include afatinib, neratinib, and dacomitinib; whereas osimertinib, approved by the FDA on 2015, is a third-generation TKI targeting EGFR harboring specific mutations. Compared with the first- and second-generation TKIs, third-generation EGFR inhibitors display a significant advantage in terms of patient survival. For example, the median overall survival in NSCLC patients receiving osimertinib reached 38.6 months. Unfortunately, however, like other targeted therapies, new EGFR mutations, as well as additional drug-resistance mechanisms emerge rapidly after treatment, posing formidable obstacles to cancer therapeutics aimed at surmounting this chemoresistance. In this review, we summarize the molecular mechanisms underlying resistance to third-generation EGFR inhibitors and the ongoing efforts to address and overcome this chemoresistance. We also discuss the current status of fourth-generation EGFR inhibitors, which are of great value in overcoming resistance to EGFR inhibitors that appear to have greater therapeutic benefits in the clinic.

Background: The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers. Methods: Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model. Results: Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log₂(MAT1A)-0.1526*log₂(MT1H) + 0.4645*log₂(FN1) -0.2244*log₂(FAP), log₂(gene count). Besides, a four-hub gene module was also identified as prognostic model = − 1.400*log₂(CEL) + 1.321*log₂(CPA1) + 0.454*log₂(POSTN) + 1.011*log₂(PM20D1), log₂(gene count). Conclusion: Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Cancer stem‐like cells (CSLC) are considered a major contributor to the development and progression of hepatocellular carcinoma (HCC). Previous studies indicated that CSLC are characterized by resistance to ferroptosis, a type of lipid peroxidation‐dependent cell death. Here, we identified a set of ferroptosis‐related stemness genes (FRSG) and found that these genes may be involved in immune infiltration in HCC. A four‐FRSG (CDKN2A, GABARAPL1, HRAS, RPL8) risk model with prognostic prediction was constructed by a Cox analysis in HCC. Among these four genes, GABARAPL1 was downregulated in HCC tumor‐repopulating cells (TRC; a type of CSLC). Its downregulation decreased the sensitivity of HCC TRC to erastin‐ or sorafenib‐triggered ferroptosis. Together, we uncovered a molecular mechanism via which CSLC could achieve tolerance to ferroptosis. Further studies may provide potential therapeutic strategies targeting CSLC in HCC.

Background. Sufficient evidence indicated the crucial role of NF‐κB family played in gastric cancer (GC). The novel discovery that NF‐κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF‐κB, metabolism, and cancer immunity in GC still requires further improvement. Methods. TCGA, hTFtarget, and MSigDB databases were employed to identify NF‐κB‐related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients’ response to immunotherapy. We also established a NFMG‐related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets. Results. We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12‐NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC. Conclusion. Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF‐κB‐mediated cancer metabolism and immunity.

Previous studies showed that fibroblast growth factor receptor 1 (FGFR1) is an attractive target in gastric cancer therapy. In the current study, we aimed to investigate whether the compound L6123, a novel non-ATP-competitive FGFR1 inhibitor, could show better antitumor activity than the leading compound, nordihydroguaiaretic acid (NDGA), in FGFR1-overexpressing gastric cancer cells. Using an MTT assay, we investigated the inhibitory effect of L6123 on the viability of three gastric cancer cells (MGC-803, SGC-7901, and BGC-823) overexpressing FGFR1, wild-type mouse embryonic fibroblast (MEF), and MEF expressing FGFR1, FGFR2, and FRS2α gene knockout (MEF). We studied the antitumor mechanism of L6123 against the gastric cancer cell line SGC-7901 by western blot analysis. The antitumor effects of L6123 on the gastric cancer cell line SGC-7901 were detected by flow cytometry, Hoechst staining, western blot analysis, and Transwell invasion assay. L6123 had lower IC50 in all three gastric cancer cells than NDGA and showed better inhibitory activity against MEF cells than against MEF cells. In the SGC-7901 gastric cell, L6123 inhibited the FGF2-induced phosphorylation of FGFR1/FRS2α/ERK1/2 in a dose-dependent manner, induced the activation of the apoptosis-related proteins, cleaved-PARP and cleaved-caspase-3, decreased the expression of pro-caspase-3 and Bcl-2, and induced tumor cell apoptosis. L6123 also dose-dependently reduced cell invasion ability, and showed better activity than NDGA at the same concentration. A novel non-ATP-competitive inhibitor L6123 showed excellent antigastric cancer activity by inhibiting the FGFR1 signaling pathway. Thus, we discovered a potential agent for the treatment of FGFR1-overexpressing gastric cancer.