INTRODUCTION
Plants have played a significant role in human life, and their utilization for treating various diseases has a long history [1]. The earliest documented records of using medicinal plants date back to 2,600 BC, authored by the Sumerians and Akkadians. In ancient India, the knowledge of medicinal herbs and their application was described in the Rigveda and Atharvaveda (3,500–1,500 B.C.), paving the way for the alternative medical system of Ayurveda [2]. In the present era, over 70% of people worldwide rely on plant-derived medicines to treat various illnesses and health conditions [3]. India stands at the forefront of medicinal plant cultivation and is renowned as the “Botanical Garden of the World.” The medicinal plant market in India is currently valued at 4.2 billion (56.6 million USD) and is projected to reach 14 billion (188.6 million USD) by 2026 [4]. According to a World Health Organization (WHO) estimate, approximately 75% of people all over the world incorporate herbal remedies as alternative or complementary medicine. Plant-derived drugs are enormously consumed, particularly in combating various types of malignancies, due to their antioxidant, immune-modulatory, and cancer-healing properties, which offer minimal side effects.
Each year, more people throughout the world succumb to cancer. According to the “Globocon 2022” survey conducted by the WHO, nearly 19,292,789 new cancer cases were reported worldwide in 2020, resulting in 9,958,133 deaths across genders [5,6]. Lung cancer remains the leading cause of death with 18.4% of cancer cases, followed by breast cancer in women at 11.6%, and stomach and liver cancer at 8.2% each. Nearly 7% of cancer-related deaths are attributable to prostate and colorectal cancers, respectively [7,8].
Three basic categories of etiological factors, namely chemical, physical, and environmental, are responsible for turning normal cells into cancerous cells. Chemical carcinogens such as benzpyrene, asbestos, and over 800 other chemical moieties; physical carcinogens, such as various forms of ionizing radiation; and biological carcinogens, including microorganisms, are to name a few [9,10]. In addition, environmental factors like pollution and lifestyle choices such as smoking, alcohol consumption, sedentary living, obesity, and the adoption of high-fat, high-calorie diets significantly contribute to the incidence of various malignancies [11]. In practical terms, identifying the precise etiology and genetic factors responsible for a specific form of cancer is a complex process with limited treatment options. Modern cancer treatment methods primarily involve chemotherapy, radiation therapy, and immunotherapy, accompanied by surgical tumor removal through different procedures. However, chemotherapeutic agents often produce multiple side effects and adverse drug events, affecting normal cells such as the bone marrow, gastrointestinal tract, and hair follicles. In addition, synthetic drugs face significant challenges, including drug resistance [4].
The utilization of plants by ancient individuals, driven by astute observation and inherited sagacity, holds historical significance. Phyto-origin drugs offer substantial advantages over synthetic medications, displaying minimal side effects, adverse events, cost-effectiveness, efficacy, and low toxicity. The anti-cancer mechanisms of phytoconstituents encompass modulation of cell growth, differentiation, induction of cell death, hindrance of angiogenesis, and obstruction of metastasis. Recently, research on phytodrugs has been escalating exponentially [12]. In addition, more than 50% of the approved new chemical entities registered with the USFDA in the last 15 years originate from phytoconstituents and their derivatives [13]. Phytopharmaceuticals and their derivatives, with their diversified structures and distinctive pharmacological and molecular characteristics, harness potential as chemotherapeutic agents. Alkaloids isolated from Vinca rosea, such as Vincristine, Vinblastine, Taxane diterpenoids (Paclitaxel and Docetaxel), Epipodophyllotoxin (Etoposide and Teniposide), and Camptothecin, have significantly contributed to cancer chemotherapy [14,15]. Drugs such as Paclitaxel and Camptothecin command over 30% of the world’s anti-cancer drug market [16], accounting for nearly $9 billion, underscoring the significance of plant-derived drugs [17,18]. Numerous plant-origin drugs, such as Betulinic acid, Combretstatin, Curcumin, Homoharringtonine, Indirubin, Flavipiridol, Roscovitine, Brucantine, Lycophene, Resveratrol, and Silvestrol, are currently undergoing clinical or preclinical trials, showcasing their potential for future utilization [14,19]. Therefore, access to ethnobotanical information on medicinal plants from both traditional and folklore sources is indispensable for the scientific community to develop innovative drugs and drug delivery strategies. Kerala, renowned as “God’s own country,” is celebrated for its biodiversity and traditional Ayurvedic wisdom. The tribes of Kerala, comprising Malayans, Kurumbas, Karimpalans, Kattunaikans, Mullukkurumans, Malapanickars, Kadars, Koragas, and Cholanaikkans, possess extensive knowledge of medicinal plant usage for diverse ailments, spanning millennia [19]. The Wayanad and Kozhikode districts were chosen and surveyed for this review. This study approach encompassed 50% survey data, 30% meta-analysis reports, 11% observational studies, and 9% interventional studies. The PRISMA web tool (https://prisma-statement.org) was used for the systematic review guidelines [20]. Subsequently, a flow chart was plotted for the review screening process using the Prisma flow chart 2020 protocol. Furthermore, the screening of 552 articles obtained from Medline, Web of Science, and PubMed was done using the Ovid web tool (https://ovidsp.ovid.com/) [21] for duplication removal. The filtered articles underwent manual screening with two reviewers, and the final check was enabled by the third reviewer. The shortlisted articles underwent meta-analysis using the Meta-Mar web tool (https://meta-mar.shinyapps.io/) [22], covering subgroup analysis, effect size analysis, publication bias, and publication correlation.
The Wayanad and Kozhikode regions of Kerala
Kerala is situated on the south-western Malabar coast, with latitudes ranging from 8°18′ N to 12° 48′ N. The longitudes cover the span of 74° 52′ E–77°24′ E. The state is divided into three main regions, namely the eastern highlands, the central midlands, and the western lowlands. The selected territory falls between the eastern highlands and the central midlands, with latitudes ranging from 11°5′ N to 11° N and longitudes spanning from 75°2′ E to 76°5′ E. This region is situated between the Sayadri mountains and the coastal lowlands [23]. The climate in this area experiences a significant variation in temperatures throughout the year, ranging from 17°C to 48°C. During the summer months, temperatures can soar up to 50°C. In contrast, during the winter, the average temperature ranges from 17°C to 21°C in the Wayanad region and slightly higher (around ±5°C) in Kozhikode. The ethnobotanical information for this region was meticulously collected during the period from February 2021 to August 2022. Ethnobotany involves the study of the intricate relationship between plants and people, including the traditional knowledge and uses of plants in local communities. The data gathered during this time frame is expected to encompass the plant usage and traditional knowledge of the local people during that specific period [24].
The selected districts of “Wayanad and Kozhikode” in Kerala are known as the “Malabar Hill regions.” These areas are inhabited by various tribes, each with its own unique culture and practices. In the “Wayanad region,” the tribes include Adiyans, Paniyars, Mullukkurumans, Kattunaikans, Kurichyars of Wayanad, Kanaladi, and Wayanad Kadars. On the other hand, the tribes found in the “Kozhikode district” are Malapanickars, Malayans, Paniyans, Kattunaikans, Karimpalans, and Kurichyars [25,26]. The “Kadars” are inhabitants of various districts, including Malappuram, Wayanad, Thrissur, Kannur, and Kozhikode. The selected Malabar regions are represented in Figure 1.
The “Kattunaikans” tribes reside in the Palakkad, Kannur, and Kozhikode districts of the Malabar hills, where they are primarily involved in the cultivation of food grains and fruits. The “Mullukkurumans” community in Wayanad district is known for their traditional hunting and food gathering practices. However, in modern times, they have also extensively engaged in agriculture, particularly in growing various spices, herbs, medicinal plants, and millets. The “Ayurveda” system of medicine is widely practiced and utilized by both experts and common people in the selected study regions. Moreover, the region is renowned for its enormous spice collection, trading, and exports, making it a significant hub for the spice industry [20,27].
Figure 1. The selected Malabar regions (Wayanad and Kozhikode) of Kerala. [Click here to view] |
MATERIALS AND METHODS
Data collection methods for the survey
The data collection of this survey enabled the “participatory rural appraisal method,” which proved to be a cost-effective and time-efficient approach. All the data for this study were gathered through structured interviews, semi-structured questionnaires, field surveys, sample collection, and note procurement. The study took place in the selected area of the Malabar Hills, which is inhabited by various tribal communities, including Mullukurumban, Kattunaikans, Karimpalans, Malapanickars, Malayans, Paniyans, Kurichyars, Kadars, and Koragas. Within these two districts, a total of 38 villages were included in the study, each hosting more than five types of indigenous tribes unique to that particular territory. Extensive interviews were conducted with the tribal people using different methods, predominantly semi-structured questionnaires and field surveys. In addition, traditional healers among the tribes were identified, and their knowledge of folklore-related ailments was documented. To ensure the reliability of the data, two separate visits were made to the tribal settlements, and the medicinal plant samples were locally identified. The samples were labeled with ethnobotanical information, and all the acquired information was diligently recorded in field notebooks.
Meta analysis
Literature search methods
The PRISMA systematic review guidelines were followed for this study, and the flow chart was plotted using the PRISMA web tool 2020 [20]. The article search was achieved using PubMed, Ovid/MEDLINE, Scopus, and Web of Science databases. The option for English abstracts from database inception to April 3, 2023, was enabled. The search strategy consisted of keywords related to phytochemicals, the English language, and cancer. Furthermore, screening of the 552 selected articles was carried out using the Ovid web tool (https://ovidsp.ovid.com) [21]. The Prisma flow chart for the screening process is illustrated in Figure 2.
Inclusion and exclusion criteria
The inclusion criteria encompassed full-text articles with phytoconstituents, anticancer activity, and pharmacological uses. The search was enabled using Boolean search terms such as “Phytoconstituents AND anticancer activity OR pharmacological uses” and “Phytochemicals OR plant constituents AND (Antitumor activity OR cytotoxicity) OR medicinal properties.” The articles that are phytoconstituent derivatives, incomplete data, duplicate articles, retracted articles, non-English articles, and articles not of interest were excluded. The inclusion and exclusion criteria were achieved with a manual review with two reviewers, followed by a final check by a third reviewer. The 347 shortlisted articles were screened for inclusion and exclusion criteria. Around 15 articles were excluded. The 331 original articles were subjected to the final screening process. Fifteen articles that were not the outcome of interest and five articles with phytochemical derivatives were excluded after screening, and the 311 articles were included for further analysis using the Meta-Mar web tool [22].
Figure 2. Prisma flow chart for the systematic review screening process. [Click here to view] |
Meta analysis
The meta-analysis of the included articles covering risk ratio (RR), publication bias, publication correlation, subgroup analysis, effect size prediction, sensitivity, and heterogeneity was performed using the Meta-Mar web tool. The 311 articles were thoroughly analyzed statistically using the Meta-Mar web tool (https://meta-mar.com) [22]. Effect sizes were determined after the articles were chosen based on their quality and relevancy. To evaluate the diversity of the study outcomes and pinpoint heterogeneity, statistical techniques like Cochran’s Q and I² were utilized. To investigate the probable cause of the discrepancy in results among selected articles, subgroup analyses were performed. To examine the potential impact of study-specific factors on the noted effects, a meta-regression analysis was conducted. Analyses using funnel plots and statistical tests were used to determine the likelihood of publication bias and publication correlation. A sensitivity analysis was carried out to guarantee the accuracy of the findings when particular articles were excluded.
RESULTS
There are 4,600 native plants in Kerala, and 900 of them have potent therapeutic properties. More than 180 medicinal plants can be found in Kozhikode and Wayanad, the two districts that were chosen. Among these, 95 plant species from 46 plant families have demonstrated the possibility of possessing anticancer characteristics. The information was gathered and organized, and screening and meta-analysis were performed for phytoconstituents and pharmacological applications, including cancer (Table 1). The common names, vernacular names, folklore applications, and traditional usage were obtained from survey reports, observational, and interventional study data. The tribal communities have used medicinal plants for centuries. They prepare decoctions and pastes from plant parts, such as leaves, flowers, stems, barks, and roots. These preparations are ingested or applied externally for a variety of health conditions. The native tribal people have a deep knowledge of the medicinal properties of plants. They are able to identify the plants that are effective for specific conditions, and they know how to prepare the plants for optimal effectiveness.
The alternative medical science of “Ayurveda” is extensively practiced and embraced by the common people in the selected districts of Wayanad and Kozhikode. Despite this, it is essential to note that many of the selected medicinal plants remain unexplored or only partially explored in terms of their potential benefits and uses. Ayurvedic practitioners in the region have a long history, with records dating back 3,000 years detailing the usage of various medicinal plants found in the region. Some Ayurvedic preparations for the aforementioned medicinal herbs can be procured from Ayurvedic pharmacies. In Ayurveda, medicinal plants and their parts are formulated in different forms depending on the disease condition, need, and efficacy. These formulations include Choornams, Vati, Kashayams, Arishtams, Avaleha, and Tailams [27].
A comprehensive analysis was conducted utilizing 326 articles. In the context of a common effect model, the pooled RR exhibited a value of 0.6503 [95% confidence interval (CI): (0.6007; 0.7040)], indicating a statistically significant association (p < 0.0001). The random effects model demonstrated an RR of 0.4894 [95% CI: (0.3301; 0.7257)], which was also statistically significant (p = 0.0019). Heterogeneity within the study was substantial, with an I² value of 92.1% [95% CI: (88.3%; 94.7%)] and a corresponding Tau2 value of 0.3132 [95% CI: (0.1197; 1.1115)]. The Cochran’s Q test supported the presence of significant heterogeneity [Q: 152.23, degrees of freedom (d.f.): 12, p < 0.0001]. Subgroup analysis under the common effect model revealed varying RR values across different subgroups: subgroup 1 [RR = 0.2644, 95% CI: (0.1449; 0.4825)], subgroup 2 [RR = 0.4118, 95% CI: (0.3602; 0.4708)], subgroup 3 [RR = 0.9836, 95% CI: (0.8704; 1.1115)], and subgroup 4 [RR = 0.6563, 95% CI: (0.5519; 0.7805)]. Significant differences were identified among the subgroups (Q between groups: 97.32, d.f.: 3, p < 0.0001; Q within groups: 54.91, d.f.: 9, p < 0.0001). Similarly, the random effects model’s subgroup analysis demonstrated varying RR values: subgroup 1 [RR = 0.2644, 95% CI: (0.1073; 0.6514)], subgroup 2 [RR = 0.4355, 95% CI: (0.0970; 1.9554)], subgroup 3 [RR = 0.4784, 95% CI: (0.0000; 1482.5025)], and subgroup 4 [RR = 0.6376, 95% CI: (0.3002; 1.3543)]. While the differences in subgroups were not statistically significant in the random effects model (Q: 6.87, d.f.:3, p = 0.0763), a potential publication bias was assessed through Egger’s regression, yielding a nonsignificant result (t = −1.40, d.f.: 11, p = 0.1887). Subgroup meta-regression was carried out, indicating that the intercept was −1.2958 [standard error (SE): 0.4565, t-value: −2.8384, d.f.: 9, p = 0.0195], subgroup2 had a coefficient of 0.4650 (SE:0.5725, t-value: 0.8123, d.f.: 9, p = 0.4376), subgroup3 had a coefficient of 0.6880 (SE: 0.6462, t-value: 1.0646, d.f.: 9, p = 0.3148), and subgroup 4 had a coefficient of 0.8558 (SE: 0.5428, t-value: 1.5766, d.f.: 9, p = 0.1493). This comprehensive analysis underscores the complex interplay of factors influencing the observed outcomes. The relevant data are listed in Supplementary Tables S1–S6. The graphical visualization of the stated data as box plots, drapery plots, funnel plots, and forest plots is depicted in Figure 3a and b. A need for increased sensitivity is indicated by subgroup 1, which displays apparent imbalances between true positives (ranging from 3 to 6) and false negatives (ranging from 119 to 300). From ranges 11 to 29, false positives exist with a control strategy. True negatives exhibit rather an evenly distributed dispersion. The greater true positives (ranging from 33 to 180) and significant false negatives (ranging from 1,361 to 13,536) in subgroup 2 suggest that recall has to be improved. In addition, noteworthy are false positives, which range from 47 to 372. Subgroup 3 has a balanced performance with low false positives and high true negatives, along with respectable true positives and false negatives (between 1,699 and 50,448) in subgroup 4, which highlights the need to improve sensitivity. True negatives and false positives can be controlled. Improved sensitivity in subgroups 1, 2, and 4 while preserving precision is necessary to maximize model performance. Already, subgroup 3 performs in a more even manner (Fig. 3c). The numerical values are categorized and illustrated in Supplementary Table S7. The average “n” value and average “r” value for subgroup 1 are 38 and 0.44, respectively. Similarly, the “n” and “r” values of subgroup 2 are 144 and 0.18. The average “n” for subgroup 3 is 115, while the average “r” is 0.70. The data distribution is depicted as a Pareto chart (Fig. 3c), and the details are listed in Supplementary Table S8.
Table 1. Significant anticancer medicinal plants from Wayanad and Kozhikode districts of Kerala. [Click here to view] |
DISCUSSION
This review dives into 4,600 native plants in Kerala’s botanical treasure trove, 900 of which have therapeutic promise. The districts of Wayanad and Kozhikode stand out since they are home to more than 180 medicinal plants, 95 of which may have anticancer qualities. India is a significant exporter of almost 960 medicinal herbs to different regions of the globe, with 178 plant species being traded for more than 100 metric tons. The global market has a significant demand for medicinal herbs such as barberry, senna, isabgol, chandan, long pepper, brahmi, kalmegh, satavari, madhunashini, ashwagandha, sankhpushpi, kokum, and guggal, which are highly sought after. The Malabar Hill region, in particular, plays a crucial role, contributing more than 60% to the trade of medicinal plants [19]. Inspired by folklore origins, several drugs have been adapted for pilot studies to address chronic conditions such as psoriasis, leprosy, HIV infections, cancer, arthritis, tuberculosis, and asthma [23]. Some of these drugs are currently in clinical use, while others are undergoing various stages of clinical trials. In a collaborative effort with India, the WHO established the “Global Centre for Traditional Medicine” in 2022, with a significant contribution of 250 million USD from the Indian government ([email protected]). This initiative aims to promote traditional medicine and explore its potential benefits further. In this study, 552 articles were carefully examined, and the results revealed a statistically significant relationship between specific plants and their anticancer potential. However, the studies’ notable variation highlights the complexity of this topic. Finally, Kerala’s plant diversity offers hope for both conventional and cutting-edge medicine.
Figure 3. (a). Box plot (effect size), Drapery (risk ratio), Funnel plot (standard error and risk ratio), (b). Forest plots for heterogeneity (A) fixed model, (B) random model, (c). Publication bias depicted as bar plot and publication correlation depicted as pareto chart. [Click here to view] |
CONCLUSION
The study’s findings emphasized Kerala’s enormous potential for using its rich botanical resources, with an emphasis on the Kozhikode and Wayanad districts. There are 4,600 native species in the area, and 900 of them have plausible therapeutic benefits. The ongoing practice of Ayurveda, a science rooted in history, adds to this treasure. The examination of 552 articles along with the survey emphasized the complexity of this topic and the necessity for careful analysis. Hence, in the existing conditions, the exploration of more medicinal plants with extraordinary phytoconstituents for cancer therapy is crucial. In addition, it is imperative to focus on reinstating medicinal herbs that are on the verge of extinction, utilizing modern, sophisticated methods and tissue culture techniques. The selected region, along with the entire Malabar Mountains, houses an abundance of medicinal plants that remain unexplored or only partially studied. Therefore, scrutinizing these untapped medicinal plants is essential to addressing rare, chronic diseases, including cancer.
ACKNOWLEDGMENT
The authors want to thank the tribal settlement villages of the Wayanad and Kozhikode regions of Kerala for their contribution to the collection of data.
AUTHOR CONTRIBUTION
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the international committee of medical journal editors (ICMJE) requirements/guidelines.
FUNDING
There is no funding to report.
CONFLICTS OF INTEREST
The authors declare that there is no conflict of interest in this research.
ETHICAL APPROVAL
This study does not involve experiments on animals or human subjects.
DATA AVAILABILITY
All the data is available with the authors and shall be provided upon request.
PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS
During the preparation of this work, the author(s) used Chat-GPT for writing assistance. After using this tool or service, the author(s) reviewed and edited the content as needed and took full responsibility for the content of the publication.
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SUPPLEMENTARY MATERIALS
Supplementary Table S1. RR for common effect and random effect models. [Click here to view] |
Supplementary Table S2. Test of heterogeneity. [Click here to view] |
Supplementary Table S3. Subgroup analysis (Common effects model). [Click here to view] |
Supplementary Table S4. Subgroup analysis (Random effects model). [Click here to view] |
Supplementary Table S5. Publication bias analysis. [Click here to view] |
Supplementary Table S6. Meta regression analysis of subgroups. [Click here to view] |
Supplementary Table S7. Publication bias analysis. [Click here to view] |
Supplementary Table S8. Publication correlation analysis. [Click here to view] |