Abstract
Cancer is a deadly disease that leads to many therapeutic failures. Drug treatment needs diagnosis, prognosis and therapeutic information creations and promotion. To promote therapeutics, clinical diagnosis should be expanded in dimensionality and therapeutic guidance. This editorial discusses this issue of diagnosis improvements and normality (digital profiling creations).
Keywords:Cancer Diagnosis; Neoplasm Metastasis; Drug Treatment; Digital Profiling; Clinical Diagnosis
Introduction
Cancer is the secondary leading mortality disease worldwide (12% of all human deaths) [1-2]. 70-90% of all cancer death is caused by neoplasm metastasis [3-6]. Cancer diagnosis needs to improve information for both growth inhibition and therapeutic guidance against cancer. To avoid further devastating incidence and human mortality of late-stage cancer, cancer diagnosis should be more informative and usefulness. This editorial discusses the landscape of cancer diagnosis in the clinic.
Clinical Limitation
Cancer metastasis is a common feature of leading to therapeutic failure. Drug treatment needs diagnosis, prognosis and therapeutic information creations and promotion. To promote therapeutics against neoplasm metastases, clinical diagnosis should be expanded in dimensionality and therapeutic guidance. This editorial discusses the issue of digital profiling creations and basic pathogenesis revealing.
Clinical Diagnosis
Diagnosis for cancer utilizes different platforms and systems. Treatment progresses are based on diagnosis updating and new classifications. Yet, there is little progress in clinical trials. Several common diagnostic systems are currently utilized;
• Morphological data (computerized topography or
ultrasonic)
• Historic review of tumor tissues (subtypes)
• Multi-omics data for cancer
• DNA or RNA sequencing, or copy number or others
• Cancer biomarkers in plasma or elsewhere
System Innovations
Clinical treatment evaluation and responses are based on diagnostic systems. However, current cancer diagnostic systems are not well categorized for the purpose of prognostic or therapeutic predictions. Biopsy was the common procedure for pathological and diagnostic evaluation in the past. According to this stage of techniques, more informative data of diagnostic platforms and systems should be created and optioned.
Dimension Expansion
Today, human cancer in the clinic is termed as TNM. It only contains information about morphology, especially the status of tumor metastasis. With the rapid expansion and updating of molecular biology technology [7-14], it should be categorized as TNMH (hallmark)G(genomic)M(molecular)T(target) or other systems to embrace new information categories and dimensions for prognostic or therapeutic applications. This should be a new trend for clinical cancer trials.
Discussion
Tumors are subtyped to more than two hundred. Proper division or integration of clinical diagnostic data or systems has great benefits for different treatments (surgery, radio, thermos, or pharmaceutical). Due to these new integrations, further treatments will be more targeted or comprehensive, like personalized oncology [15-18], drug combination from highest number (one million) to several thousands in real clinical settings [19-22]. Drug combination commonly promotes clinical outcomes, yet mechanisms are obscure. In the past decade, several pathways and mechanisms have been proposed. Large volume of such research may be followed in upcoming decades. Including many pharmaceutical progresses, clinical cancer treatment will be disciplinary changes.
Digitalized Pathogenesis in Future
To fulfill tumor pathological profiling, digitalized cancer
information should be undergone several pathways;
• Study the possibility and sustainability of digital
pathogenesis of cancer by clinical data accumulation, analysis,
and innovations
• Establish potential systems of TNMHGMT or other forms
alike by automatic production or formation
• Cancer pathogenesis investigations and highlights
• Establish better personalized oncology or medicine for
promoting clinical cancer trials
Conclusion
Cancer metastasis should be emphasized for patient’s survival benefiting. Different profiles in genomic or molecular, like circulatory tumor cells or biomarker diagnostics and drug sensitivity testing are helpful for clinical cancer trials. Many new discoveries, methodology, and classifications could be obtained by these experimental and clinical investigations. More efficacy in cancer treatments can be achieved.
References
- Lu DY (2025) Advances in cancer therapeutics and genomics for improved patient outcomes. Current Cell Science 1: 43-46.
- Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A (2025) Cancer stastistics 2025. CA-Cancer J Clin 75(1): 10-45.
- Lu DY, Lu TR, Wu HY, Cao S (2013) Cancer metastasis treatments. Current Drug Therapy 8(1): 24-29.
- Lambert, AW, Pattabiraman DR, Weinberg RA (2017) Emerging biological principles of metastasis. Cell 168(4): 670-691.
- Lu DY, Lu TR (2025) Antimetastatic drugs, pharmacologic challenge and opportunity. Current Drug Therapy 20(2): 169-179.
- Gerstberger S, Jiang Q, Ganesh K (2023) Metastasis. Cell 186(8): 1564-1579.
- Lu DY, Qi RX, Lu TR, Wu HY (2017) Cancer bioinformatics for update anticancer drug developments and personalized therapeutics. Reviews on Recent Clinical Trials 12(2): 101-110.
- Birkbak NJ, McGranahan N (2020) Cancer genome evolutionary trajectories in metastasis. Cancer Cell 37(1): 8-19.
- Wang DF, Liu BL, Zhang ZM (2023) Accelerating the understanding of cancer biology through the lens of genomics. Cell 186: 1755-1771.
- Passaro A, Bakir MA, Hamilton EG, Diehn M, Andre F, et al. (2024) Cancer biomarkers: emerging trends and clinical implications for personalized treatment. Cell 187: 1617-1635.
- Gadade DD, Jha H, Kumar C, Khan F (2024) Unlocking the power of precision medicine: exploring the role of biomarkers in cancer management. Future J Pharmaceutical Science 10: 5.
- Lu DY, Lu TR, Xu B, Ding J (2015) Pharmacogenetics of cancer therapy: breakthroughs from beyond? Future Science OA 1 (4): FSO80.
- Hyman DH, Tayloe BS, Baselga J (2017) Implementing genome-driven oncology. Cell 168: 584-599.
- Pantel K, Alix-Panabieres C (2022) Crucial roles of circulating tumor cells in the metastatic cascade and tumor immune escape: biology and clinical translation. J Immuno Therapy of Cancer 10(12): e005615.
- Lu DY, Lu TR (2025) Personalized oncology: scientific and technical approaches. Current Cell Science 1(1): 57-66.
- Lu DY (2014) Personalized cancer chemotherapy, an effective way for enhancing outcomes in clinics. 2014, Woodhead Publishing, Elsevier, UK (ISBN. 978-0-08-100346-6).
- Lu DY, Lu TR, Che JY, Shen Y, Yarla NS (2018) Individualized cancer therapy, future approaches. Current Pharmacogenomics & Personalized Medicine 16(2): 156-163.
- Khalili M, Azizi M, Sadeghzadeh H (2022) Immune evader cancer stem cells direct the perspective approaches to cancer immunotherapy. Stem Cell Res Therapy 13(1): 150.
- Lu DY, Lu TR, Yarla NS, Wu HY, Xu B, et al. (2017) Drug combination in clinical cancer treatment. Reviews on Recent Clinical Trials 12(3): 202-211.
- Lu DY, Lu TR (2026) Anticancer drug combinations: Different strategies used in the clinic. Current Advances in Medicine 1: e29496632394562.
- Aarwai U, Paliwal S, Tonk RK, Kumar N, Gupta SK (2025) Combinational drug therapy; its adverse effects and mitigation strategies. Current Advance Medicine 1: e29496632404793.
- Lu DY, Lu TR (2024) Anticancer drug combination, from possibility to principles. Clinics in Oncology 9: 2077.

















